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®

8 The Analysis of Covariance

In This Chapter

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

In previous chapters I’ve noted that it’s possible to use both a factor, measured originally on a nominal scale, and a covariate, measured on an interval scale, as predictors in the same regression equation. For example, suppose that you assign members of a group of 20 people who want to lose weight to one of two groups: one group ...