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Probability Density Function | The chi-square distribution results when independent variables with standard normal distributions are squared and summed. The formula for the probability density function of the chi-square distribution is where is the shape parameter and is the gamma function. The formula for the gamma function is In a testing context, the chi-square distribution is treated as a "standardized distribution" (i.e., no location or scale parameters). However, in a distributional modeling context (as with other probability distributions), the chi-square distribution itself can be transformed with a location parameter, , and a scale parameter, . The following is the plot of the chi-square probability density function for 4 different values of the shape parameter. | ||||||||||||||||
Cumulative Distribution Function | The formula for the cumulative distribution function of the chi-square distribution is where is the gamma function defined above and is the incomplete gamma function. The formula for the incomplete gamma function is The following is the plot of the chi-square cumulative distribution function with the same values of as the pdf plots above. | ||||||||||||||||
Percent Point Function | The formula for the percent point function of the chi-square distribution does not exist in a simple closed form. It is computed numerically.The following is the plot of the chi-square percent point function with the same values of as the pdf plots above. | ||||||||||||||||
Other Probability Functions | Since the chi-square distribution is typically used to develop hypothesis tests and confidence intervals and rarely for modeling applications, we omit the formulas and plots for the hazard, cumulative hazard, survival, and inverse survival probability functions. | ||||||||||||||||
Common Statistics |
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Parameter Estimation | Since the chi-square distribution is typically used to develop hypothesis tests and confidence intervals and rarely for modeling applications, we omit any discussion of parameter estimation. | ||||||||||||||||
Comments | The chi-square distribution is used in many cases for the critical regions for hypothesis tests and in determining confidence intervals. Two common examples are the chi-square test for independence in an RxC contingency table and the chi-square test to determine if the standard deviation of a population is equal to a pre-specified value. | ||||||||||||||||
Software | Most general purpose statistical software programs support at least some of the probability functions for the chi-square distribution. |
Learning Objectives Create and interpret frequency polygons Create and interpret cumulative frequency polygons Create and interpret overlaid frequency polygons Frequency polygons are a graphical device for understanding the shapes of distributions. They serve the same purpose as histograms, but are especially helpful for comparing sets of data. Frequency polygons are also a good choice for displaying cumulative frequency distributions . To create a frequency polygon, start just as for histograms , by choosing a class interval. Then draw an X-axis representing the values of the scores in your data. Mark the middle of each class interval with a tick mark, and label it with the middle value represented by the class. Draw the Y-axis to indicate the frequency of each class. Place a point in the
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