The matrix plot above allows us to vizualise the relationship among all variables in one single image. plot ( newdata, pch = 16, col = "blue", main = "Matrix Scatterplot of Income, Education, Women and Prestige" ) The women variable refers to the percentage of women in the profession and the prestige variable refers to a prestige score for each occupation (given by a metric called Pineo-Porter), from a social survey conducted in the mid-1960s. Remember that Education refers to the average number of years of education that exists in each profession. Our response variable will continue to be Income but now we will include women, prestige and education as our list of predictor variables. The columns relate to predictors such as average years of education, percentage of women in the occupation, prestige of the occupation, etc.įor our multiple linear regression example, we’ll use more than one predictor. Each row is an observations that relate to an occupation. If you recall from our previous example, the Prestige dataset is a data frame with 102 rows and 6 columns.
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