# 11.2: Facts About the Chi-Square Distribution

The notation for the chi-square distribution is:

\[\chi \sim \chi^{2}_{df}\]

where \(df =\) degrees of freedom which depends on how chi-square is being used. (If you want to practice calculating chi-square probabilities then use \(df = n - 1\). The degrees of freedom for the three major uses are each calculated differently.)

For the \(\chi^{2}\) distribution, the population mean is \(\mu = df\) and the population standard deviation is \(\sigma = \sqrt{2(df)}\).

The random variable is shown as \(\chi^{2}\), but may be any upper case letter.

The random variable for a chi-square distribution with \(k\) degrees of freedom is the sum of \(k\) independent, squared standard normal variables.

\[\chi^{2} = (Z_{1})^{2} + ... + (Z_{k})^{2}\]

- The curve is nonsymmetrical and skewed to the right.
- There is a different chi-square curve for each \(df\).
**Figure 11.2.1.** - The test statistic for any test is always greater than or equal to zero.
- When \(df > 90\), the chi-square curve approximates the normal distribution. For \(\chi \sim \chi^{2}_{1,000}\) the mean, \(\mu = df = 1,000\) and the standard deviation, \(\mu = \sqrt{2(1,000)}\). Therefore, \(X \sim N(1,000, 44.7)\), approximately.
- The mean, \(\mu\), is located just to the right of the peak.
**Figure 11.2.2.**

### References

Data from *Parade Magazine*.

“HIV/AIDS Epidemiology Santa Clara County.”Santa Clara County Public Health Department, May 2011.

### Chapter Review

The chi-square distribution is a useful tool for assessment in a series of problem categories. These problem categories include primarily (i) whether a data set fits a particular distribution, (ii) whether the distributions of two populations are the same, (iii) whether two events might be independent, and (iv) whether there is a different variability than expected within a population.

An important parameter in a chi-square distribution is the degrees of freedom \(df\) in a given problem. The random variable in the chi-square distribution is the sum of squares of *df* standard normal variables, which must be independent. The key characteristics of the chi-square distribution also depend directly on the degrees of freedom.

The chi-square distribution curve is skewed to the right, and its shape depends on the degrees of freedom \(df\). For \(df > 90\), the curve approximates the normal distribution. Test statistics based on the chi-square distribution are always greater than or equal to zero. Such application tests are almost always right-tailed tests.

### Formula Review

\[\chi^{2} = (Z_{1})^{2} + (Z_{2})^{2} + ... + (Z_{df})^{2}\] chi-square distribution random variable

\(\mu_{\chi^{2}} = df\) chi-square distribution population mean

\(\sigma_{\chi^{2}} = \sqrt{2(df)}\) Chi-Square distribution population standard deviation

Exercise 11.2.1

If the number of degrees of freedom for a chi-square distribution is 25, what is the population mean and standard deviation?

**Answer**

mean \(= 25\) and standard deviation \(= 7.0711\)

Exercise 11.2.2

If \(df > 90\), the distribution is _____________. If \(df = 15\), the distribution is ________________.

Exercise 11.2.3

When does the chi-square curve approximate a normal distribution?

**Answer**

when the number of degrees of freedom is greater than 90

Exercise 11.2.4

Where is \(\mu\) located on a chi-square curve?

Exercise

Is it more likely the *df* is 90, 20, or two in the graph?

**Figure 11.2.3.**

**Answer**

\(df = 2\)