Regression Analysis Microsoft® Excel® (SHARLA SORGE's Library) by Carlberg, Conrad -- Read -- Imperial Library of Trantor

Index

About This E-Book Title Page Copyright Page Contents at a Glance Contents About the Author Acknowledgments We Want to Hear from You! Reader Services Introduction 1. Measuring Variation: How Values Differ

How Variation Is Measured The Standard Deviation The Standard Error of the Mean

2. Correlation

Measuring Correlation Calculating Correlation Correlation and Causation Restriction of Range

3. Simple Regression

Predicting with Correlation and Standard Scores Predicting with Regression Coefficient and Intercept Shared Variance The TREND() Function Partial and Semipartial Correlations

4. Using the LINEST() Function

Array-Entering LINEST() Comparing LINEST() to SLOPE() and INTERCEPT() The Standard Error of a Regression Coefficient The Squared Correlation, R2 The Standard Error of Estimate Understanding LINEST()’s F-ratio The General Linear Model, ANOVA, and Regression Analysis Other Ancillary Statistics from LINEST()

5. Multiple Regression

A Composite Predictor Variable Understanding the Trendline Mapping LINEST()’s Results to the Worksheet Building a Multiple Regression Analysis from the Ground Up Using the Standard Error of the Regression Coefficient Using the Models Comparison Approach to Evaluating Predictors Estimating Shrinkage in R2

6. Assumptions and Cautions Regarding Regression Analysis

About Assumptions The Straw Man Coping with Nonlinear and Other Problem Distributions The Assumption of Equal Spread Unequal Variances and Sample Sizes

7. Using Regression to Test Differences Between Group Means

Dummy Coding Effect Coding Orthogonal Coding Factorial Analysis Statistical Power, Type I and Type II Errors Coping with Unequal Cell Sizes

8 The Analysis of Covariance

Contrasting the Results Structuring a Conventional ANCOVA Structuring an ANCOVA Using Regression Checking for a Common Regression Line Testing the Adjusted Means: Planned Orthogonal Coding in ANCOVA ANCOVA and Multiple Comparisons Using the Regression Approach Multiple Comparisons via Planned Nonorthogonal Contrasts Multiple Comparisons with Post Hoc Nonorthogonal Contrasts

Index