PSYC 316 Lecture Notes - Lecture 7: Repeated Measures Design, Homoscedasticity, Skewness
10 May 2016
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From correlation to regression: correlation was previously used to look at bivariate associations between two variables, now, we take a step farther and to see how two variables might be related through prediction. Regression: regression, a way of predicting the value of one variable from another: i. ii. It is a hypothetical model of the relationship between two variables. Therefore, we describe the relationship using the equation of a straight line: regression equation, skeleton: Outcome = model + error i: model statements: The model part of the formula is the only thing that changes: it changes depending on the experimental design and the research question, regression equation: i. Yi = b0 + b1xi + i : Population regression equation (capital beta is referring to the model specifically; won"t be looking at this much): ii. Yi = 0 + 1x1 + ei iii. Population predicted equation (apostrophe means prediction for an individual; we will be using this):
