## Description

**Test Bank for Principles and Methods of Statistical Analysis Frieman**

**Downloadable Instructor Test Bank for Principles & Methods of Statistical Analysis By Jerome Frieman, Donald A. Saucier, Stuart S. Miller, ISBN: 9781483358598**

**Table Of Content**

Models

The Classical Statistical Model

Designing Experiments and Analyzing Data

Summary

Questions Raised by the Use of the Classical Statistical Model

Conceptual Exercises

Descriptive Statistics

Histograms

Exploratory Data Analysis

Quantile Plots

Stem-and-Leaf Displays

Letter-Value Displays

Box Plots

Did My Data Come From a Normal Distribution?

Why Should We Care About Looking at Our Data?

Summary

Conceptual Exercises

The Effects of Adding a Constant or Multiplying by a Constant

The Standard Score Transformation

The Effects of Adding or Subtracting Scores From Two Different Distributions

The Distribution of Sample Means

The Central Limit Theorem

Averaging Means and Variances

Expected Value

Theorems on Expected Value

Summary

Conceptual Exercises

Statistical Inference With the Classical Statistical Model

Criteria for Selecting Estimators of Population Parameters

Maximum Likelihood Estimation

Confidence Intervals

Beyond Normal Distributions and Estimating Population Means

Summary

Conceptual Exercises

A Closer Look at Sampling From Non-Normal Populations

The Sample Mean and Sample Median Are L-Estimators

Measuring the Influence of Outliers on Estimates of Location and Spread

?-Trimmed Means as Resistant and Efficient Estimators of Location

Winsorizing: Another Way to Create a Resistant Estimator of Location

Applying These Resistant Estimators to Our Data

Resistant Estimators of Spread

Applying These Resistant Estimators to Our Data (Part 2)

M-Estimators: Another Approach to Finding Resistant Estimators of Location

Which Estimator of Location Should I Use?

Resampling Methods for Constructing Confidence Intervals

A Final Caveat

Summary

Conceptual Exercises

Experimental and Statistical Hypotheses

Estimating Parameters

The Criterion for Evaluating Our Statistical Hypotheses

Creating Our Test Statistic

Drawing Conclusions About Our Null Hypothesis

But Suppose H0 Is False?

Errors in Hypothesis Testing

Power and Power Functions

The Use of Power Functions

p-Values, a, and Alpha (Type I) Errors: What They Do and Do Not Mean

A Word of Caution About Attempting to Estimate the Power of a Hypothesis Test After the Data Have Been Collected

Is It Ever Appropriate to Use a One-Tailed Hypothesis Test?

What Should We Mean When We Say Our Results Are Statistically Significant?

A Final Word

Summary

Conceptual Exercises

Student’s t-test

Distribution of the Independent Groups t-Statistic when H0 Is True

Distribution of the Independent Groups t-Statistic When H0 Is False

Factors That Affect the Power of the Independent Groups t-Test

The Assumption Behind the Homogeneity of Variance Assumption

Graphical Methods for Comparing Two Groups

Suppose the Population Variances Are Not Equal?

Standardized Group Differences as Estimators of Effect Size

Robust Hypothesis Testing

Resistant Estimates of Effect Size

Summary

Conceptual Exercises

Classifying Data

Testing Hypotheses When the Dependent Variable Consists of Only Two Possibilities

The Binomial Distribution

Testing Hypotheses About the Parameter p in a Binomial Experiment

The Normal Distribution Approximation to the Binomial Distribution

Testing Hypotheses About the Difference Between Two Binomial Parameters (p1 – p2)

Testing Hypotheses in Which the Dependent Variable Consists of Two or More Categories

Summary

Conceptual Exercises

The Assumptions Underlying the Classical Statistical Model

The Assumptions Underlying the Randomization Model

Hypotheses for Both Models

The Exact Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior

The Approximate Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior

Using the Randomization Model to Investigate Possible Effects of Treatments

Single-Participant Experimental Designs

Summary

Conceptual Exercises

Additional Resources

Measuring the Degree of Relationship Between Two Interval-Scale Variables

Randomization (Permutation) Model for Testing Hypotheses About the Relationship Between Two Variables

The Bivariate Normal Distribution Model for Testing Hypotheses About Population Correlations

Creating a Confidence Interval for the Population Correlation Using the Bivariate Normal Distribution Model

Bootstrap Confidence Intervals for the Population Correlation

Unbiased Estimators of the Population Correlation

Robust Estimators of Correlation

Assessing the Relationship Between Two Nominal Variables

The Fisher Exact Probability Test for 2 x 2 Contingency Tables With Small Sample Sizes

Correlation Coefficients for Nominal Data in Contingency Tables

Summary

Conceptual Exercises

Assumptions for the Linear Regression Model

Estimating Parameters With the Linear Regression Model

Regression and Prediction

Variance and Correlation

Testing Hypotheses With the Linear Regression Model

Summary

Conceptual Exercises

The Importance of Looking at Our Data

Using Residuals to Check Assumptions

Testing Whether the Relationship Between Two Variables Is Linear

The Correlation Ratio: An Alternate Way to Measure the Degree of Relationship and Test for a Linear Relationship

Where Do We Go From Here?

When the Relationship Is Not Linear

The Effects of Outliers on Regression

Robust Alternatives to the Method of Least Squares

A Quick Peek at Multiple Regression

Summary

Conceptual Exercises

The Point Biserial Correlation Coefficient and the t-Test

Advantages and Disadvantages of Estimating Effect Sizes With Correlation Coefficients or Standardized Group Difference Measures

Confidence Intervals for Effect Size Estimates

Final Comments on the Use of Effect Size Estimators

Summary

Conceptual Exercises

What Are the Sources of Variation in Our Experiments?

Experimental and Statistical Hypotheses

Estimating Variances

When There Are More Than Two Conditions in Your Experiment

Assumptions for Analysis of Variance

Testing Hypotheses About Differences Among Population Means With Analysis of Variance

Factors That Affect the Power of the F-Test in Analysis of Variance

Relational Effect Size Measures for Analysis of Variance

Randomization Tests for Testing for Differential Effects of Three or More Treatments

Using ANOVA to Study the Effects of More Than One Factor on Behavior

Partitioning Variance for a Two-Factor Analysis of Variance

Testing Hypotheses With Two-Factor Analysis of Variance

Testing Hypotheses About Differences Among Population Means With Analysis of Variance

Dealing With Unequal Sample Sizes in Factorial Designs

Summary

Conceptual Exercises

Overview of the General Linear Model Approach

Regression

Simple Versus Multiple Regression

Multiple Regression

Types of Multiple Regression

Interactions in Multiple Regression

Continuous x Continuous Interactions

Categorical x Continuous Interactions

Categorical x Categorical Interactions: ANOVA Versus Regression

Summary

Conceptual Exercises