Learning_goals_part3 - 1 Correlation and...

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1. Correlation and Regression (Chapters 13 and 14 of Kiess) You should understand: What regression and correlation are, and be able to explain the meaning of slope, intercept, and correlation coefficient. When to use regression and when correlation, and should know that if a given set of points is standardized around the means of X and Y, the correlation coefficient is equal to the slope of the regression line. The best-fitting regression line passes through the mean of X and Y, and that it minimizes the squared errors of estimation. The regression line represents the central tendency of a bivariate distribution much as the mean does for a univariate distribution, and that the sum of squared errors of estimation represent the variance of the bivariate distribution. Correlation does not imply causation. You should understand the factors that affect the size of r, including nonlinearity, restriction in range, and outliers. You should be able to: Find the best-fitting regression line (slope and intercept) for a small set of points by hand (using one formula). Use regression coefficients to write an equation for a regression line. Explain why a variable should be designated as X or Y in a regression equation. Use the regression equation to calculate predicted values of Y for any value of X. Examine a scatter plot and estimate what the correlation coefficient might be (e.g., is it positive, negative, near zero?) State a null hypothesis regarding the slope, and an alternative hypothesis. You should be able to test the hypothesis using a non-directional or directional t test (as appropriate).
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2. Nonparametric Statistical Tests (Chapter 15 of Kiess)
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This note was uploaded on 09/24/2010 for the course STATS 13a taught by Professor Chen during the Spring '10 term at UCLA.

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Learning_goals_part3 - 1 Correlation and...

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