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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.
-------------------------------------------------------------------
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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KWIKSTAT 3
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
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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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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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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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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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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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