Book description
This is today’s most complete guide to regression analysis with Microsoft® Excel for any business analytics or research task. Drawing on 25 years of advanced statistical experience, Microsoft MVP Conrad Carlberg shows how to use Excel’s regression-related worksheet functions to perform a wide spectrum of practical analyses.
Carlberg clearly explains all the theory you’ll need to avoid mistakes, understand what your regressions are really doing, and evaluate analyses performed by others. From simple correlations and t-tests through multiple analysis of covariance, Carlberg offers hands-on, step-by-step walkthroughs using meaningful examples.
He discusses the consequences of using each option and argument, points out idiosyncrasies and controversies associated with Excel’s regression functions, and shows how to use them reliably in fields ranging from medical research to financial analysis to operations.
You don’t need expensive software or a doctorate in statistics to work with regression analyses. Microsoft Excel has all the tools you need—and this book has all the knowledge!
Understand what regression analysis can and can’t do, and why
Master regression-based functions built into all recent versions of Excel
Work with correlation and simple regression
Make the most of Excel’s improved LINEST() function
Plan and perform multiple regression
Distinguish the assumptions that matter from the ones that don’t
Extend your analysis options by using regression instead of traditional analysis of variance
Add covariates to your analysis to reduce bias and increase statistical power
Table of contents
- 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
- 2. Correlation
- 3. Simple Regression
-
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
- 7. Using Regression to Test Differences Between Group Means
-
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
Product information
- Title: Regression Analysis Microsoft® Excel®
- Author(s):
- Release date: May 2016
- Publisher(s): Que
- ISBN: 9780134393537
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