Correlation coefficient r value
[DOC File]1
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(Assume data are linear.) (H) A correlation coefficient of -0.25 indicates a _____ _____ correlation. (I) Why not calculate r when data are not linear? (J) Does the population need to be bivariate normal to describe correlation with r? (K) List the null and alternative hypotheses for the p value when testing r.
[DOC File]CORRELATION ANALYSIS
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The value of the multiple correlation R and the test for statistical significance of R are the same for standardized and raw score formulations. Test of R Squared Added An especially useful application of multiple regression analysis is to determine whether a set of variables (Set B) contributes to the prediction of Y beyond the contribution of ...
[DOC File]Correlation - Radford
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Correlation and Regression. How can we explore the relationship between two quantitative variables? Graphically, we can construct a scatterplot. Numerically, we can calculate a correlation coefficient and a regression equation. Correlation. The Pearson correlation coefficient, r, measures the . strength. and the . direction. of a straight-line ...
[DOC File]Correlation and Regression
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It is called the Pearson correlation coefficient (r) – named after Karl Pearson who invented it. It's longer name, the Pearson product-moment correlation, is sometimes used. The formula for computing the Pearson r is as follows: The value of r ranges between +1 and -1: r > 0 indicates a positive relationship of X and Y: as one gets larger ...
How to Interpret a Correlation Coefficient r - dummies
If your correlation coefficient is a larger negative number than the critical region, your data indicate a significant negative correlation. The closer your r-value is to —1.0, the stronger the negative correlation. REPORTING YOUR RESULTS. The examples below show how the results of your analysis of linear correlation should be presented ...
[DOC File]Correlation and Regression
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When the relationship has a straight-line pattern, the Pearson correlation coefficient describes it numerically. We can analyze the data further by finding an equation for the straight line that best describes the pattern. This equation predicts the value of the response(y) variable from the value of the explanatory variable.
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