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Chapter 11. Chi-Square and F Distributions. Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania. The Chi-Square Distribution. The χ 2 distribution has the following features: All possible values are positive.

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chapter 11
Chapter 11

Chi-Square and F Distributions

Understandable Statistics

Ninth Edition

By Brase and Brase

Prepared by Yixun Shi

Bloomsburg University of Pennsylvania

the chi square distribution
The Chi-Square Distribution
  • The χ2 distribution has the following features:
    • All possible values are positive.
    • The distribution is determined solely by the degrees of freedom.
    • The graph of the distribution is skewed right, but as the degrees of freedom increase, the distribution becomes more bell-shaped.
    • The mode of the distribution is at n – 2 (for sample sizes greater than 3).
table 7 in appendix ii
Table 7 in Appendix II
  • The table gives critical values for area that falls to the right of the critical value.
chi square test of independence
Chi-Square: Test of Independence
  • The goal of the test is to determine if one qualitative variable is independent of another qualitative variable.
  • The hypotheses of the test:

H0: The variables are independent

H1: The variables are not independent

chi square test of independence1
Chi-Square: Test of Independence
  • The data will be presented in a contingency table in which the rows will represent one variable and the columns will represent the other variable.
chi square test of independence2
Chi-Square: Test of Independence
  • First, we need to compute the expected frequency, E, in each cell.
chi square test of independence3
Chi-Square: Test of Independence
  • The sample statistic for the test will be:

O = the observed frequency in each cell

E = the expected frequency in each cell

n = the total sample size

chi square test of independence4
Chi-Square: Test of Independence
  • To use Table 7 to estimate the P-value of the test, the degrees of freedom are:
test of homogeneity
Test of Homogeneity
  • A test of homogeneity tests the claim that different populations share the same proportions of specified characteristics.
  • Test of homogeneity is also conducted using contingency tables and the chi-square distribution.
test of homogeneity1
Test of Homogeneity

Obtain random samples from each of the population. For each population, determine the numbers that share a distinct specified characteristic. Make a contingency table with the different populations as the rows (or columns) and the characteristics as the columns (or rows). The values recorded in the cells of the table are the observed value O taken from the samples.

  • Set the level of significance and use the hypotheses

H0: The proportion of each population sharing specified characteristics is the same for all populations.

H1: The proportion of each population sharing specified characteristics is not the same for all populations.

2. Follow steps 2—5 of the procedure used to test for independence.

chi square goodness of fit
Chi-Square: Goodness of Fit
  • We will test whether a given data set “fits” a given distribution.

H0: The population fits the given distribution.

H1: The population has a different distribution.

testing 2
Testing σ2
  • If we have a normal population with variance σ2and a random sample of n measurements with sample variance s2, then

has a chi-square distribution with n – 1 degrees of freedom.

working with the chi square distribution
Working with theChi-Square Distribution

Use Table 7 in Appendix II.

confidence intervals for 2
Confidence Intervals for σ2

Confidence Intervals for σ

testing two variances
Testing Two Variances
  • Assumptions:
    • The two populations are independent of each other.
    • Both populations have a normal distribution (not necessarily the same).
  • We choose population 1 to have the larger sample variance, i.e. s21 ≥ s22.
compute the test statistic
Compute the Test Statistic

We will compare the test statistic to an F distribution, found in Table 8 of Appendix II.

testing two variances using f distribution
Testing Two VariancesUsing F Distribution
  • Use the F-distribution and the type of test tofind or estimate the P-value with Table 8 of Appendix II.
  • Conclude the test. If P-value , then rejectH0. Otherwise do not reject H0.
  • Interpret your conclusion in the context of the application.
using table 8
Using Table 8

Estimate the P-Value for F = 55.2 with d.f.N = 3 and d.f.D = 2

one way anova
  • ANOVA is a method of comparing the means of multiple populations at once instead of completing a series of 2-population tests.
establishing the hypotheses in anova
Establishing the Hypotheses in ANOVA

In ANOVA, there are k groups and k group means.

The general problem is to determine if there exists a difference among the group means.

two way anova
  • Two-Way ANOVA is a statistical technique to study two variables simultaneously.
  • Each variable is called a factor.
  • Each factor can have multiple levels.
  • We can study the different means of the factors as well as the interaction between the factors.
steps for two way anova
Steps For Two-Way ANOVA
  • Establish the hypotheses.
  • Compute the Sums of Squares Values.
  • Compute the Mean Squares Values.
  • Compute the F Statistic for each factor and the interaction.
  • Conclude the test.
concluding the test
Concluding the Test
  • Check the P-Values on the computer output.
  • Always perform the test for interaction first. If you reject the test for interaction, you should not test for a row or column variable effect.
experimental design
Experimental Design
  • Completely Randomized Design: Independent random samples determine the individuals or objects for each treatment group.
  • Block Design: One of the factors is predetermined and not randomized.