If the explanatory variable is a perfect predictor of the response variable, then there will be no variation from the line. Recall that the LSLR line is written in the form y=α+ βx. In R you may assign any name you so choose to your variables. Remember that we routinely use x as the explanatory variable and y as the response variable. While it does not matter which variable is considered to be x when evaluating the strength of the relationship, it does matter when we establish the regression model. We will examine the bi-variate data set first to determine the linear relationship between two attributes of individuals or objects in a population. Residual plots will be examined for evidence of patterns that may indicate violation of underlying assumptions. ![]() You will examine data plots and residual plots for single-variable LSLR for goodness of fit. ![]() You will learn to identify which explanatory variable supports the strongest linear relationship with the response variable. ![]() AP Statistics students will use R to investigate the least squares linear regression model between two variables, the explanatory (input) variable and the response (output) variable.
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