Chi Square Test
Chi Square Test
The chi-square distribution is commonly used to make inferences about a population
variance. If a population follows the normal distribution, you can draw a sample of
size N from this distribution and form the sum of the squared standardized scores
(chi-square).
This random variable chi-square follows the chi-square probability distribution with n
degrees of freedom (df ), where n is a positive integer equal to N-1. The degrees of
freedom parameter determines the shape of the distribution. With more degrees of
freedom, the skew is less.
CHIDIST
The CHIDIST function returns the area in the upper tail of the chi-square distribution. You use
the CHIDIST function the same way you would use a chi-square distribution table. The
CHIDIST function uses the following syntax:
=CHIDIST (x, df)
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For example, if you pul a random sample of 16 from a population and want to find the
probability of a sample chi-square value (x) 25 or larger, you would enter:
=CHIDIST (25,15)
The function returns the value 0.049943, meaning that a value of 25 or more should in the
long run occur about five times in a hundred.
CHIINV
You can use the CHIINV function to create confidence interval estimates of a population
variance. That is, you use the CHIDIST function if you know x and want to find the probability,
and you use the CHIINV function if you have a probability and want to find x. For example, if
you're creating a product and weigh a sample of 18 units to find a sample variance of 0.36,
you may want to construct a 90% confidence interval estimate of the population variance for
the product. With a sample size of 18, you have 17 degrees of freedom.
To find the upper limit, enter:
=CHIINV (0.95,17)
To find the lower limit, enter:
=CHIINV (0.05,17)
These formulas return the values 8.67175 and 27.5871. Multiply the sample variance of 0.36
by the degrees of freedom and divide this product by each of the values returned from the
CHIINV function to find the lower and upper limits of the confidence interval. You can take the
square root of these values to establish interval estimates of the population standard
deviation.
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CHITEST
The chi-square test is used to test independence of two variables. You can use the chi-square
test to determine whether there is a significant difference between observed and expected
frequencies.
For example, if you want to find out whether soft drink preference differs between male and
female drinkers, you can construct a nul hypothesis that soft drink preference is independent
of the gender of the drinker, and create a worksheet range, or table, of expected results based
on a sample of 93 male drinkers and 85 female drinkers. You can then create a table of the
results of the actual study findings.
TIP: You can use the Microsoft Excel Fisher's test function instead of the chi-square test for
analyzing contingency tables with two rows and two columns. Fisher's test always returns the
exact P value, whereas the chi-square test returns only an approximate p value. Definitely
avoid the chi-square test when the numbers in the contingency table are very small (in the
single digits).
The CHITEST formula uses the fol owing syntax:
=CHITEST (actual range, expected range)
where actual range is the data in the actual sample results table and expected range is the
data from the expected results table.
The formula returns the p-value. You reject the nul hypothesis if this value is less than your
level of significance alpha. So if your level of significance is .05, you would reject it, but not if
your level of significance is .025 or .01. The test for independence is a one-tailed test, so a
level of significance of .05 corresponds with a 95% confidence level.


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