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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®

3. Simple Regression

In This Chapter

Predicting with Correlation and Standard Scores

Predicting with Regression Coefficient and Intercept

Shared Variance

The TREND() Function

Partial and Semipartial Correlations

As I write this chapter, a program at the University of Virginia, called the Reproducibility Project, is getting considerable publicity for its work. That work consists of replicating published experiments to determine whether the findings stand up when the studies are repeated. The project expends considerable effort to ensure that the conditions that were in place when the experiment was originally conducted are maintained when it’s replicated. Even so, the findings in around 60% of the experiments are substantially weaker when repeated ...