Exam 13: Goodness-Of-Fit Tests and Contingency Analysis

It is believed that the number of drivers who are ticketed for speeding on a particular stretch of highway is a Poisson distribution with a mean of 3.5 per hour. A random sample of 100 hours is selected with the following results: Given this information, it can be seen that the cells will need to be combined since the actual number of occurrences at some levels of x is less than 5.

(True/False)

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Question 101

The degrees of freedom for the chi-square goodness-of-fit test are equal to ________, where k is the number of categories.

(Multiple Choice)

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Question 102

When the expected cell frequencies are smaller than 5, the cells should be combined in a meaningful way such that the expected cell frequencies do exceed 5.

(True/False)

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Question 103

In a contingency analysis the expected values are based on the assumption that the two variables are independent of each other.

(True/False)

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Question 104

Unlike the case of goodness-of-fit testing, with contingency analysis there is no restriction on the minimum size for an expected cell frequency.

(True/False)

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Question 105

Showing 101 - 105 of 105

The degrees of freedom for the chi-square goodness-of-fit test are equal to ________, where k is the number of categories.

When the expected cell frequencies are smaller than 5, the cells should be combined in a meaningful way such that the expected cell frequencies do exceed 5.

In a contingency analysis the expected values are based on the assumption that the two variables are independent of each other.

Unlike the case of goodness-of-fit testing, with contingency analysis there is no restriction on the minimum size for an expected cell frequency.

The Where, Why, and How of Data Collection

Graphs, Charts and Tablesdescribing Your Data

Describing Data Using Numerical Measures

Introduction to Probability

Discrete Probability Distributions

Introduction to Continuous Probability Distributions

Introduction to Sampling Distributions

Estimating Single Population Parameters

Introduction to Hypothesis Testing

Estimation and Hypothesis Testing for Two Population Parameters

Hypothesis Tests and Estimation for Population Variances

Analysis of Variance

Introduction to Linear Regression and Correlation Analysis

Multiple Regression Analysis and Model Building

Analyzing and Forecasting Time-Series Data

Introduction to Nonparametric Statistics

Introducing Business Analytics

Introduction to Decision Analysis

Introduction to Quality and Statistical Process Control

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