ch 6 - Chapter 6 Regression This chapter is about making...

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Chapter 6: Regression - This chapter is about making predictions. - The higher the correlation between the independent variable and the dependant variable, the more accurate the predication will be. Regression analysis: - Applies to paired data (Xi,Yi) where X is the independent variable w/ values Xi that are selected in advance and y is the dependant variable with values Yi that are free to vary. - Regression procedures also are applicable when both x and y are free to vary as they are in correlation. Multiple Regression: - The simultaneous use of two or more predictors in predicting a dependent variable Objectives: - How to predict one variable from another - How to determine the line of best fit - The relationship between r and the slopes of the best-fitting regression lines - What the standard error of estimate is and how to interpret it - How to interpret multiple regressions and multiple correlations An Overview of the Prediction Process: - An example of George’s Statistics class, tracked his grades, made a scatter plot, - Predictions based on small samples tend to be unstable. They tend to vary markedly from sample to sample. Improvement can be made by utilizing all the data rather than a small subset of the data. - “I started smoking because I am socially inadequate” –Just thought that would make you smile, don’t be stressed out! 26 hours till your birthday! - Predictions based on the regression line take into account all the sample data and hence are more stable than those based on only the mean of the Y scores corresponding to a given X score. 6.2 Criterion for the line of best fit:
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This note was uploaded on 04/15/2008 for the course STATS 2402 taught by Professor Kirk during the Spring '08 term at Baylor.

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ch 6 - Chapter 6 Regression This chapter is about making...

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