Question 1

If the test statistic for a chi-squared goodness-of-fit test is larger than the critical value, the null hypothesis should be rejected.

Accepted Answer

A) True 
 B)False

Question 2

Medical statistics show that deaths due to four major diseases - call them A, B, C and D - account for 15%, 21%, 18%, and 14%, respectively, of all non-accidental deaths. A study of the causes of 616 non-accidental deaths at a hospital give the following counts:   Do these data provide sufficient evidence to indicate that the proportions of people dying of diseases A, B, C, and D at this hospital differ from the proportions accumulated for the population at large?
Find the approximate p-value and use it to make your decision.
Compute   :
______________
Reject   when   > ______________.
Thus the p-value ______________.
Conclude that the proportions of people dying of diseases A, B, C, and D at this hospital ______________ from the proportions for the larger population.

Accepted Answer

63.535; 14

Question 3

In a typical contingency-table test, we calculate each expected cell frequency as the product of row total times column total times sample size.

Accepted Answer

A) True 
 B)False

Question 4

The following table shows the results of a study in which random samples of 200 members of each of five large unions were asked whether they are for, undecided, or against a certain piece of legislation.   Use the 0.025 level of significance to test whether the unions differ with respect to their views.
Compute   = ______________
Reject   when   > ______________.
Conclude:
The unions ______________ different views.

Accepted Answer

71.178; 17

Question 5

Numbers in a contingency table show the frequency of occurrence of all possible combinations of categories.

Accepted Answer

A) True 
 B)False

Question 6

Consumer panel preferences for three proposed fast food restaurants are as follows:   Use 0.05 level of significance and test to see if there is a preference among the three restaurants.   = ______________
Critical Value = ______________
Conclusion: ______________
There ______________ preference among the three restaurants.

Accepted Answer

4.96; 5.99

Question 7

In a chi-square test of independence, the value of the test statistic was   = 9.572, and the critical value at   was 11.1433. Thus,

Accepted Answer

A)  we fail to reject the null hypothesis at 
B)  we reject the null hypothesis at 
C)  we don't have enough evidence to accept or reject the null hypothesis at 
D)  we should decrease the level of significance in order to reject the null hypothesis 
E)  none of these 
A)  we fail to reject the null hypothesis at 
B)  we reject the null hypothesis at 
C)  we don't have enough evidence to accept or reject the null hypothesis at 
D)  we should decrease the level of significance in order to reject the null hypothesis 
E)  none of these

Question 8

A market research study was conducted to compare three different brands of car oil. The results of the study are summarized below.   Use the accompanying MINITAB output and   = 0.005 to determine whether the brand of oil is independent of opinion.
Compute   = ______________
Reject   when   > ______________.
Conclude that the two variables are ______________.

Accepted Answer

44.192; 14

Question 9

A chi-square test of independence is applied to a contingency table with 4 rows and 5 columns for two qualitative variables. The degrees of freedom for this test will be:

Accepted Answer

A)  20 
B)  16 
C)  15 
D)  12 
E)  9 
A)  20 
B)  16 
C)  15 
D)  12 
E)  9

Question 10

A goodness-of-fit test determines the likelihood that sample data have been generated from a population that conforms to a specified type of probability distribution.

Accepted Answer

A) True 
 B)False

Question 11

If we want to conduct a one-tail test of a population proportion, we can employ:

Accepted Answer

A)  z-test of a population proportion 
B)  the chi-square goodness-of-fit test since 
C)  the chi-square test of independence 
D)  none of these 
E)  all of these 
A)  z-test of a population proportion 
B)  the chi-square goodness-of-fit test since 
C)  the chi-square test of independence 
D)  none of these 
E)  all of these

Question 12

Consider a cell in a contingency table. Given the cell's row total of 80, the cell's column total of 60, and a sample size of 250, the cell's expected frequency:

Accepted Answer

A)  equals 19.2 
B)  equals 3.125 
C)  equals 20.0 
D)  equals 1.786 
E)  equals 140 
A)  equals 19.2 
B)  equals 3.125 
C)  equals 20.0 
D)  equals 1.786 
E)  equals 140

Question 13

The chi-square test of a contingency table is based upon:

Accepted Answer

A)  two qualitative variables 
B)  two quantitative variables 
C)  three or more qualitative variables 
D)  three or more quantitative variables 
E)  all of these 
A)  two qualitative variables 
B)  two quantitative variables 
C)  three or more qualitative variables 
D)  three or more quantitative variables 
E)  all of these

Question 14

A chi-square test for independence is applied to a contingency table with 4 rows and 4 columns for two qualitative variables. The degrees of freedom for this test must be 9.

Accepted Answer

A) True 
 B)False

Question 15

The sampling distribution of the test statistic for a goodness-of-fit test with k categories is:

Accepted Answer

A)  Student t distribution with k-1 degrees of freedom 
B)  normal distribution 
C)  chi-square distribution with k-1 degrees of freedom 
D)  approximately chi-square distribution with k-1 degrees of freedom 
E)  approximately chi-square distribution with k degrees of freedom 
A)  Student t distribution with k-1 degrees of freedom 
B)  normal distribution 
C)  chi-square distribution with k-1 degrees of freedom 
D)  approximately chi-square distribution with k-1 degrees of freedom 
E)  approximately chi-square distribution with k degrees of freedom

Question 16

The degrees of freedom associated with a contingency-table test of independence equal the number of rows times the number of columns in the table.

Accepted Answer

A) True 
 B)False

If the test statistic for a chi-squared goodness-of-fit test is larger than the critical value, the null hypothesis should be rejected.

In a typical contingency-table test, we calculate each expected cell frequency as the product of row total times column total times sample size.

Numbers in a contingency table show the frequency of occurrence of all possible combinations of categories.

Consumer panel preferences for three proposed fast food restaurants are as follows: Use 0.05 level of significance and test to see if there is a preference among the three restaurants. = Critical Value = Conclusion: There preference among the three restaurants.

In a chi-square test of independence, the value of the test statistic was = 9.572, and the critical value at was 11.1433. Thus,

A chi-square test of independence is applied to a contingency table with 4 rows and 5 columns for two qualitative variables. The degrees of freedom for this test will be:

A goodness-of-fit test determines the likelihood that sample data have been generated from a population that conforms to a specified type of probability distribution.

If we want to conduct a one-tail test of a population proportion, we can employ:

Consider a cell in a contingency table. Given the cell's row total of 80, the cell's column total of 60, and a sample size of 250, the cell's expected frequency:

The chi-square test of a contingency table is based upon:

A chi-square test for independence is applied to a contingency table with 4 rows and 4 columns for two qualitative variables. The degrees of freedom for this test must be 9.

The sampling distribution of the test statistic for a goodness-of-fit test with k categories is:

The degrees of freedom associated with a contingency-table test of independence equal the number of rows times the number of columns in the table.

Describing Data With Graphs

Describing Data With Numerical Measures

Describing Bivariate Data

Probability and Probability Distributions

Several Useful Discrete Distributions

The Normal Probability Distribution

Sampling Distributions

Large-Sample Estimation

Large-Sample Tests of Hypotheses

Inference From Small Samples

The Analysis of Variance

Linear Regression and Correlation

Multiple Regression Analysis

Nonparametric Statistics

Filters

Exam 14: Analysis of Categorical Data

If the test statistic for a chi-squared goodness-of-fit test is larger than the critical value, the null hypothesis should be rejected.

In a typical contingency-table test, we calculate each expected cell frequency as the product of row total times column total times sample size.

Numbers in a contingency table show the frequency of occurrence of all possible combinations of categories.

In a chi-square test of independence, the value of the test statistic was = 9.572, and the critical value at was 11.1433. Thus,

A chi-square test of independence is applied to a contingency table with 4 rows and 5 columns for two qualitative variables. The degrees of freedom for this test will be:

A goodness-of-fit test determines the likelihood that sample data have been generated from a population that conforms to a specified type of probability distribution.

If we want to conduct a one-tail test of a population proportion, we can employ:

Consider a cell in a contingency table. Given the cell's row total of 80, the cell's column total of 60, and a sample size of 250, the cell's expected frequency:

The chi-square test of a contingency table is based upon:

A chi-square test for independence is applied to a contingency table with 4 rows and 4 columns for two qualitative variables. The degrees of freedom for this test must be 9.

The sampling distribution of the test statistic for a goodness-of-fit test with k categories is:

The degrees of freedom associated with a contingency-table test of independence equal the number of rows times the number of columns in the table.

Describing Data With Graphs

Describing Data With Numerical Measures

Describing Bivariate Data

Probability and Probability Distributions

Several Useful Discrete Distributions

The Normal Probability Distribution

Sampling Distributions

Large-Sample Estimation

Large-Sample Tests of Hypotheses

Inference From Small Samples

The Analysis of Variance

Linear Regression and Correlation

Multiple Regression Analysis

Nonparametric Statistics

Filters