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[DOCX File]Correlation and Regression Analysis: SPSS
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Look at the output. You get the same value of r obtained with the correlation analysis, of course. The r 2 shows that our linear model explains 32% of the variance in cyberloafing. The adjusted R 2, also known as the “ shrunken R 2,” is a relatively unbiased estimator of the …
r2 vs r

[DOCX File]R projects 7 and 8 - Gonzaga University
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Multiple R-squared: 0.8688,Adjusted R-squared: 0.8674 F-statistic: 589.6 on 1 and 89 DF, p-value: < 2.2e-16 Although this seems like a pretty good fit, the plot of Residuals vs Fitted shows signs of a pattern and the Normal Q-Q plot of residuals is pretty wiggly.
regression r vs r squared

[DOC File]Chapter 11
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(Correlation r = 0.8667) Example 1: Mortgage Rates & Fees. Y = Interest Rate vs. X = Loan Fee. Description of the Relationship (Correlation is r = – 0.441) Example 2: The Stock Market. Y = Today’s vs. X = Yesterday’s Percent Change (Correlation is r = 0.18) Example 3: Maximizing Yield. Y = Output Yield vs. X =Temperature for an industrial ...
calculate r squared in r

[DOC File]Two different ways to arrive at the value “percent of ...
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Use r*r * 100 % as the percent of variation explained. 2. Feed the X-values (column 1) and their corresponding Y-values (column 2) to either DDXL or another statistics package to find the regression line. Once we have the regression line we can plug each X-value (column 1) into the line to get our predicted values (column 6).
multiple r vs r squared

[DOC File]REGRESSION ANALYSIS ASSIGNMENT
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Regression Analysis #1 (Group A, Variable 1 vs. Variable 2) Regression Equation: R-squared value: F statistic: P-value: Conclusion: Is there is statistically significant linear relationship between the two variables YES. or . NO (circle one) Regression Analysis #2 (Group B, Variable 1 vs. Variable 2) Regression Equation: R-squared value: F ...
r squared and r

[DOC File]Stat 112 Review Notes for Chapter 3, Lecture Notes 1-5
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R squared ranges from 0 to 1, with higher R squared values meaning that the regression model is explaining more of the variability in the response. 9. Prediction Intervals: The best prediction for the of a new observation with is the estimated mean of given : .
multiple r and r square

R value vs r squared