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                                  TexaSoft's
                                USING KWIKSTAT

                      Reference Guide, Condensed Version
                       (C)Copyright 1991 Alan C. Elliott

    All rights reserved. No part of this manual may be reproduced without
    prior permission. For information, address TexaSoft, P.O. Box 1169,
    Cedar Hill, Texas 75104. CIS:70721,3145

    No patent liability is assumed with respect to the use of the
    information  contained herein. While every precaution has been taken
    in the preparation of  this publication, the publisher assumes no
    responsibility for errors or omissions. Neither is any liability
    assumed for damages resulting from the use of the information herein.

    This shareware copy of the program is made available so you can "try it
    before you but it." When you register, you will receive the latest
    version of the program, license to use the program on a regular basis,
    a 288 page printed manual, 3 months of support, a newsletter and more.
    -----------------------------------------------
    For more information, print these files
    Registration form - KSORDER.TXT
    Site license - SITELICE.DOC
    Updated information - LATENEWS.DOC Detailed
    Installation instructions - KSINSTAL.DOC
    -------------------------------------------------

    CONDENSED TABLE OF CONTENTS
    ---------------------------
    Part I: An OverView of KWIKSTAT
    Part II: Using the KWIKSTAT Database
    Part III: A Review of Statistical Concepts
    Part IV: Performing A Statistical Analysis
          o Descriptive statistics
          o t-tests and analysis of variance
          o Non-parametric comparative procedures
          o Regression analysis
          o Crosstabulations and Chi-Square
          o Life tables and survival analysis
    Part V:  Using KWIKSTAT Utilities
          o Export data from a database to an ASCII file
          o Produce a printed report
          o Import 1-2-3 type files
          o Create and edit images for pictograph procedure
    Appendices: Error Codes/ Problem Form/ Ballot


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    PART I - AN OVERVIEW OF KWIKSTAT
    -------------------------------------------------

    KWIKSTAT is for people who need to summarize, analyze or interpret
    numerical information. It will help you decide what kind of analysis
    is appropriate, read the data you already have on your computer (from
    a variety of file types) or from the keyboard, perform the analysis
    and offer interpretation of the results. Unlike older programs such
    as SPSS or SAS, you do not have to be a professional statistician or
    programmer to beneficially use KWIKSTAT.

    REQUIREMENTS:100% compatible computer, including the IBM PS/2
    computers, 384K RAM, CGA, EGA, VGA or Hercules compatible monitor.
    Many printers are supported.

    INSTALLATION

    Detailed installation procedures for KWIKSTAT are in the file
    KINSTALL.DOC. For quick installation on a hard disk, place the
    KWIKSTAT disk (1) in the A: drive and enter:

    A:INSTALL

    Follow the instructions on the screen.

    USING THE KWIKSTAT MENU

    The main KWIKSTAT menu uses a pull-down menu interface. When you begin
    the KWIKSTAT program with the KS command from the DOS prompt (after it
    has been installed), and after the Copyright screen, you will see the
    main KWIKSTAT "DATA" menu. (If the ANALYZE menu appears instead of the
    DATA menu, press the left arrow key once, and the DATA menu will
    appear.) The top line of the menu is a menu bar. This bar contains the
    options "Data", "Analyze" and "Helps". These are the three main
    options for KWIKSTAT. Using the right and left arrow keys, you can
    move bewteen the options.

    To select options from an extended menu (pulled-down), use the up and
    down arrow keys on the cursor pad to highlight the option you desire,
    then press the Enter key. Or, to select on option from a pull-down
    menu, press the first letter of the option name.

    To exit KWIKSTAT choose the Quit - Exit from the DATA menu or press
    the Esc key.






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    USING THE ANALYZE MENU

    The KWIKSTAT Analyze menu allows you to choose which analysis module
    to run. See the section titled "TUTORIAL: Try this Example".

    USING THE KWIKSTAT HELP SYSTEM

    The "HELPS" pull-down menu contains the following choices:

    o HELP ON USING THE PROGRAM OPTION - general help
    o DECIDE WHAT ANALYSIS TO USE OPTION - what analysis to use
    o ABOUT KWIKSTAT - copyright and order information
    o GO TO DOS, RETURN WITH EXIT (SHELL) - temporarily go to DOS prompt
    o CHANGE SETUP - select default directory, printer, monitor
    o SET MONITOR COLOR - select monitor colors

    --------------------------------------------------------------------
    DO THIS TUTORIAL EVEN IF YOU DON'T READ ANY MORE OF THE MANUAL.
    --------------------------------------------------------------------

    This tutorial will give you a feeling for how to use KWIKSTAT. It
    assumes you are using KWIKSTAT on a hard disk. To begin KWIKSTAT, you
    must first be in the \KWIKSTAT directory on your hard disk. Use the CD
    (Change Directory) command from the DOS prompt to change to the
    \KWIKSTAT directory by using the command:

    CD\KWIKSTAT

    From the \KWIKSTAT directory, begin KWIKSTAT with the command:

    KS

    After the copyright information, the Data pull-down menu will appear.
    (If the Analyze menu appears, press the left arrow key once to open
    the Data menu.)

    ACCESSING THE KWIKSTAT HELP SCREEN

    To examine the KWIKSTAT HELP menu, press the F1 function key. (This
    help screen is available from any menu.) The HELP menu lists major
    topics, and the screen number. You can think about the HELP procedure
    as a book, with screens instead of pages. To look at a particular
    topic, enter the screen number you desire. For example, to look at
    screen 7, type 7 and press Enter.

    KWIKSTAT displays screen 7. Once you have displayed screen 7, to move
    to screen number 8, press Enter. To go back to the menu, type the "M"
    key. To exit the HELP module, press the Enter key from the main Help
    menu or the Esc key from a help screen. Press Enter now. This takes you
    back to the KWIKSTAT Data pull-down menu. Every module has the help
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    screens available.

    The KWIKSTAT "Decision" help screen is available from the Helps
    pull-down menu. To look at this help menu, use the right arrow key to
    move to the Helps pull-down menu. Then use the down arrow key to
    highlight "Decide what analysis to use" and press Enter.

    EXAMPLE OF DESCRIPTIVE STATISTICS

    Entering data from the keyboard is explained later in Part II, "Using
    the KWIKSTAT database". This example will use the database named
    EXAMPLE on disk. To open this database, use your arrow keys to move to
    the Data pull-down menu. Select "Open a Database". A "Pick" menu will
    appear of available database names. Use the up and down arrow keys to
    highlight EXAMPLE and press Enter. (If the EXAMPLE database does not
    appear on the list of databases, you may not have installed the
    program correctly. Review the installation instructions.)

    Once the database is opened, a notice at the bottom of the screen tells
    you that the database is open. Press the "L" key to choose the List the
    Contents option. This will list the contents of EXAMPLE database to the
    screen. Press Enter several times to list the entire database to the
    screen. When the list is finished, you will return to the Data
    pull-down menu. Use the right arrow key to move to the Analyze
    pull-down menu. Choose the Descriptive Statistics and Graphs option
    from the Analyze menu by highlighting it and press Enter. KWIKSTAT now
    switches to the Descriptive module (which may take a few seconds).

    From the Descriptive Statistics menu, press the letter B (or highlight
    the "B" option and press Enter) to choose "Detailed statistics on a
    single variable." The program now displays the variables available for
    analysis from the database.

    Choose variable number 2 (AGE) by typing a 2, then press Enter. Before
    the statistics for this variable are displayed, two options are
    presented. First, you are prompted you with the question:

    Specify Confidence Interval level (.5 to .99) (Default is .95)

    For this example, PRESS ENTER TO ACCEPT THE DEFAULT.

    Default for percentiles is Tukey 5 Number Summary
    Specify your own percentiles to calculate (y/N)?

    When a Yes/No question appears on the screen, notice that the Y or the
    N will be uppercase (in this case it is (y/N). This means that if you
    press Enter without entering a Y or an N, the uppercase option is the
    default (No). For this example, to choose No to the question, JUST
    PRESS ENTER.

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    The program will display a screen of descriptive statistics, and a box
    plot of the data. Notice that this screen is different from previous
    screens. The information on this screen is displayed in graphics mode
    (if you have a graphics monitor). Normally, information on the screen
    is in "text" mode. When graphs are displayed on the screen, the
    program must use a graphics screen mode. This graph appears in black
    and white, although some graphs will appear in color.

    On graphic screens, a menu will appear at the bottom of the screen for
    a few seconds, then disappear. This allows you to capture or print the
    screen without the menu appearing on your printout. To bring the menu
    back, press the spacebar once. The menu options are still available
    even when the menu is not visible. The menus differ according to your
    setup and particular options available for the graphic display, but
    most graphic menus will include the following options:

    Esc:Exit R:Replot P:Print

    Press Esc to end the display, press R to replot (choose other display
    options) and P to print the graphic screen to the printer. For example,
    if you want a printed copy of this graphics screen, MAKE SURE YOUR
    PRINTER IS TURNED ON, and is ON LINE, and HAS PAPER. Then, press "P"
    (for Print).

    IF THE SCREEN DOES NOT PRINT PROPERLY: You may not have your printer
    graphics command properly implemented - review the installation
    procedures and technical considerations in the appendix and the file
    PRINTERS.DOC.

    To return to the main Descriptives menu, press Esc. To end this module
    and return to the main KWIKSTAT menu, press Esc. To end KWIKSTAT from
    the main menu, press Esc again and answer Y to the prompt "End
    KWIKSTAT."

    Procedures are explained more fully later in the manual. However, you
    may find that you will be able to use most of the KWIKSTAT features
    without any further aid from the manual. Remember, you have three
    sources of information if you need help. (1) the regular help menu
    (F1), (2) the help procedure that will assist you in choosing the right
    statistical analysis to use (main menu, Helps) and (3) the manual.

    IF SOMETHING GOES WRONG

    If an error code appears and cannot resolve the problem, please fill
    out the Problem Report Form and send it in right away, so that errors
    in the program can be eliminated. (For fastest response, fax it to
    214-291-3400 or send a Compuserve E-Mail message to 70721,3145.) If
    you have a suggestion for how to improve KWIKSTAT, fill out the USER'S
    BALLOT. Thanks.

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    PART II - USING THE KWIKSTAT DATABASE
    ---------------------------------------------------------

    The DATA pull-down menu is used to manage your data. From this menu
    you enter data, change data, create new data fields from existing
    ones, and perform other data maintenance tasks. Once your data is in
    the KWIKSTAT (dBASE-type) database, you can access the data from any
    of the other KWIKSTAT modules.

    HOW DATA IS STORED IN KWIKSTAT

    A KWIKSTAT database uses the same file format as the dBASE III and
    dBASE IV programs. Therefore, data already stored in a dBASE III or
    dBASE IV file may be read directly into all the KWIKSTAT programs. The
    only exception to this is that KWIKSTAT does not read dBASE MEMO
    fields. Therefore, if your data in dBASE contains memo fields, you may
    have to create a subset of your database before using it in KWIKSTAT.
    Data from other programs can also be used in KWIKSTAT. Refer to the
    section called "Entering Data into a Database." The following
    information describes how to use the DATA pull-down menu.

    OPENING AN EXISTING DATABASE

    The OPEN A DATABASE TO USE option on the DATA menu allows you to
    access information in a dBASE file. Use this option to choose the
    database that you will be analyzing.

    When you choose the OPEN option on the DATA menu, a pick list of
    databases currently in the default directory will be displayed. To
    select a database, use the up and down arrow keys to highlight a
    database name, then press Enter. If the database you want to use is not
    in the current (default) directory, you can temporarily change the
    default directory by pressing the F2 function key.

    DESIGNING AND CREATING A DATABASE

    The CREATE A NEW DATABASE OPTION on the DATA menu is used to create a
    new database. The structure, or layout, of a database must be
    described before you enter your data.










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    NAME YOUR DATABASE

    The database name must be standard DOS file name. DO NOT include an
    extension to the name. Once you have named the database, you will
    define the fields -- names of the places where the data will be
    stored. Each variable (or field) description requires the following
    information

    o A field name
    o A field type (character or numeric)
    o A width
    o Number of decimals (if field is numeric)

    DEFINE THE FIELDS IN YOUR DATABASE

    When you first enter the definition mode, the cursor will be in the
    FIELD NAME area. Enter a name (such as AGE), and press Enter.

    In the TYPE area, you only need to enter the first character of the
    type (N, or C), then press Enter. If your choice is NUMERIC, press
    ENTER when your cursor moves to this area (the default).

    WIDTH is the number of characters reserved for the entry. Decimal is the
    number of decimal places (only relevant for numbers). Note that the number
    of decimal places must be at least one less than the width. For example,
    if a number has the format ###.##, the width is 6 (count the decimal
    point), and the number of decimal places is 2.

    Once a complete field description is entered, a next blank field
    description will appear, ready for entry. To end the creation process,
    type Control-END (^END). The End key is on the numeric pad. As long as
    you have not ended the procedure, you may use the cursor keys to back
    up, and make any corrections. If you mess up, end the procedure with
    Esc and begin again.

    If you want to enter data now, answer "Y" to the question Enter
    Records Now (y/N)  Otherwise answer "N". You can always enter the
    data later, or add to data already in a database.

    SPECIFICATION FOR DATABASE FIELDS

    1. The FIELDNAME: 1 to 10 characters, MUST begin with a character (A
    to Z).

    2. The TYPE may be: CHARACTER - May contain any character. NUMERIC -
    Must contain numbers only. Examples:1.00, -4.32, 6, 10000.

    3. The WIDTH of the field: Choose a width so that the maximum number of
    characters will fit into the field.

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    4. DECIMALS:Decimals are only valid for numeric fields. This specifies
    to KWIKSTAT how many decimals to retain in the field.

    LIMITATIONS

    Maximum 250 fields.
    Maximum width of a cell is 60 characters (15 for numbers).
    Memo fields are not supported.
    Date and Logical fields are recognized, but they cannot be used
    in transformations or subsetting.

    ENTERING DATA INTO THE DATABASE

    When you choose the Data entry option, you will be asked to specify
    entry from the keyboard or from a file (ASCII file). For most small
    data sets, you will probably enter data from the keyboard. If another
    program supports ASCII or dBASE files, you will be able to enter data
    from that program in to KWIKSTAT. Also, KWIKSTAT contains a translation
    facility to import data from 1-2-3 type files and comma delimited
    files. (See Part V, Using KWIKSTAT Utilities.) The following
    information describes how to enter data from the keyboard, from an
    ASCII file.

    ENTERING DATA FROM THE KEYBOARD

    If you choose KEYBOARD data entry, an entry screen will appear
    containing the fields you created in the CREATE option. Entering data
    from the keyboard is similar to the way you enter field descriptions
    when creating a new database. The entry screen displays the name of
    each field followed by a highlighted entry area where you will type in
    the contents of the field.

    Note: The word FIELD refers to the variable that contains information,
    such as GROUP or AGE. The word RECORD refers to the entire collection
    of FIELDS for one entry -- for example, the GROUP, AGE, TIME1, etc.
    are for one person.

    While you are entering information into a record, you can use the up
    and down arrow to move among fields to make corrections. Once you
    enter information in the last field of a record, KWIKSTAT assumes you
    have finished entering data for that record, and goes to the next
    record. If a record contains too many fields to fit on one screen,
    KWIKSTAT will display the first 21 fields on the screen. When you have
    entered information into those fields, the next 21 fields will appear
    on the screen. This will continue until information has been entered
    into all fields for the record.

    If you need to go back to a previously entered record to edit,
    pressing the PgUp key will automatically place you into edit mode.

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    IMPORTANT: Once you have finished entering information, you can use
    either Esc or ^END (Ctrl-End) to end the entry process. (Just as in
    the dBASE program.) Be careful, since THESE TWO COMMANDS MEAN
    DIFFERENT THINGS. When you press Esc to end, it means, "DO NOT SAVE
    the current record." When you use ^END to end it means "SAVE the
    current record." Therefore, if you are entering data, and come to the
    last record, and KWIKSTAT is displaying a blank record beyond the
    actual data, use the ESC to end. If you are on your last record, and
    it contains information you want to keep, use the ^END to end entry.
    If you accidentally end up with a blank record in your database, use
    the Delete and Pack procedure to get rid of it. (See Deleting and
    Packing.)

    ENTERING DATA FROM AN ASCII FILE

    KWIKSTAT can read data from ASCII text files. (See also LATENEWS.DOC
    for information on entering data from a comma delimited file.) These
    kinds of files are usually supported by most word processing programs
    (such as WordPerfect DOS Text Mode). Data must be in the form of
    column data, like this...

    A 22 3.3 WF
    A 33 4.2 BF
    B 27 3.3 WM
    :
    Etc.

    Notice that each column of data is in fixed fields. It does not matter
    that there is no space between the last two fields (Race and Sex)
    since the program will pick off the information from the column and
    does not require that there be spaces between the columns. Use the
    instructions below to prepare the KWIKSTAT (dBASE) database structure
    to be used to read in ASCII data.

    The steps to enter ASCII data into KWIKSTAT are:

    STEP 1. Use the CREATE option to create a database structure to match
    the columns in the ASCII file. The field widths MUST match the width
    of the columns of data on file. If there are spaces between columns of
    data, make widths wide enough to account for those spaces. The
    following data is from the file EX.DAT on disk:









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    A  12 22.3 25.3 28.2 30.6 5
    A  11 22.8 27.5 33.3 35.8 5
    B  12 22.8 30.0 32.8 31.0 4
    A  12 18.5 26.0 29.0 27.9 5
    :
    etc
    :
    B  12 22.4 27.2 31.8 35.6 4

    Try your hand at doing this example by creating a database named EX
    with the following structure:

    FIELD NAME     TYPE         WIDTH         DECIMALS
    GROUP          C            2
    AGE            N            4             0
    TIME1          N            5             1
    TIME2          N            5             1
    TIME3          N            5             1
    TIME4          N            5             1
    STATUS         N            2

    Notice that even though the first column has data 1 column wide, this
    structure uses a width of 2 for GROUP. Even though the age only uses 2
    columns, the structure calls for AGE to have a width of 4. These
    widths are enter this way to take care of the blank spaces between the
    columns. Create the database called EX with the specifications listed
    above, then go to the next step.

    STEP 2: Once you have defined the database to match the ASCII input
    file, choose the Data entry option from the DATA menu, and choose to
    read data from a file. You will be prompted to enter the name of the
    file containing the ASCII data, then the data will be read into the
    database file.

    STEP 3: To verify that the data was read properly, use List option to
    examine the database.

    USING DBASE TYPE FILES

    If the program you are using supports dBASE files, all you have to do
    to copy the file to the KWIKSTAT data directory.









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    EDITING RECORDS

    When you choose the Edit a record option, you will be asked to specify
    the record number to edit. Editing is similar to entering data. Use
    the up and down arrow keys to move from field to field within a
    record. Use the PgUp and PgDn keys to move forward or backwards in the
    database one record at a time. When you are finished editing record,
    use the ^END command to exit from the edit mode.

    DELETING RECORDS

    If you want to delete an entire record within a database, use the edit
    procedure to display the record to delete. While a record is displayed,
    pressing ^U marks the record for deletion. A **DEL** will appear on the
    screen (upper right corner) of a "deleted" record. You can use PgUp and
    PgDn to move within the database and mark as many records as you choose.
    If you accidentally mark a record for delete, pressing ^U a second time
    will cancel the mark, and the **DEL** will disappear from the screen.

    PACKING THE DATABASE

    The records marked for delete are not actually deleted at this point.
    However, they will be ignored in most analyses. Once you have marked
    one or more record for delete, you may want to permanently get rid of
    them. To erase all records marked for delete, choose the Pack
    procedure from the FILES menu. This procedure erases all "deleted"
    records from the database.

    MODIFYING AND DISPLAYING THE STRUCTURE

    The Modify or Display database structure option on the DATA menu
    allows you to display the structure of your database, and allows you
    to change characteristics about the database structure. When you
    choose to display the structure, a list of all field names, their
    types, widths and decimals (if any) are listed.

    SETTING MISSING VALUES CODES

    Sometimes in the collection of data there are values that are lost or
    cannot be gathered. These are called "missing values". When such
    values occur, it is important for the program to know that the values
    are missing so that statistical calculations may take this into
    account. Missing values are usually designated as an impossible value.
    For example, the missing values designated for the variable AGE may be
    -9, since it is impossible for the variable AGE to have the value -9.
    When the program is asked to calculate the mean of age, for example,
    it will ignore those records where AGE is -9 in that calculation if -9
    has been specified as the missing value code. In most KWIKSTAT
    procedures, there is a casewise deletion of the record from
    calculation whenever a missing value is encountered. Once you
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    designate a missing value code for a variable, it is up to you to make
    sure that this code gets placed into your database in the proper
    records and fields. For example, if you have designated -9 as the
    missing value code for AGE, you must make sure that in your database a
    -9 appears in the field AGE if that data is missing or unknown.

    A standard dBASE file does not have a way to designate missing values,
    but KWIKSTAT allows a way for you to designate these values in this
    program. The Indicate missing value codes option on the DATA menu is
    used to set up these values. When this option is selected, the program
    will display an entry screen that is similar to a data entry screen.
    You may enter one missing value for each field name. The missing value
    must obey the definition of the field in terms of length and type.

    Once missing values are entered, they are stored on disk in a file
    named filename.MV, where "filename" is the name of the designated
    database. If a new variable is created using the transformation
    procedure, its missing value is appended to the missing value file.

    You may change or correct the missing values for a database at any
    time by calling up this option. If missing values are already
    designated for the database, they will be displayed on the entry
    screen, and you may edit them or accept them as they are.

    IMPORTANT NOTE: If missing values are NOT used, and there is a blank
    numeric variable in a calculation, it will be treated like the value 0
    (zero), so it is important to use missing values if your data contains
    such entries. Otherwise, the statistical calculations will be in
    error!!

    MAKING A VARIABLE BY TRANSFORMATION

    You may create a new numeric variable in a database by choosing the
    Transformation option. For example, if you wanted a new variable to be
    the ratio of WEIGHT to HEIGHT, you could name a new variable RATIO,
    and use the transformation WEIGHT/HEIGHT as the expression to create
    the new variable.

    When you request the TRANSFORMATION procedure, you will
       o Define a name for the new field
       o Define a width for the new variable.
       o Define the number of decimals, if any.
       o Define a missing value code. If none is selected, it is
         assumed to be 0 (zero).

    CAREFUL ATTENTION must be paid to the definition to assure that the
    calculated numbers will fit into the field width specifications. If
    the calculated number is too large to fit into the field, it will be
    given the missing value code. If an illegal calculation is attempted,
    such as a division by 0, the result will be missing. If a calculation
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    includes a missing value, the result will be a missing values.

    TRANSFORMATIONS SUPPORTED

    KWIKSTAT supports standard mathematical operation and functions, as
    described below:

    Mathematical operators: Add (+), Subtract (-), Multiply (*), divide
    (/) and exponenation(^).

    Following are a few examples of correct expressions:

     NEW = AGE/HEIGHT
     NEW = SUM(AGE,WEIGHT,HEIGHT,SCORE)
     NEW = PI * (SCORE ^ 2)

    Notice that SUM is a function. KWIKSTAT supports over 20 functions,
    including ABS, ACOS, ASIN, ATAN, ATAN2, CSC, COS, COT, EXP, INT, LN,
    LOG, MAX, MIN, MOD, PI, RAND, RECNO, RECODE, ROUND, SEC, SIN, SQRT,
    SUM and TAN.

    The RECODE function is defined as follows:

    NEW = RECODE(SCORE,1,0,10,15) means NEW = 1 if SCORE is between 10 and
    15, else NEW=0)

    SUBSETTING THE DATABASE

    The Subset database option on the DATA menu allows you to create a new
    database from an old database. The new database can be a subset of the
    old one, using a conditional criteria for outputting information from
    the old database to the new one.

    For example, suppose you have a database with a field GROUP with
    values 1, 2, 3, 4 and 5. You want to create a database that does NOT
    include Group 5. After choosing Subset database from the DATA menu,
    you are asked for the name of the new database. For example, your new

    database might be named NO5.DBF. You are asked for the field name to
    be used in the selection criteria. In this case, you would choose the
    field named GROUP. Next you must enter the selection relationship. It

    will be described as a numerical expression. The conditional
    operators you may use are: = > < >= <= <> = and the logical operator
    ".NOT.".

    The program will prompt you with

    SELECT IF GROUP

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    and you must finish the selection criteria. For example:

    SELECT IF GROUP .NOT. = 5

    (Select records for which the variable GROUP is not equal to 5.) You
    may use all of the variables in the database in the of the expression,
    and you may use the functions described in the Transformation option.

    For example, other selections might be

    SELECT IF GROUP = 4
    SELECT IF GROUP > STATUS
    SELECT IF GROUP < WEIGHT*HEIGHT
    SELECT IF TIME1 = TIME2*1.96

    LISTING THE DATABASE TO THE SCREEN

    The LIST option on the DATA menu allows you to look at the information
    in your database.

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    DO THIS TUTORIAL TO LEARN ABOUT CREATING A DATABASE
    -------------------------------------------------------------------

    TUTORIAL:YOUR TURN - GIVE IT A TRY

    Suppose you are given data from an experiment. The data are from a
    sample of 15 hogs (randomized to four groups) that have been given
    one of four feeds. The measured response for this experiment is weight
    gain. The data are summarized below:

        FEED1       FEED2       FEED3       FEED4
        60.8        78.7        92.6        86.9
        67.0        77.7        84.1        82.2
        54.6        76.3        90.5        83.7
        61.7        79.8        90.3

    This will be analyzed as a One-Way Analysis of Variance. For that
    procedure, you must have a grouping variable (FEED) and a response
    variable (WEIGHT GAIN). Therefore, the database to be created for this
    data will have two variables. You can call them FEED and WEIGHT.
    Before entering the database, you must first create a new database:

    STEP 1. Begin the KWIKSTAT program from the DOS prompt with the KS
    command. From the DATA menu, choose the option Create a new database.

    STEP 2. The database creation screen will appear. On this screen,
    define the two fields. Field one will be named FEED, it will be of
    NUMERIC type with a width of 2, with no decimals. Field two will be
    named WEIGHT, it will be of NUMERIC type with a with of 5 and 1
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    decimal place. (NOTE:Widths could actually be 1 and 4 instead of 2 and
    5, but the one extra space gives you some space for unexpected large
    values and makes data entry easier.) Enter the two field
    specifications on the creation screen:

    FIELD 1: FEED, NUMERIC, WIDTH 2, NO DECIMALS
    FIELD 2: WEIGHT, NUMERIC, WIDTH 5, 1 DECIMAL PLACE

    End the database creation process by entering a ^END (Hold the CTRL
    key down with one finger, and press the End key with another finger.)

    STEP 3. You will be asked if you want to enter the data now. Answer Y for
    yes. The data entry screen will appear. On the first screen (the first
    record, you will enter the following values:

    FEED       N: 1    <--- You enter the 1
    WEIGHT     N:60.8  <--- You enter the 60.8

    The next record will be FEED=1, WEIGHT=67.0, and so on. The following
    table lists the values you will enter into the database:

       FEED          WEIGHT
        1            60.8
        1            67.0
        1            54.6
        1            61.7
        2            78.7
        2            77.7
        2            76.3
        2            79.8
        3            92.6
        3            84.1
        3            90.5
        4            86.9
        4            82.2
        4            83.7
        4            90.3

    It is important to understand how KWIKSTAT reads this data from the
    database. The FEED variable places the WEIGHT values in one of four
    groups. Thus, KWIKSTAT "knows" that the number 77.7 belongs to group
    (FEED) 2, and so on.

    STEP 4. Once you have entered the data for the 15 hogs into the
    database, the screen should be displaying the data entry screen for
    record 16, which does NOT contain any data. To end the entry procedure
    and NOT save the empty record 16, press the Esc key. Your data is now
    saved in the database.

    STEP 5. Always verify that your data is correctly entered by choosing
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    the List option from the DATA menu. This lists the values to the
    screen. If everything looks okay, you are ready to analyze your
    information. The various procedures in KWIKSTAT expect the data to be
    stored in a particular way to perform a statistical test. Refer to the
    sections on each procedure for examples of how to design your database
    to match the expectations of that procedure.

    PART III
    -----------------------------------------------------------

    The Data Generations and Simulations module contains several
    simulations that can be used to demonstrate statistical concepts.

    PART IV - PERFORMING A STATISTICAL ANALYSIS
    ------------------------------------------------------------

    This section of the KWIKSTAT manual describes the statistical analysis
    procedures available in the basic KWIKSTAT program.

    USING DESCRIPTIVE STATISTICS AND GRAPHS

    The Descriptive Statistics and Graphs module allows you to examine
    summary statistics of the data in a database. Procedures and graphics
    in this module include:

    DETAILED STATISTICS ON A SINGLE VARIABLE

    This option calculates the mean, standard deviation, median, standard
    error of the mean, minimum, maximum, sum, and variance of a set of
    data. KWIKSTAT also calculates five percentiles and computes a
    two-sided confidence interval about the mean. You have the opportunity
    to specify the five percentiles to be calculated, as well as the level
    of confidence of the confidence interval.

    EXAMPLE 4.1: DESCRIPTIVE STATISTICS ON A SINGLE VARIABLE

    Suppose you have the following data on seven persons, and you want to
    know the average age of persons in the group being weighed.

    Data for Age/Weight Example

    Person   Age   Weight
    1        23   140.0
    2        21   133.5
    3        34   200.0
    4        33   150.0
    5        40   296.5
    6        28   167.0
    7        25   175.5

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    CREATING THE DATABASE

    The database will include seven records (one for each of the seven
    persons) and two fields ( for the two variables, age and weight). That
    is, in each record two pieces of information about that person (age
    and weight) will be entered.

    FIELD NAME    TYPE    WIDTH  DECIMALS
    1     AGE     Numeric 3      0
    2     WEIGHT  Numeric 6      1

    You will be asked if you want to enter records now. Answer Yes by
    typing Y and pressing Enter. A data entry screen will appear where you
    will enter the data. The data you will enter in the first record is 23
    (press Enter) and 140.0 (press Enter).

    Enter the data for the seven records. Refer to the example in the
    database tutorial for entering the data. After entering the data,
    examine the AGELBS database by choosing the List (display) the
    contents of a database option from the Data menu. The data in the
    database should look like this:

    RECNO   AGE   WEIGHT
    1       23   140.0
    2       21   133.5
    3       34   200.0
    4       33   150.0
    5       40   296.5
    6       28   167.0
    7       25   175.5

    If your data do not look like this, use the Edit a record option to
    correct errors.

    PERFORMING THE ANALYSIS

    Once you have entered the data into a database, choose the DESCRIPTIVE
    STATISTICS AND GRAPHS module from the Analyze pull-down menu. The
    Descriptive Statistics and Graphs menu will appear. Select DETAILED
    STATISTICS ON A SINGLE VARIABLE.

    You will be prompted to choose the field name of the variable on which
    you wish to calculate summary statistics. In this case Enter 1, which
    chooses AGE.

    You will then be asked to specify the level of confidence for the
    confidence interval. If you want a 95% C.I., simply press Enter for
    the default setting. If you want, say, a 99% interval, type .99. Next,
    you will be asked if you want to specify percentiles other than the
    Tukey 5 number summary (0, 25th, 50th, 75th, 100th). If you answer
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    yes, you will be prompted for the five percentiles you want. KWIKSTAT
    will perform the calculations and display the results. Figure 4.1
    shows the results of the summary statistics procedure on the AGE
    variable using default settings for percentiles and a 95% C.I.

    SUMMARY STATISTICS ON A NUMBER OF VARIABLES

    This option is similar to the above Descriptive statistics on a single
    variable, but in this option several variables can be summarized using
    descriptive statistics (sample size, mean, standard deviation,
    minimum, maximum, and standard error of the mean). If you have a
    grouping variable in your database, you may request output of summary
    statistics by group. You are also given the opportunity to print
    results to the printer, or to output results to a file.

    APPROXIMATE P-VALUE DETERMINATION

    This option calculates p-values for entered values of four test
    statistics: normal (z), student's t, F, chi-square. If you designate
    the statistic being used, degrees of freedom and the calculated value
    of the test statistic, KWIKSTAT will tell you the p-value associated
    with that test statistic.

    PRODUCING A HISTOGRAM

    This procedure produces a histogram from values read from a database.
    A histogram can be helpful in determining if the distribution of a
    continuous variable is approximated by a normal distribution. If the
    histogram has a peak toward the center, with both tails diminishing,
    the data could be considered to be approximated by a normal
    distribution.

    PRODUCING AN XY-PLOT (SCATTERPLOT)

    This option enables you to produce a scatterplot of two variables. A
    scatterplot is simply a plot of all the data values plotted one
    variable against the other. Such a plot is helpful in determining if
    two variables are related, and if the relationship is linear (a
    straight line), curvilinear, or something else.

    TIME SERIES PLOT

    This option enables you to produce a time-series plot for one
    variable. This plot is useful in examining data that is time related,
    such as profit by month, etc. The X axis is assumed to be "time". The
    data values must be entered into records in chronological order the
    observations occurred, i.e., the first record must contain the results
    of the first observation (first time period), etc. Use UNEMP variable
    in the LONGLEY database to see an example of graphing an observation
    over time.
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    USING T-TESTS AND ANOVA PROCEDURES

    T-tests and Analysis of Variance (ANOVA) procedures are used to test
    hypotheses about population means using data obtained through random
    sampling of those populations.

    PARAMETRIC INDEPENDENT GROUP ANALYSIS

    Independent group analysis is appropriate when observations are
    taken from groups in which subjects in one group do not appear in
    another group. In this module, a t-test is performed when there are
    two groups, and an ANOVA is performed when there are three to ten
    groups being compared. When performing a t-test or ANOVA on two or
    more independent groups, you are testing the hypotheses:

    Ho:  The difference in the means of the groups is zero.
    Ha:  The difference in the means of the groups is not zero.

    For a two-sample t-test, two t-statistics are calculated, one for
    the case in which the variances of the two samples are equal and
    the other for use in the case of unequal variances. KWIKSTAT
    performs a test of the hypothesis that the variances are equal,
    that is, a test to determine if the variances are equal, and
    reports a p-value. If this p-value is small (e.g., less than 0.05),
    the hypothesis of equal variances is rejected and you use the
    t-statistic for unequal variances. If the p-value is large, use the
    t-statistic for equal variances.

    EXAMPLE 4.7: TWO SAMPLE T-TEST (INDEPENDENT GROUPS)

    The data used here are heights of 13 plants grown using two
    different fertilizers. Suppose you want to know if there is a
    difference in the average heights of plants in the two treatment
    groups.

    Data for independent group t-test (fertilizer study)

    Present Fertilizer   Newer Fertilizer
    46.2 cm               51.3 cm
    55.6                  52.4
    53.3                  54.6
    44.8                  52.2
    55.4                  64.3
    56.0                  55.0
    48.9

    In order to enter this data into a database, you must assign group
    numbers (or letters) such as Present = 1 and Newer = 2, or you
    could use P and N (if the variable is of the character type).
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    Since the observations are independent, the database will include
    thirteen records (one for each plant) and two fields (one for the
    response and one for the group indicator.

    FIELD    NAME       TYPE      WIDTH  DECIMALS
    1        GROUP      Numeric   5      0
    2        HEIGHT     Numeric   5      1

    You can choose any field names up to ten characters. You may want to
    use FERTILIZER instead of GROUP, for example. You will be asked if you
    want to enter records now. Answer Yes by typing Y and pressing Enter.

    A data entry screen will appear where you will enter the data. The
    data you will enter in the first record is 1 (press Enter) and 46.2
    (press Enter). Enter the data for the thirteen records. For each
    record of a "Present Fertilizer" observation, enter "1" for the GROUP
    variable. For the "Newer" observations enter a "2" for the GROUP
    variable. The second record is a 1 and 55.6. They eighth record is 2
    and 51.3.

    From the List option the data in the database should look like this:

    RECNO  GROUP   HEIGHT
    1       1       46.2
    2       1       55.6
    3       1       53.3
    4       1       44.8
    5       1       55.4
    6       1       56.0
    7       1       48.9
    8       2       51.3
    9       2       52.4
    10      2       54.6
    11      2       52.2
    12      2       64.3
    13      2       55.0

    Notice that the GROUP field is 1 if the data are from the Present
    Fertilizer group and 2 if the data are from the Newer Fertilizer
    group.









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    PERFORMING THE ANALYSIS

    Once you have entered the data into a database, select the T-TESTS AND
    ANALYSIS OF VARIANCE option from the Analyze menu. Then select the
    COMPARE INDEPENDENT GROUPS option.

    You will be prompted to choose the field name of the grouping
    variable, which in this case is simply GROUP. Enter 1, which chooses
    GROUP. Next, you will be asked for the data field. Enter 2, which
    chooses HEIGHT, the response variable. KWIKSTAT will now perform the
    calculations and display the results on the screen, as illustrated in
    Figure 4.6.

    The means for each group (1=Present, 2=Newer) are displayed. A test
    for equality of variance is also performed to see if the variances
    of the two groups can be considered equal. This is necessary for
    deciding which t-statistic and p-value to use for the text on
    means. A p-value for the equal variances test is displayed. A large
    p-value (e.g., greater than 0.05) indicates that you can consider
    the variances to be equal. In this case, p=0.4807, large enough to
    consider the variances to be equal.

    If the variances are equal, according to this test, you use the "Equal
    variances" t-statistic. Otherwise, use the "Unequal variances" result.
    In this case, the two t-statistics are identical at -1.32. The t-test
    is performed with 11 degrees of freedom, and the p=value associated
    with the test is 0.213. A large p-value (greater than the significance
    level, e.g., 0.05) is usually interpreted to mean that there is no
    significant difference in the means -- the null hypothesis of equal
    means is not rejected. That is, there is not enough evidence to
    conclude that the average height of plants grown with the newer
    fertilizer is significantly different from the average height of
    plants grown with the present fertilizer.

    Type G for a graphical comparison of the two samples. Tukey's five
    number summaries and box plots will appear. Press Esc to continue and
    you will be given the option to print a report for this analysis.

    EXAMPLE 4.8. SINGLE FACTOR ANOVA

    When more than two independent groups are compared with respect to one
    variable, one-way or single factor analysis of variance techniques are
    appropriate. This example uses data for hogs which have been randomly
    assigned to four groups, with each group being given a different feed.
    The response is weight gain.





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    Data for Independent Group ANOVA

       GP1    GP2    GP3    GP4
       60.8   78.7   92.6   86.9
       67.0   77.7   84.1   82.2
       54.6   76.3   90.5   83.7
       61.7   79.8   90.3

    The database to analyze this data is similar to the one used for
    Example 4.7 above, differing only with respect to the number of
    groups. In fact, this one-way ANOVA is an extension of the t-test when
    there are three or more groups.

    See the tutorial in the database section for information on how to
    create and enter this database.

    The results of this test are summarized in the p-value. In this case,
    the small p-value (0.000) means that there is a significant difference
    between groups.

    The ANOVA tells you only that there is a difference among the feeds.In
    order to find out which groups are significantly different from which
    others, press M to choose (M)ultiple comparison. The Newman-Keuls
    multiple comparison test will describe which of the means are
    significantly different from which others (at the 0.05 significance
    level). Figure 4.8 displays a graphical representation of the
    Newman-Keuls multiple comparisons test. See the example of Friedman's
    test for how to interpret the Newman-Keuls chart.

    Box plots are also available to graphically illustrate the differences
    between the groups. Type G (for graphical comparison) and press Enter
    to produce the plots.

    PARAMETRIC REPEATED MEASURES (PAIRED) ANALYSIS

    Repeated measures are observations taken on the same or related
    subjects over time or in differing circumstances. Examples would be
    weight loss, or reaction to a drug across time. Repeated measures may
    also be matched subjects.

    A t-test is performed when there are two groups (two repeated
    measures), and an analysis of variance is performed if there are three
    to ten groups.

    In a database for paired or repeated measures data, each record
    represents one subject (e.g., person, animal). There must be one field
    for each repeated measure (each treatment group). For paired data,
    there are two groups, hence two fields. Thus, in each record, there is
    a field in which to enter data from each observation (treatment) on
    that subject. The hypotheses being tested with a paired t-test or a
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    repeated measures ANOVA is:

    Ho: There is no difference among means of the groups (repeated
    measures).
    Ha: There is a difference among means of the groups.

    EXAMPLE 4.9: PAIRED T-TEST

    The data in this example are before and after weights for eight
    persons on a diet. Notice that in this case, both data values are
    taken from the SAME entity (person).

    Data for paired t-test

    Person  Before  After
     1      162     168
     2      170     136
     3      184     147
     4      164     159
     5      172     143
     6      176     161
     7      159     143
     8      170     145

    The database will include two fields (BEFORE and AFTER) and eight
    records, one for each person. Since the observations are paired, not
    independent, the database reflects this by having each record contain
    a pair of observations. Each record, that is, each person, is
    independent of the over seven persons, but within a record, the before
    and after observations are not independent of each other.

    FIELD   NAME    TYPE     WIDTH  DECIMALS
     1      BEFORE  Numeric   5      0
     2      AFTER   Numeric   5      0

    As a result of the analysis, the means and standard deviations for
    each group are displayed, but more importantly, the mean difference
    between BEFORE and AFTER measurements is given. The statistical
    procedures are performed on this average difference. A 95% confidence
    interval for the mean difference is given, as well as a calculated
    t-statistic and a p-value. These results are interpreted like those of
    a single sample t-test with null hypothesis: mean=0, and alternative
    hypothesis: mean <> 0.

    The calculated t-statistic is 2.37. The p-value associated with the
    test is 0.008. A small p-value such as this is usually interpreted to
    indicate rejection of the null hypothesis and leads to the conclusion
    that the average difference in BEFORE and AFTER weights is not zero,
    i.e., there is evidence of a significant (at the 0.05 level) change of
    weight in these eight subjects on average.
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    EXAMPLE 4.10: ONE-WAY REPEATED MEASURES ANOVA

    For more than a pair of repeated measures on the same subject, a
    one-way repeated measures analysis of variance is appropriate. The
    data in this example are repeated measures of reaction times of five
    persons after being treated with four drugs in randomized order.

    One-way repeated measures ANOVA data

    Person   Drug 1   Drug 2   Drug 3   Drug 4
    1         31       29       17       35
    2         15       17       11       23
    3         25       21       19       31
    4         35       35       21       45
    5         27       27       15       31

    Create a database (named e.g., MEDICINE) with the field names, e.g.,
    DRUG1, DRUG2, DRUG3, DRUG4. For the first record, enter the data for
    the first person 31,29,17,35. The second record will contain
    15,17,11,23 and so forth.

    You will be prompted to choose the fields which you wish to
    compare. Enter 1,2,3,4. KWIKSTAT will now perform the calculations
    and display the results on the screen.

    The results of this ANOVA are summarized in the p-value. In this case,
    the small p-value (p=0.000) means that there is a statistically
    significant difference in the mean response times for the four drugs.

    If you want to determine which of the four drugs are significantly
    different from which others, press M for Multiple comparison. The
    Newman-Keuls multiple comparison test will describe which of the means
    are significantly different from which others (at the 0.05
    significance level).

    INDEPENDENT GROUP TESTS FROM SUMMARY DATA

    This option allows you to perform a one-way ANOVA or a t-test if you
    have only the means, standard deviations and group sizes of two to ten
    groups. Since data are summary, no box plots can be given.









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    SINGLE SAMPLE ANALYSIS

    This option allows you to choose a single variable, and test a
    hypothesis that the mean differs from a hypothesized mean. You must
    enter the hypothesized population mean. The hypotheses you are testing
    in this case are: Ho: The mean equals the hypothesized value. Ha: The
    mean does not equal the hypothesized value.

    USING NON-PARAMETRIC COMPARATIVE PROCEDURES

    Non-parametric procedures are appropriate when the assumption of
    normality cannot be made for a small data set or when a large data set
    is known to be from a non-normal population. Non-parametric procedures
    are generally based on ranks rather than actual data values, so these
    procedures can be useful also when actual data values are not known,
    but the order or ranks of the data values are known.

    NON-PARAMETRIC INDEPENDENT GROUP ANALYSIS - MANN-WHITNEY AND KRUSKAL
    WALLIS TESTS

    In the Non-Parametric Comparison Tests Module, KWIKSTAT uses the
    Mann-Whitney procedure if two independent groups are being compared,
    and the Kruskal Wallis procedure if three or more groups are being
    compared. The hypotheses being tested are:

    Ho: There is no difference in the medians of the groups.
    Ha: There is a difference in the medians of the groups.

    EXAMPLE 4.15: MANN-WHITNEY NON-PARAMETRIC TEST OF TWO INDEPENDENT
    GROUPS

    The data from Example 4.7, are used in this example, the database
    named FERTILIZ.

    KWIKSTAT will perform the calculations and display the results,
    including the Mann-Whitney U statistic, the rank sums, sample sizes
    and mean ranks of the groups, a z statistic and an approximate
    p-value. In this case, U=24.00, z=0.421 and p=0.673. The p-value is
    large so the null hypothesis of no difference in medians between
    groups is not rejected. There is not sufficient evidence based on this
    procedure to say that there is a difference between the median heights
    of plants in the two groups grown using different fertilizers.








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    KRUSKAL-WALLIS PROCEDURE

    If more than two independent groups are being compared using
    non-parametric methods, KWIKSTAT uses the Kruskal Wallis test.

    NON-PARAMETRIC REPEATED MEASURES ANALYSIS  - FRIEDMAN'S TEST

    When repeated observations are taken on the same subject, and there is
    interest in comparing the observations for each repeated measure
    (e.g., each type of treatment), then a repeated measures analysis may
    be appropriate. If you cannot make the assumption that the data that
    being observed are normally distributed with equal variances between
    repeated measures, then a non-parametric analysis is appropriate. One
    method of performing a non-parametric one-way analysis of variance
    (ANOVA) with repeated measures (randomized complete block experimental
    design) is with the Friedman test. (When there are only two groups,
    this test is equivalent to the sign test.) The hypotheses for the
    Friedman test are:

    Ho:There is no difference in mean ranks between repeated measures.
    Ha:There is a difference in mean ranks between repeated measures.

    The following data are the same data used in a previous example for a
    standard repeated measures ANOVA:

    One Way Repeated Measures ANOVA Data

    Person   Drug 1  Drug 2   Drug 3   Drug 4
    1        31      29       17       35
    2        15      17       11       23
    3        25      21       19       31
    4        35      35       21       45
    5        27      27       15       31

    The data presented here are repeated measures of reaction times of 5
    persons after being given 4 drugs in randomized order. For a Friedman
    test, the analysis is performed by ranking the data within each of the
    5 subjects.

    KWIKSTAT calculates the Friedman's Chi-Square and reports the p-value
    associated with the test. If the resulting p-value is low (usually
    less than 0.05), it is appropriate to examine multiple comparisons of
    the groups (repeated measures).

    To perform the FRIEDMAN test, choose the REPEATED MEASURES ANALYSIS
    option from the Non-Parametric Tests menu. You will be prompted to
    choose the field names that represent the repeated measures you want
    to compare. In this case, enter

    1,2,3,4
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    which chooses fields DRUG1, DRUG2, DRUG3 and DRUG4. KWIKSTAT will now
    perform the calculations and display the results on the screen.

    For this data set, a Chi-Square value of 14.13 and a small p-value
    (p=0.00, which means p < 0.005) is reported. The small p-value means
    that there is a statistically significant difference in the mean ranks
    of times for the four drugs. Press Enter to see the results of the
    Newman-Keuls multiple comparison test. This test describes which of
    the mean ranks are significantly different from the others (at the
    0.05 significance level).

    In this case, the following results are reported from the multiple
    comparison procedure:

                    Gp     Gp     Gp     Gp
                    3      2      1      4
    Population 1           ----------------
    Population 2   -----------------

    This table is interpreted in the following way: Any two groups
    underlined by the same line are considered not different at the 0.05
    level of significance. Therefore, the result of this analysis is that
    the mean rank for DRUG 3 is less than the mean rank for DRUG 4. There
    are no other statistically significant pairwise differences among the
    four groups.

    NON-PARAMETRIC DICHOTOMOUS DATA ANALYSIS - COCHRAN'S Q

    Cochran's Q procedure is a non-parametric procedure appropriate for
    use with dichotomous data when the experiment involves repeated
    measures on blocks. Often the blocks are subjects (people or
    animals). The response of the subjects to the treatments is
    dichotomous if it is taken as one of only two possible outcomes, often
    labeled "success" and "failure", rather than as a measurement.
    Cochrans's Q is used to test three or more treatments, or groups, and
    is in fact an extension of McNemar's test for two groups.

    The hypotheses being tested are:

    Ho: The proportion of successes is the same for all treatments.
    Ha: The proportion of successes is not the same for all treatments.








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    USING REGRESSION & CORRELATION PROCEDURES

    To examine the linear relationship between variables, correlation and
    linear regression are used.

    SIMPLE LINEAR REGRESSION ANALYSIS

    Simple linear regression is used for predicting a value of a dependent
    variable using an independent variable. To begin the regression module,
    choose the Regression and Correlation option from the Analyze menu in
    the main KWIKSTAT module. When you choose the Simple Linear Regression
    option, KWIKSTAT will prompt you to give the "independent" and
    "dependent" variables to be used in the analysis. The "independent"
    variable is generally that variable that you can choose, regulate or
    specify (e.g., amount of money spent on advertising) and the
    "dependent" variable is the one you observe and would possibly like to
    predict. After the two variables are chosen, KWIKSTAT will present the
    results of its calculations. The regression equation will be displayed
    along with other results. If the fit is appropriate, the equation may
    be used to predict a new value of the dependent variable given the
    value of the independent variable, within the range of the original
    data.

    The Pearson correlation coefficient is a number between -1.0 and 1.0,
    and tells the strength of the linear relationship between the two
    variables. A correlation coefficient close to -1 or 1 means that the
    relationship is strong, and a correlation close to 0 means that a
    relationship is non-existent or very weak. KWIKSTAT also performs a
    t-test for significance of the slope of the regression line (Ho: Slope
    = 0, Ha: Slope <> 0). This test is equivalent to testing whether the
    population correlation coefficient rho = 0. If the p-value is small
    (less than the chosen significance level) you can conclude that the
    slope of the regression line is not zero. That is, the linear
    relationship is statistically significant.

    Scatterplots of raw data and plots of residuals from linear fit are
    optionally available. Plots are helpful in visually examining the
    relationship between the variables. It is important to verify that
    the relationship is indeed a straight line.

    Since regression and correlation are used to relate variables to
    each other, the database must be structured so that each record
    contains values for each variable. The records often represent time
    periods or locations from which an observed value for each variable
    is available. The fields, then, are the variables and you are asked
    for a value for each field in each record.




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    EXAMPLE 4.19:  SIMPLE LINEAR REGRESSION ANALYSIS

    Data for this example of simple linear regression are Homicide Rate
    and Handgun Licenses Issued per 100,000 population for the years
    1961 to 1973 in Detroit (Fisher, 1976, reprinted from Gunst and
    Mason, 1980).

    Data for simple linear regression (handgun study)

    Year   Homicide   Handguns
           Rate       Registered
    1961    8.60       178.15
    1962    8.90       156.41
    1963    8.52       198.02
    1964    8.89       222.10
    1965   13.07       301.92
    1966   14.57       391.22
    1967   21.36       665.56
    1968   28.03      1131.21
    1969   31.49       837.60
    1970   37.39       794.90
    1971   46.26       817.74
    1972   47.24       583.17
    1973   52.33       709.59

    Since you want to compare homicide rate with handguns registered,
    you need a database with only these two sets of numbers, and can
    exclude year. The data in the database will be from the table
    above, excluding the year column.

    The database will include two fields (Homicide Rate and Guns
    Registered) and thirteen records (one for each year).

    FIELD    NAME        TYPE      WIDTH     DECIMALS
    1        HOMICIDES   Numeric   6         2
    2        HANDGUNS    Numeric   8         2

    The data you will enter in the first record is 8.60 (press Enter)
    and 178.15 (press Enter), and so on.

    PERFORMING THE ANALYSIS

    Enter the Regression module from the Analyze menu, and choose the
    Simple Linear Regression option. You will be prompted to enter the
    INDEPENDENT (X) variable, which in this case is HANDGUNS. Enter 2,
    which chooses HANDGUNS. Next, you will be asked for the DEPENDENT (Y)
    variable. Enter 1, which chooses HOMICIDES. KWIKSTAT will now perform
    the calculations and display the results on the screen.

    Pearson's correlation coefficient (r) is reported (0.7263) as well
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    as R2 (R-Square, 0.5275). The linear regression equation given is a
    mathematical representation of a straight line that passes through a
    plot of the data, and can be used to predict the dependent variable
    (HOMICIDES) given a value for the independent variable (HANDGUNS). In
    this case the linear regression equation is:

    HOMICIDES = 4.910512 + 3.761144E-02 * HANDGUNS

    If you want to predict the homicide rate for 300 handguns registered,
    you would use the equation:

    HOMICIDES = 4.910512 + 3.761144E-02 * 300

    A t-test is performed to test the statistical significance of the
    linear relationship between the two variables. A low p-value means
    that the two variables are significantly related. In this case
    p=0.005, quite small, so the null hypothesis (Slope = 0) is rejected
    and you conclude that the regression line has a slope significantly
    different from zero.

    The program also allows you to view a scatterplot of the data with
    the fitted line and a plot of the residuals.

    MULTIPLE REGRESSION

    Multiple regression is an extension of simple linear regression into
    several dimensions (several independent variables). In the multiple
    regression procedure, you must enter a list of the independent
    variables and a single dependent variable on which you wish to perform
    the regression analysis. In KWIKSTAT you may use up to 10 independent
    variables in this option.

    An analysis of variance is performed to determine the overall
    significance of the model. If the ANOVA reveals a significant
    relationship, (that is, if the p-value is small) the model may be a
    good representation of the sample data.

    A plot of residuals from the fit is available. You may plot the fit
    against any of the variables. Look for patterns in the residuals.
    Patterns other than a horizontal band about zero suggest that the
    assumptions necessary for regression analysis may be violated. If you
    are unfamiliar with multiple regression, the Neter and Wasserman book
    contains an excellent treatment.







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    EXAMPLE 4.20: MULTIPLE REGRESSION ANALYSIS (LONGLEY DATA)

    Longley introduced a data set which has often been used in comparing
    multiple linear regression procedures in the literature. The variables
    refer to economic factors. This example uses the LONGLEY database on
    the KWIKSTAT disk.

    The LONGLEY database consists of 7 fields: DEFLATOR, GNP, UNEMP,
    ARMED, POP, TIME, and TOTAL. The first six of these will be used as
    independent variables and the seventh, TOTAL, is the dependent
    variable (the one to be predicted). Figure 4.15 displays the LONGLEY
    database. You can get this display by using the List (display) the
    contents of a database option on the Data main menu.

    PERFORMING THE ANALYSIS

    In the Multiple Regression procedure, you will be prompted to enter
    the INDEPENDENT  VARIABLE(S), which in this case are DEFLATOR, GNP,
    UNEMP, ARMED, POP, TIME. Enter any combination of 1,2,3,4,5,6 to
    choose the variable(s) you wish to analyze against TOTAL. One way to
    approach a multiple regression problem is to first include all of the
    independent variables. After initial analysis (see below) you may
    decide to eliminate those independent variables found to not be
    significant.

    After entering the independent variables, you will be asked for the
    DEPENDENT VARIABLE. Enter 7, which chooses TOTAL. KWIKSTAT will now
    perform the calculations and display the results on the screen, as
    illustrated in Figure 4.16.

    The table at the top of the screen (in Figure 4.16) tells you the
    intercept value and the coefficient values for each of the
    independent variables. These can be used to create an equation for
    prediction of the dependent variable. In this case, the equation
    is:

    TOTAL = -3481930.1065 + DEFLATOR*(15.0161517122) +
    GNP*(-0.03579443400) + UNEMP*(-2.0199053296) +
    ARMED*(-1.0332049046) + POP*(-0.05130725587) + TIME*(1828.99249535)

    The t-value associated with each coefficient tests its significance
    in the equation. You can use the p-value associated with each
    coefficient to make a decision about the validity of having that
    variable in the equation. A low p-value suggests that the dependent
    variable, TOTAL, is related to the independent variable whose
    p-value you are examining. In this case, you might question the
    validity of having DEFLATOR (p=0.8636), GNP (p=0.3132) and POP
    (p=0.8257) in the equation.

    In choosing the variables to have in such an equation, you also need
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    to consider such questions as multicollinearity, heteroscedasticity
    and parsimony.

    If you wish to delete some variables from the equation, you can do so
    by re-running the analysis an leaving out some variables. The Multiple
    Regression procedure also allows you to plot residuals and to calcuate
    predicted values using the prediction equation.

    CORRELATION ANALYSIS

    The correlation coefficient is a measure of the strength of the
    linear relationship between two variables. KWIKSTAT allows you to
    find both Pearson's and Spearman's (rank) correlation coefficients
    of two variables. It also displays the matrix of correlation
    coefficients of pairs of variables when there are more than two
    variables being considered.

    EXAMPLE 4.22: CORRELATION MATRIX (LONGLEY DATA)

    This example uses the LONGLEY database on disk. You will be prompted
    to choose variables from the list of fields that appears. In this
    case, there are seven fields, and you can choose any combination of
    them. If you want correlation coefficients of all pairs of the seven
    variables, type 1,2,3,4,5,6,7 and press Enter. KWIKSTAT will perform
    the calculations and display the 7 by 7 array shown in Figure 4.17.
    Only half of the array is displayed since the other half is a mirror
    image. The diagonal entries are also omitted since they are all one; a
    variable is always perfectly correlated with itself.

    Each entry in the array consists of two numbers (three numbers if the
    information is printed to a printer). The first (upper) is the
    Pearson's correlation coefficient for the two (row and column)
    variables of that entry. The second (middle) number, in parentheses,
    is the p-value of the t-test for Ho: rho = 0 vs. Ha: rho <> 0. In the
    hard copy printout (if requested), the third (bottom) number, in
    brackets, is the sample size, or number of paired observations used in
    the calculations.

    EXAMPLE 4.23: GRAPHICAL CORRELATION MATRIX (LONGLEY DATA)

    This example also uses the Longley data. You will be prompted to
    choose variables from the list of fields that appears. In this case,
    there are seven fields, and you can choose any combination of them. If
    you want correlation coefficients of all pairs of the seven variables,
    type 1,2,3,4,5,6,7 and press Enter. KWIKSTAT will perform the
    calculations and display the 7 by 7 array of scatterplots. These
    scatterplots are a visual way of examining the relationships between
    pairs of variables.


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    USING FREQUENCY AND CROSSTABULATION PROCEDURES

    The Crosstabulations, Frequencies, Chi Square module performs analyses
    on categorical data, that is, data observed in categories, rather than
    measurement data. Previous examples using measurement data include
    weights of hogs, weights of people, heights of plants, numbers of
    handguns and homicides, and dollar amounts. If, rather than taking a
    measurement, a data observation involves identifying which of a set of
    categories the observation falls into, you are working with
    categorical data.

    Generally, categorical data are entered into a database by using
    one record for each person or entity on which the observation is
    made and one field for each characteristic which is divided into
    categories. For example, to categorize ten people by sex, hair
    color and eye color, you would need ten records (one per person)
    and three fields (e.g., SEX, HAIR, EYE).

    Some of the procedures in this module give you the choice of simply
    entering totals for each category rather than creating a database
    and entering the results of each observation. This can save time if
    totals are known and only totals are needed to perform a test or
    calculation or to produce a graph.

    PERFORMING A FREQUENCIES ANALYSIS

    In the Frequencies, Pictograph, Pie Chart option, KWIKSTAT "counts"
    the occurrence of each data value for a single variable or field
    and displays that information in a table. You can also create a bar
    chart, pictograph and/or pie chart of this information using this
    option.

    EXAMPLE 4.24: FREQUENCY TABLE, PICTOGRAPH, BAR AND PIE CHARTS

    This example uses the EXAMPLE database file on the KWIKSTAT disk.
    One of the fields (variables) in this database is STATUS referring
    to socioeconomic status. Suppose you want to know how the total
    data set is divided up into the five levels of STATUS.

    You will be prompted to enter one field (variable) to use. Since
    you want to do a frequency table on STATUS, enter 7. KWIKSTAT will
    count the data in each of the five categories of STATUS and display
    the results as a frequency table, shown in Figure 4.19.

    You are then prompted to press Enter, which takes you to the
    Frequencies Analysis menu. From this menu you may choose to print
    your table, go back and do another analysis, or create charts
    (Select an option by using the up and down arrow keys to highlight
    the desired option and pressing Enter).

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    You may choose to display a pie chart or pictograph. Selecting Bar
    Chart/Pictograph takes you to another menu from which you can select
    the type of chart you want to produce. BAR1 gives regular size bars
    and BAR2 gives wide bars on a bar chart. Each of the four options
    BEETLE, CAT, PC, PERSON gives a pictograph whose symbol is the item
    listed. (BEETLE is the car, not the insect.)

    A disappearing menu at the bottom of the screen offers the options
    to replot (press R), print (press P), or exit the pictograph or bar
    chart (press Esc). Press Enter to retrieve the bottom menu.

    PERFORMING A GOODNESS OF FIT ANALYSIS

    A goodness-of-fit test of a single population is a test to determine
    if the distribution of observed frequencies in the sample data closely
    matches the expected number of occurrences under a hypothetical
    distribution of the population. The hypotheses being tested are

    Ho: The population distribution follows the hypothesised distribution.
    Ha: The population does not follow the hypothesised distribution.

    EXAMPLE 4.25: GOODNESS-OF-FIT ANALYSIS

    The data for this example come from the text by Zar, 1974, page 46.
    According to a genetic theory, crossbred pea plants show a 9:3:3:1
    ratio of yellow smooth, yellow wrinkled, green smooth, green wrinkled
    offspring. Out of 250 plants, under the theoretical ratio
    (distribution) of 9:3:3:1, you would expect

    about  (9/16)x250=140.625 to produce yellow smooth peas,
           (3/16)x250=46.875             yellow wrinkled,
           (3/16)x250=46.875             green smooth,
           (1/16)x250=15.625             green wrinkled.

    After growing 250 of these pea plants, you observe that

                  152 have yellow smooth peas,
                   39 have yellow wrinkled,
                   53 have green smooth,
                    6 have green wrinkled.










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    PERFORMING THE ANALYSIS

    You will be prompted to enter the number of categories. In this
    case, type 4 for the four categories of peas (yellow smooth, yellow
    wrinkled, green smooth, green wrinkled) and press Enter. You will
    also be asked if you want to enter the expected ratios, or if you
    will be entering the actual expected values into the table. If you
    choose to enter ratios, you will enter

    9,3,3,1

    An empty table will appear with the instructions to enter the
    observed values for each category. Enter the observed values given
    above, pressing Enter after each entry. For example, for the first
    row, enter 152 for observed (Press Enter) enter 39 (Press Enter)
    and so on. KWIKSTAT will perform the calculations (including
    filling in the expected values column) and display the results.

    The calculated chi-square statistic in this case is 8.97 and the
    p-value is 0.031.

    PERFORMING A CROSSTABULATION ANALYSIS (CHI-SQUARE)

    Crosstabulations can be used to perform a chi-square test for
    independence or a chi-square test for homogeneity. A two-way table is
    constructed that displays the number of counts for each category. It
    must be possible to assume that the data observations are independent
    and that each data value can be counted in one and only one category.
    It is also assumed that the number of observations is fixed. KWIKSTAT
    allows you to enter data for a two-way table from the keyboard or from
    a database.

    When you choose to enter the two-way table from the keyboard, KWIKSTAT
    will ask you the size of the table (number of rows and columns). A
    blank table will be presented on the screen, and you will then be
    prompted to enter a number in each cell of the table. If you choose to
    enter the information from a database, KWIKSTAT will prompt you to
    choose the two variables (fields) from the currently active database
    that you wish to tabulate. KWIKSTAT will read the information from the
    database, and construct the table. For instance, in the EXAMPLE
    database, if you choose to tabulate the variables GROUP and STATUS,
    KWIKSTAT will form the table on the screen as illustrated in Figure
    4.23. (Note that the first variable entered is the row variable.)

    For a test for independence, a contingency table looks at two
    categorical variables from a single sample of one population and tests
    whether the two variables are related in some way, (e.g., are sex and
    hair color related?)


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    The hypotheses being tested are

    Ho: The variables are independent of each other. (There is no
    association between them).
    Ha: The variables are not independent of each other.

    KWIKSTAT reports both the chi-square statistic and the p-value. If the
    expected value in one or more cells is less than 5, the chi-square
    test may not be valid. A warning to this effect appears on the screen
    if appropriate. In the case of a 2 by 2 table, Fisher's Exact Test and
    the chi-square with Yates' correction are also performed and results
    displayed.

    SCREEN LIMITATIONS: The limits to tables being displayed on the screen
    are 10 columns by 7 rows. If the table is too big for the screen, only
    the test results are displayed. Tables as large as 15 columns by 100
    rows may be printed on a line printer if data are entered from a
    database.

    EXAMPLE 4.26: CROSSTABULATION ANALYSIS (2 BY 2) TEST FOR INDEPENDENCE

    Data for this example are observations of the number of beetles and
    bugs on the upper and lower sides of leaves (Zar,1974, page 292).

    2 BY 2 CONTINGENCY TABLE DATA

              Beetles   Bugs
              ---------------
    Upper Leaf  12     7
    Lower Leaf   2     8

    Since you are given only the totals for each of the four categories,
    and not the individual data for each leaf, there is no need to create
    a database. Rather, you can just enter these totals from the keyboard.

    PERFORMING THE ANALYSIS

    When you choose the crosstabulations option, you will be asked if you
    want to enter data from a (D)atabase or (K)eyboard. Type K and press
    Enter.

    You will then be prompted to give the size of the table. Enter 2 for
    rows and 2 for columns. An empty table will appear with the
    instructions to enter the counts for each category into the
    appropriate cell. Enter the values given above, pressing Enter after
    each entry.

    The calculated chi-square statistic in this case is 4.89 with a
    p-value of 0.028. The chi-square with Yates correction is 3.31 with a
    p-value of 0.069 and the Fisher Exact Test (two tail) has a p-value of
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    0.050. Because one of the cells produces an expected value less than
    5, KWIKSTAT gives a warning that the chi-square analysis for this data
    may not be valid. Given this warning, it is best to rely on the
    Fisher's Exact Test for making a decision.

    If you choose Output to Printer or File, each cell in the output will
    contain five numbers. The top number is the count that you entered.
    The second number is the calculated expected value used to calculate
    the chi-square statistic. The third number is the percentage of the
    TOTAL number of observations that the observed number in that cell
    represents. the fourth and fifth numbers are percentages of the ROW
    and COLUMN totals that the observed number in that cell represents.

    EXAMPLE 4.27: CONTINGENCY TABLE LARGER THAN 2 BY 2 (SEX BY HAIR COLOR)

    A generalization of the 2 by 2 table is the R by C (Rows by Columns)
    table. This is an example (Zar, 1984, page 62) of a two by four
    contingency table involving the variables hair color and sex. The null
    hypothesis is that there is no relationship between hair color and
    sex.

    2 BY 4 CONTINGENCY TABLE DATA (SEX BY HAIR COLOR)

                   HAIR COLOR
          Black   Brown   Blonde   Red
    -----------------------------------------
    Male   32      43      16       9
    Female 55      65      64       16

    KWIKSTAT will perform the calculations and display the results as
    shown in Figure 4.25. The calculated chi-square statistic in this case
    is 8.99 with a p-value of 0.03.

    DRAWING A 3-D BAR CHART

    KWIKSTAT allows you to draw a 3-dimensional bar chart of data for
    a contingency table (crosstabulation), and then to focus in on a
    part of it if desired.

    Data for the 3-dimensional bar chart must be entered first, either
    from the keyboard or a database, by using the Crosstabulations,
    Chi-Square option of the Frequencies and Crosstabulations Choose
    Analysis Option menu. To get to this menu from the Data main menu,
    select Analyze at the top of the screen, and then select
    Crosstabulations, Frequencies, Chi Square.





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    EXAMPLE 4.28: DRAWING A 3-D BAR CHART (EXAMPLE DATABASE)

    Check the lower left corner of the screen to see which database is
    currently in use. If it is the one you want, EXAMPLE, go on to
    Producing the Chart. If it is not EXAMPLE, retrieve the  EXAMPLE
    database.

    PRODUCING THE CHART

    Choose the option D) CROSSTABULATIONS, CHI-SQUARE from the menu and
    specify to enter data from a database. You will be asked to enter the
    variables to analyze. Choose the variables GROUP and STATUS by
    entering 1,7. KWIKSTAT will display the results of the chi-square test
    in a contingency table. Pressing Enter will bring you to the
    Crosstabulations Analysis menu. Using the up and down arrow keys,
    highlight 3-D Bar Chart and press Enter. You will be given a default
    title for the chart and prompted to enter an alternative if desired.
    You will be similarly prompted for labels for "row" and "column" axes.
    Default labels are also given.

    The disappearing menu at the bottom of the screen gives you the
    options of (R)eplot, (P)rint, or (D)etail (Press Enter to make it
    reappear.)  Pressing D selects Detail, which allows you to look at a
    part of the chart in isolation. For example, suppose in this case you
    want to see a detail of the second category (B) in the "row" field
    (GROUP) by all categories (1,2,3,4,5) of the "column" field (STATUS).
    Press D for detail and you will be prompted to specify the detail
    parameters. When prompted for the first and last row, type 2,2 (that
    is, begin at row 2 and end at row 2). When prompted for the first and
    last column, type 1,5 (begin at column 1 and end at column 5). Figure
    4.27 shows the display of the detail requested. To exit from the plot
    press Esc.

    MCNEMAR'S TEST

    McNemar's test is appropriate for use with paired, dichotomous (i.e.,
    0, 1 data) data. This test is sometimes called a test for related
    samples or a test for the significance of changes. It is useful for
    comparing paired or related observations in which the response is
    dichotomous, that is, the response is one of only two possible
    outcomes. McNemar's test is the 2 by 2 version of Cochran's Q test
    described in the section on non-parametric tests. The test assumes
    that any pair of observations is independent of any other pair of
    observations, although clearly the observations within a pair are not
    independent of each other.





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    USING LIFE TABLES AND SURVIVAL ANALYSIS PROCEDURES

    As the name indicates, this module performs life tables and
    survival analysis procedures. The data must be in the following
    form:

    1) a TIME variable which contains a time (e.g., minutes, days,
    years, etc.) in which the subject or component has been observed to
    be alive (not failed).

    2) a CENSOR variable which must take on the values 0 or 1,  where
    1 means the subject has died (failed), and a 0 means the subject
    was still alive (not failed) at the last available time period.

    3) optionally, a GROUPING variable which may have up to ten values
    (numeric or character), i.e., the data may be in groups.
    Once the data are entered into the program, a life table for each
    group is produced which includes, for each time interval, the
    number entered, withdrawn, lost, dead, exposed, the proportion
    dead, proportion surviving, cumulative proportion surviving, hazard
    and density.

    A plot is given for the cumulative proportion surviving in the
    group(s) against time. If more than one group is entered, a
    Mantel-Haenszel test is performed to test the hypothesis of equal
    survival patterns for the groups.

    A small version of the survival plot will appear on the screen, and
    if you choose to print a report of the session the report will
    include a larger version of the plot along with other information
    from the analysis.

    EXAMPLE 4.31A: LIFE TABLE ANALYSIS

    The data for this example are in the LIFE database on the KWIKSTAT
    disk. These data are from Prentice (1973).

    Open the LIFE database, and begin the Life Table Module from the
    Analyze menu. Use the up and down cursor keys to highlight Life Tables
    and Survival Analysis and press Enter, or simply press B.

    You will be prompted to choose a time variable indicating the
    amount of survival time observed, and a censor variable indicating
    which subjects are still alive (censored) at the last time period.
    In this case, enter 1,2 to choose SURVIVAL as the time variable and
    CENSOR as the censor variable.

    You will then be prompted to choose a grouping variable if you
    wish. If there is no grouping variable or you don't wish to group
    the data, simply press Enter. In this case, type 3 to choose GROUP
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    as the grouping variable, and press Enter. KWIKSTAT reports the
    names and sizes of the groups and then asks you to specify the
    length of each interval for the table to be produced. You can
    specify a desired interval length or you can use the default length
    by simply pressing Enter.

    KWIKSTAT will perform the calculations and display a table which
    includes the numbers of subjects entered alive, withdrawn, dead,
    exposed, the proportion dead, proportion alive, cumulative survival
    proportion and standard error for the first group. Press Enter and
    a second table appears, which includes 95% confidence limits on the
    cumulative survival proportion. A summary of the upper table also
    appears. KWIKSTAT now creates the two similar tables for the second
    group.

    From the tables, you can see that, in the first group, 22 of 37
    exposed, or 59.5% died in the first interval (0.0-99.0) and two
    were withdrawn. In the second group, 12 of 23.5 exposed (51.1%)
    died and one was withdrawn in the first interval.

    KWIKSTAT also draws a small graph of the two survival curves, and
    performs the Mantel-Haenszel comparison of the two curves testing
    the hypothesis:

    Ho: The survival curves are the same.
    Ha: The survival curves are not the same.

    A chi-square statistic is reported, as well as a p-value. A low
    p-value is taken to indicate rejection of the null hypothesis.

    In this example, the Mantel-Haenszel comparison procedure results in a
    chi-square statistic of 0.7191 and a p-value of 0.397. This p-value is
    too large to reject the hypothesis of equal curves. This indicates
    that the two distributions are not significantly different - thus
    neither treatment is superior in terms of survival distributions. At
    the end of a survival analysis, you will be asked if you want to print
    the results to the printer.

    PART V - USING KWIKSTAT UTILITIES
    --------------------------------------------------------

    The KWIKSTAT UTILITY module contains a number of utilities that do not
    fit into any of the other modules.







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    KWIKSTAT 3


    EXPORTING DATA

    You may output the data from your KWIKSTAT (DBF) file into a standard
    ASCII TEXT file. (Often called an SDF file - Standard Data Format
    file.)

    PRINTING A REPORT

    You may output a listing of the data in the dataset (or a selected
    subset of the database) by using the report facility. To use this
    procedure, choose the Print REPORT to printer or file option from the
    Utilities menu.

    IMPORTING DATA FROM 1-2-3 TYPE FILES

    This option is useful for translating 1-2-3 files into a DBF format
    that can be used by the KWIKSTAT program. Begin this option by
    choosing the Convert WKS file to DBF option on the Utilities Menu.
    This translation facility will translate most versions of WK* files.
    An example file on disk to translate is TEST.WKS, which contains data
    in cells A1.H6. The import program will not allow you to specify more
    than 128 columns to translate into a DBF file.

    CREATING AND EDITING KWIKSTAT IMAGES

    The image program allows you to create or edit images to be use by
    the Pictograph procedure. When you begin the IMAGE module, you will be
    asked if you want to create a NEW image, or to edit an OLD image. If
    you choose to enter a NEW image, you will be asked the pixel size.
    Maximum size for an image is 40 pixels (dots) wide and 30 pixels high.
    The Pictograph routine will adjust its graph according to the size of
    the image. In the editor, you may move the cursor around the grid, and
    select to fill a dot by pressing the numbers 1, 2 or 3. To unfill a
    dot, place the cursor at the dot and press the space bar or 0. You
    will see a version of the image in its correct size at the upper right
    of the screen. Once you have created or edited an image, choose the
    (S)ave option to write the information to disk.

    APPENDIX

    INTERPRETING ERROR CODES: If the program encounters a problem it does
    not know how to resolve, it will usually display an error message
    containing an error and reference code. Many times, you can correct
    this error situation by understanding what caused it. For example, if
    you were to get an error number 27, you would know that it was caused
    by your printer sending an "Out of Paper" message to the program.

    If you are unable to resolve a problem, please write down the steps
    you took before the error was encountered, and send it in on the
    Problem Report Form.
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    KWIKSTAT 3



    ERROR CODES:
    5=Illegal function call         57=Device I/O error
    6=Overflow                      58=File already exists
    7=Out of Memory                 61=Disk full
    9=Subscript out of range        62=Input past end of file
    11=Division by zero             63=Bad record number
    14=Out of String Space          64=Bad filename
    24=Device Timeout               67=Too many files
    25=Device fault                 68=Device unavailable
    27=Out of Paper                 70=Permission denied
    50=FIELD overflow               71=Disk not ready
    51=Internal Error               72=Disk media error
    52=Bad filename or number       74=Rename across disks
    53=File not found               75=Path/File access error
    54=Bad file mode                76=Path not found
    55=File already open            81=Invalid filename

































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    KWIKSTAT 3


                        Problem Report form: KWIKSTAT

    Please explain in detail the problem that occurred. If possible,
    send a print out of the results or Print Screen.
    KWIKSTAT VERSION YOU ARE USING:________________________
    KWIKSTAT MODULE where problem occurred:____________________
    YOUR COMPUTER: BRAND/Model_____________________________
    MONITOR TYPE:________AMOUNT OF MEMORY:_______________
    VERSION OF DOS YOU ARE USING:____________________________
    MEMORY RESIDENT PROGRAMS YOU USE:____________________
    PROBLEM:





























    Mail to:TexaSoft, P.O. Box 1169, Cedar Hill, Texas 75104. Or fax to
    214-291-3400, or send E-Mail to Compuserve 70721,3145.








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    KWIKSTAT 3


                                USER'S BALLOT

    Indicate your preference for improvements in KWIKSTAT. On a scale of 0
    to 10: 0 = Low priority, 10 = High priority

    Vote  Proposed change
    ----  -----------------------------------------------------
    ____  Allow "Spreadsheet-like" entry of data
    ____  Ability to sort database
    ____  More ANOVA types
    ____  More Non-parametric statistical tests
    ____  General Linear Model
    ____  Make Report more flexible
    ____  Stem and leaf plot
    ____  Quality Control Module
    ____  More speed
    ____  More graphics
    ____  Improve graphic quality
    ____  Cluster analysis
    ____  Discriminant analysis
    ____  Automate analysis from a command file
    ____  _____________________________________________
    ____  _____________________________________________
    Comments:

















    Mail to:TexaSoft, P.O. Box 1169, Cedar Hill, Texas 75104. Fax
    to:214-291-3400 or send E-Mail to Compuserve 70721,3145.










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