Home > Store

Regression Analysis Microsoft Excel

eBook (Watermarked)

  • Your Price: $25.59
  • List Price: $31.99
  • Includes EPUB and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

Also available in other formats.

Register your product to gain access to bonus material or receive a coupon.

Description

  • Copyright 2016
  • Dimensions: 7" x 9-1/8"
  • Pages: 368
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-439351-1
  • ISBN-13: 978-0-13-439351-3

        

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

Downloads

Downloads

Follow the instructions to download additional support files.

  1. Click the Download button below to start the download.
  2. If prompted, click Save.
    1. Download

Sample Content

Sample Pages

Download the sample pages (includes Chapter 7 and Index)

Table of Contents

Introduction................................... 1

1 Measuring Variation: How Values Differ.......................... 5

How Variation Is Measured...........................................5

        Sum of Deviations..........................................................6

        Summing Squared Deviations...............................................7

        From the Sum of Squares to the Variance................................10

        Using the VAR.P( ) and VAR.S( ) Functions....................................11

The Standard Deviation................................................14

The Standard Error of the Mean............................................15

        About z-Scores and z-Values.................................................18

        About t-Values.....................................................................23

2 Correlation.........................................29

Measuring Correlation...........................................................................29

        Expressing the Strength of a Correlation.....................30

        Determining a Correlation’s Direction...................................32

Calculating Correlation.......................................................34

        Step One: The Covariance..................................34

        Watching for Signs........................................................36

    From the Covariance to the Correlation Coefficient..........................38

        Using the CORREL( ) Function...................................................41

        Understanding Bias in the Correlation............................41

        Checking for Linearity and Outliers in the Correlation ........................44

        Avoiding a Trap in Charting.............................48

Correlation and Causation..............................................53

        Direction of Cause........................................54

        A Third Variable................................................55

Restriction of Range..........................................................................55

3 Simple Regression.....................................59

Predicting with Correlation and Standard Scores.........................60

        Calculating the Predictions............................61

        Returning to the Original Metric............................63

        Generalizing the Predictions........................................64

Predicting with Regression Coefficient and Intercept.................................65

        The SLOPE( ) Function........................................................65

        The INTERCEPT( ) Function.....................69

        Charting the Predictions....................................70

Shared Variance...........................................71

        The Standard Deviation, Reviewed.............................71

        More About Sums of Squares..................................73

        Sums of Squares Are Additive..............................................74

        R2 in Simple Linear Regression.........................................77

        Sum of Squares Residual versus Sum of Squares Within.......................81

The TREND( ) Function............................................82

        Array-entering TREND( )..........................................84

        TREND( )’s new x’s Argument..................................85

        TREND( )’s const Argument...................................................86

        Calculating the Zero-constant Regression.............................88

Partial and Semipartial Correlations..........................90

        Partial Correlation............................................91

        Understanding Semipartial Correlations........................................................95

4 Using the LINEST( ) Function...........................103

Array-Entering LINEST( ).............................. 103

        Understanding the Mechanics of Array Formulas.....................104

        Inventorying the Mistakes............................................105

Comparing LINEST( ) to SLOPE( ) and INTERCEPT( )..........................107

        The Standard Error of a Regression Coefficient..................................109

        The Meaning of the Standard Error of a Regression Coefficient........................109

        A Regression Coefficient of Zero......................................................110

        Measuring the Probability That the Coefficient is Zero in the Population...............112

        Statistical Inference as a Subjective Decision............................113

        The t-ratio and the F-ratio..............................116

        Interval Scales and Nominal Scales.............................116

The Squared Correlation, R2.....................................117

The Standard Error of Estimate...........................120

        The t Distribution and Standard Errors.......................121

        Standard Error as a Standard Deviation of Residuals..............125

        Homoscedasticity: Equal Spread................................128

Understanding LINEST( )’s F-ratio....................129

        he Analysis of Variance and the F-ratio in Traditional Usage......................129

        The Analysis of Variance and the F-ratio in Regression.........................131

        Partitioning the Sums of Squares in Regression.....................133

        The F-ratio in the Analysis of Variance........................................136

        The F-ratio in Regression Analysis..................................................140

        The F-ratio Compared to R2............................................................................146

The General Linear Model, ANOVA, and Regression Analysis........................146

Other Ancillary Statistics from LINEST( ).....................................149

5 Multiple Regression...................................151

A Composite Predictor Variable.........................152

        Generalizing from the Single to the Multiple Predictor........................153

        Minimizing the Sum of the Squared Errors.......................................156

Understanding the Trendline...........................................................160

Mapping LINEST( )’s Results to the Worksheet......................................163

Building a Multiple Regression Analysis from the Ground Up......................166

        Holding Variables Constant............................................166

        Semipartial Correlation in a Two-Predictor Regression................167

        Finding the Sums of Squares....................................169

        R2 and Standard Error of Estimate......................................170

        F-Ratio and Residual Degrees of Freedom.................................172

        Calculating the Standard Errors of the Regression Coefficients...........................173

        Some Further Examples................................................176

Using the Standard Error of the Regression Coefficient..........................181

        Arranging a Two-Tailed Test....................................186

        Arranging a One-Tailed Test.....................................189

Using the Models Comparison Approach to Evaluating Predictors...................192

        Obtaining the Models’ Statistics.......................................192

        Using Sums of Squares Instead of R2............................196

Estimating Shrinkage in R2..................................................197

6 Assumptions and Cautions Regarding Regression Analysis................199

About Assumptions.................................................199

        Robustness: It Might Not Matter...................................202

        Assumptions and Statistical Inference.................................204

The Straw Man............................................................................204

Coping with Nonlinear and Other Problem Distributions.........................211

The Assumption of Equal Spread...........................................213

        Using Dummy Coding..........................................215

        Comparing the Regression Approach to the t-test Approach..................217

        Two Routes to the Same Destination.....................................218

Unequal Variances and Sample Sizes..................................220

        Unequal Spread: Conservative Tests..........................................220

        Unequal Spread: Liberal Tests.............................................................225

        Unequal Spreads and Equal Sample Sizes.........................226

        Using LINEST()Instead of the Data Analysis Tool......................................230

        Understanding the Differences Between the T.DIST()Functions........................231

        Using Welch’s Correction................................237

        The TTEST()Function................................................243

7 Using Regression to Test Differences Between Group Means.........................245

Dummy Coding.............................................................246

        An Example with Dummy Coding....................................246

        Populating the Vectors Automatically.....................................250

        The Dunnett Multiple Comparison Procedure..........................253

Effect Coding...................................................................259

        Coding with -1 Instead of 0.........................................260

        Relationship to the General Linear Model..............................261

        Multiple Comparisons with Effect Coding...............................264

Orthogonal Coding................................................267

        Establishing the Contrasts................................267

        Planned Orthogonal Contrasts Via ANOVA..........................268

        Planned Orthogonal Contrasts Using LINEST( )...........................269

Factorial Analysis.......................................................272

        Factorial Analysis with Orthogonal Coding....................274

        Factorial Analysis with Effect Coding..............................279

Statistical Power, Type I and Type II Errors.....................283

        Calculating Statistical Power..............................285

        Increasing Statistical Power...........................................286

Coping with Unequal Cell Sizes.......................................288

        Using the Regression Approach...............................289

        Sequential Variance Assignment...............................................291

8 The Analysis of Covariance..............................295

Contrasting the Results.............................................297

        ANCOVA Charted................................305

Structuring a Conventional ANCOVA......................308

        Analysis Without the Covariate....................308

        Analysis with the Covariate..............................310

Structuring an ANCOVA Using Regression.......................315

Checking for a Common Regression Line..........................316

        Summarizing the Analysis...............................320

Testing the Adjusted Means: Planned Orthogonal Coding in ANCOVA...............321

ANCOVA and Multiple Comparisons Using the Regression Approach.......................328

Multiple Comparisons via Planned Nonorthogonal Contrasts..................................330

Multiple Comparisons with Post Hoc Nonorthogonal Contrasts...............................332

TOC, 9780789756558, 4/13/2016

    

Updates

Updates & Corrections

Please visit the author's site at conradcarlberg.com.

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020