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Chi-square is often used to make conclusions about the population variance.

If the population follows a normal distribution, we can extract a sample of size N this form of distribution and amount of the standard value of the square (chi-square). Chi-square random variable probability distribution chi-square with n degrees of freedom (df), where n is a positive integer equal to N-1. Degrees of freedom determine the parameters of the distribution. With more degrees of freedom, the tendency is less.

CHIDIST

CHIDIST restore function at the top of the chi-square distribution. CHIDIST function is used in the same way you would use a table of chi-square distribution. CHIDIST function uses the following syntax:

CHIDIST

= (x, df)

For example, if you draw a random sample of the population 16 and want to find a probability sample chi-square value (x) 25 or more, you write: (25, 15

= CHIDIST)

function returns 0.049943, which means that the value of 25 or more must eventually occur about five times in a century.

CHIINV

Use CHIINV function to create confidence interval estimates the population variance. That is, using xy CHIDIST if you know you want to find the probability, and use CHIINV if you have the opportunity and want to find x. For example, if you make a product and sample weight 18 units to find the variance of 0.36, you may want to build a 90% confidence interval estimate of population variance for the product. With a sample size of 18, has 17 degrees of freedom.

To find the upper limit, enter:

CHIINV = (0.95,17)

to find the lower limit, enter: (0.05,17

= CHIINV)

this formula the value 8.67175 and 27.5871. Multiply the sample variance of 0.36 degrees of freedom and dividing this product by each of the values returned by the function to find CHIINV lower and upper limits of the confidence interval. You can take the square root of these values to establish interval estimates of the standard deviation.

CHITEST

chi-square test used to test the independence of two variables. You can use the chi square test to determine whether there is a significant difference between the observed frequencies and expected. For example, to determine whether soft drink preference differs between male and female drinkers, you can create a null hypothesis that the soft drinks preferences do not depend on gender and make the consumer a wide spreadsheets, or tables, waiting for the results based on a sample of 93 men and 85 women drinkers drinkers. You can create tables of the current study findings.

TIP: You can use Microsoft Excel Fisher test function is not chi-square test to analyze contingency tables with two rows and two columns. Test fishermen always return the correct value of p, while the chi-square test gives results that approximate value must avoid the chi-square test when the numbers in the contingency table is very small (single digit).

Formula

CHITEST using the following syntax:

= CHITEST (actual range, expected)

Range, where the actual data in the table of actual and expected results of the data to achieve the results table.

formula returns the value that you reject the null hypothesis if this value is lower than the alpha level of significance. So if the level of significance, 05, will reject, but not if the level of significance, or 025, 01. Test of independence was a one-tailed test, so the level of significance, 05 in accordance with the 95% confidence.
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