book

Regression Analysis Microsoft® Excel®

by Conrad Carlberg

May 2016

Beginner to intermediate

368 pages

11h 41m

English

Que

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How Variation Is MeasuredThe Standard DeviationThe Standard Error of the Mean
Measuring CorrelationCalculating CorrelationCorrelation and CausationRestriction of Range
Predicting with Correlation and Standard ScoresPredicting with Regression Coefficient and InterceptShared VarianceThe TREND() FunctionPartial and Semipartial Correlations
Array-Entering LINEST()Comparing LINEST() to SLOPE() and INTERCEPT()The Standard Error of a Regression CoefficientThe Squared Correlation, R2The Standard Error of EstimateUnderstanding LINEST()’s F-ratioThe General Linear Model, ANOVA, and Regression AnalysisOther Ancillary Statistics from LINEST()
A Composite Predictor VariableUnderstanding the TrendlineMapping LINEST()’s Results to the WorksheetBuilding a Multiple Regression Analysis from the Ground UpUsing the Standard Error of the Regression CoefficientUsing the Models Comparison Approach to Evaluating PredictorsEstimating Shrinkage in R2
About AssumptionsThe Straw ManCoping with Nonlinear and Other Problem DistributionsThe Assumption of Equal SpreadUnequal Variances and Sample Sizes
Dummy CodingEffect CodingOrthogonal CodingFactorial AnalysisStatistical Power, Type I and Type II ErrorsCoping with Unequal Cell Sizes
Contrasting the ResultsStructuring a Conventional ANCOVAStructuring an ANCOVA Using RegressionChecking for a Common Regression LineTesting the Adjusted Means: Planned Orthogonal Coding in ANCOVAANCOVA and Multiple Comparisons Using the Regression ApproachMultiple Comparisons via Planned Nonorthogonal ContrastsMultiple Comparisons with Post Hoc Nonorthogonal Contrasts

Content preview from Regression Analysis Microsoft® Excel®

5. Multiple Regression

In This Chapter

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

When you move from simple, single-predictor regression to multiple regression, you add one or more predictor variables to the mix. Doing so is very easy to accomplish in the Excel worksheet structure and the function syntax. But it has a major impact on the nature of the analysis, on how you interpret the results, and on the statistical power of the regression.

As to using LINEST() and TREND(), Excel’s ...