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®

1. Measuring Variation: How Values Differ

In This Chapter

How Variation Is Measured

The Standard Deviation

The Standard Error of the Mean

If you bear with me through this entire book, or even just some of it, you’ll read a considerable amount of material about variability: how numbers that go together in some way also differ from one another. That’s because variation—actually, shared variation—is at the heart of regression analysis. When two or more variables share variance, that means they’re quantitatively related. For example, if the correlation coefficient between two variables is 0.5, they share 25% of their variance: The proportion of shared variance is the square of the correlation coefficient.

So it’s hardly exaggerating to say that ...