<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Calculates the point at which a line will intersect the y-values by using existing x-values (known_x's) and y-values (known_y's).</help:help-text></p>
<p class="Paragraph"><span class="T1">Known_y's</span> is the dependent set of observations or data.</p>
<p class="Paragraph"><span class="T1">Known_x's</span> is the independent set of observations or data.</p>
<p class="Paragraph">Names, matrices or references containing numbers must be used here. Numbers can, of course, also be entered directly.</p>
<p class="Head3">Example</p>
<p class="Paragraph">To calculate the intercept, use cells D3:D9 as the y value and C3:C9 as the x value from the example spreadsheet. Input will be as follows:</p>
<p class="Paragraph">INTERCEPT(D3:D9;C3:C9) = 2.15. The result is 2.15.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Counts how many values are in the list of arguments.</help:help-text> Text entries are ignored, and the individual values are added together.</p>
<p class="Paragraph"><span class="T1">Value1; value2, ...</span> are 1 to 30 values arguments representing the values to be counted.</p>
<p class="Head3">Example</p>
<p class="Paragraph">The entries <span class="T1">2,4,6</span> and <span class="T1">eight</span> in the <span class="T1">value 1-4</span> fields are to be counted.</p>
<p class="Paragraph">COUNT(2;4;6;eight) = 3. The number of entries is therefore <span class="T1">3</span>.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Counts how many values are in the list of arguments.</help:help-text> Text entries are also counted. The individual values will be added together, and empty arguments are ignored.</p>
<p class="Paragraph"><span class="T1">value1; value2, ...</span> are 1 to 30 arguments representing the values to be counted.</p>
<p class="Head3">Example</p>
<p class="Paragraph">The entries <span class="T1">2,4,6</span> and <span class="T1">eight</span> in the <span class="T1">value1-4</span> fields are to be counted.</p>
<p class="Paragraph">COUNTA(2;4;6;eight) = 4. The number of entries is therefore <span class="T1">4</span>.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the probability of a sample with binomial distribution.</help:help-text></p>
<p class="Paragraph"><span class="T1">Trials</span> is the number of independent trials.</p>
<p class="Paragraph"><span class="T1">Probability_s</span> is the probability of success on each trial.</p>
<p class="Paragraph"><span class="T1">T1</span> defines the lower limit for the number of trials.</p>
<p class="Paragraph"><span class="T1">T2</span> (optional) defines the upper limit for the number of trials.</p>
<p class="Head3">Example</p>
<p class="Paragraph">What is the probability with ten throws of the dice, that a six will come up exactly twice? The probability of a six (or any other number) is 1/6. The following formula combines these factors:</p>
<p class="Paragraph">=B(10; 1/6; 2) returns a probability of 29%.</p>
<p class="Paragraph"><help:key-word value="determination coefficient" tag="kw66884_10" xmlns:help="http://openoffice.org/2000/help"/><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the square of the Pearson correlation coefficient based on the given values.</help:help-text> RSQ (also called determination coefficient) is a measure for the accuracy of an adjustment and can be used to produce a regression analysis.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the inverse of the cumulative beta probability density function.</help:help-text></p>
<p class="Paragraph"><span class="T1">Number</span> is the value between <span class="T1">A</span> and <span class="T1">B</span> at which to evaluate the function.</p>
<p class="Paragraph"><span class="T1">Alpha</span> is a parameter to the distribution.</p>
<p class="Paragraph"><span class="T1">Beta</span> is a parameter to the distribution.</p>
<p class="Paragraph"><span class="T1">A</span> (optional) is the lower bound for <span class="T1">number</span>.</p>
<p class="Paragraph"><span class="T1">B</span> (optional) is the upper bound for <span class="T1">number</span>.</p>
<p class="Head3">Example</p>
<p class="Paragraph">=BETAINV(0.5; 5; 10) returns the value <span class="T1">0.33</span>.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the cumulative beta probability density function.</help:help-text></p>
<p class="Paragraph"><span class="T1">Number</span> is the value between <span class="T1">A</span> and <span class="T1">B</span> at which to evaluate the function.</p>
<p class="Paragraph"><span class="T1">Alpha</span> is a parameter to the distribution.</p>
<p class="Paragraph"><span class="T1">Beta</span> is a parameter to the distribution.</p>
<p class="Paragraph"><span class="T1">A</span> (optional) is the lower bound for <span class="T1">number</span>.</p>
<p class="Paragraph"><span class="T1">B</span> (optional) is the upper bound for <span class="T1">number</span>.</p>
<p class="Head3">Example</p>
<p class="Paragraph">=BETADIST(0.75; 3; 4) returns the value <span class="T1">0.96</span></p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the individual term binomial distribution probability.</help:help-text></p>
<p class="Paragraph"><span class="T1">Number_s</span> is the number of successes in a set of trials.</p>
<p class="Paragraph"><span class="T1">Trials</span> is the number of independent trials.</p>
<p class="Paragraph"><span class="T1">Probability_s</span> is the probability of success on each trial.</p>
<p class="Paragraph"><span class="T1">Cumulative</span> = 0 calculates the single probability and <span class="T1">cumulative</span> = 1 calculates the cumulative probability.</p>
<p class="Head3">Example</p>
<p class="Paragraph">=BINOMDIST(A1; 12; 0,5; 0) shows (if the values 0 to 12 are entered in A1) the probabilities for 12 flips of a coin that <span class="T1">Heads</span> will come up exactly the number of times entered in A1.</p>
<p class="Paragraph">=BINOMDIST(A1; 12; 0,5; 1) shows the cumulative probabilities for the same series, i.e. if A1 = 4, the probability of 0, 1, 2, 3 or 4 times <span class="T1">Heads</span> (non-exclusive OR).</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the inverse of the one-tailed probability of the chi-squared distribution.</help:help-text></p>
<p class="Paragraph">The Chi square distribution is a distribution from statistics that is taken as the basis for the "Chi square test". A hypothesis is examined in this test: If the observed extent of randomness is sufficient for the preset distribution law (thus the Chi square distribution), then the hypothesis is confirmed. Granted the assumption that the hypothesis is correct, the observed Chi square would have to correspond to the hypothetical Chi square of the theoretical Chi square distribution (at least roughly).</p>
<p class="Paragraph">Chi square is determined as the sum of</p>
<p class="Paragraph">Since Chi square is a measure of the deviation from the true (observed) distribution from the hypothetical (theoretical) distribution, the hypothesis will be rejected if the value calculated for Chi square from a concrete random sample exceeds a certain critical value. The function CHIINV returns this critical value for the probability of error that was preset and the degrees of freedom. It can also be found in tables in mathematical reference works.</p>
<p class="Paragraph"><span class="T1">Number</span> is the value of the error probability, where the critical size CHIINV should be calculated, in other words, it is the probability that secures the hypothesis.</p>
<p class="Paragraph"><span class="T1">Degrees_freedom</span> is the degrees of freedom of the experiment.</p>
<p class="Head3">Example</p>
<p class="Paragraph">A die is thrown 1020 times. The numbers on the die 1 through 6 come up 195, 151, 148, 189, 183 and 154 times (observation values). The hypothesis that the die is not fixed is to be tested.</p>
<p class="Paragraph">The Chi square distribution of the random sample is determined by the formula given above. Since the expected value for a given number on the die for n throws is n times 1/6, thus 1020/6 = 170, the formula returns a Chi square value of 13.27.</p>
<p class="Paragraph">If the (observed) Chi square is greater than or equal to the (theoretical) Chi square CHIINV, the hypothesis will be discarded, since the deviation between theory and experiment is too great. If the observed Chi square is less that CHIINV, the hypothesis is confirmed with the indicated probability of error.</p>
<p class="Paragraph">If the probability of error is 5%, the die is not true. If the probability of error is 2%, there is no reason to believe it is fixed.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the probability of a deviance from a random distribution of two test series based on the chi-squared test for independence.</help:help-text> CHIDIST returns the chi-squared distribution of the data.</p>
<p class="Paragraph">The probability determined by CHITEST can also be determined with CHIDIST, in which case the Chi square of the random sample must then be passed as a parameter instead of the data row.</p>
<p class="Paragraph">=CHITEST(A1:A6; B1:B6) equals 0,02. This is the probability which suffices the observed data of the theoretical Chi-square distribution.</p>
<p class="Paragraph"><help:help-text value="visible" xmlns:help="http://openoffice.org/2000/help">Returns the probability value from the indicated Chi square that a hypothesis is confirmed.</help:help-text> CHIDIST compares the Chi square value to be given for a random sample that is calculated from the sum of (observed value-expected value)^2/expected value for all values with the theoretical Chi square distribution and determines from this the probability of error for the hypothesis to be tested.</p>
<p class="Paragraph">The probability determined by CHIDIST can also be determined by CHITEST, in which case instead of the Chi square of the random sample, the observed and expected data must be passed as the parameter.</p>
<p class="Paragraph"><span class="T1">Number</span> is the chi-square value of the random sample to which the error probability should be detected.</p>
<p class="Paragraph"><span class="T1">Degrees_freedom</span> are the degrees of freedom of the experiment.</p>
<p class="Paragraph">If the Chi square value of the random sample is 13.27 and if the experiment has 5 degrees of freedom, then the hypothesis is assured with a probability of error of 2%.</p>
<p class="Paragraph"><span class="T1">X</span> is the value of the function.</p>
<p class="Paragraph"><span class="T1">Lambda</span> is the parameter value.</p>
<p class="Paragraph"><span class="T1">Cumulative</span> is a logical value that determines the form of the function. <span class="T1">Cumulative = 0</span> calculates the <help:key-word value="density function" tag="kw66884_1" xmlns:help="http://openoffice.org/2000/help"/>density function, and <span class="T1">Cumulative = 1</span> calculates the distribution.</p>
