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Linear Regression in R
Review

Whew, that’s a wrap! You’ve covered a lot of material related to linear regression and its implementation in R. Here are the main concepts we’ve covered:

  • Statistical model building entails four main steps: 1) confirming data assumptions, 2) building a model on training data, 3) assessing model fit, and 4) analyzing model results.

  • We can use a combination of qualitative methods, such as box-plots, and quantitative methods, like the correlation coefficient, to assess that data meets our assumptions

  • We use the lm() method and Y ~ X notation to build a linear regression model. The Y variable is referred to as the outcome variable of the model, and any X variable is referred to as the predictor variable.

  • We can use a similar set of qualitative and quantitative methods to evaluate the fit of our model, including a comparison of the plotted model to a LOESS smoother and statistics like mean squared error (MSE) and R-squared.

  • MSE and R-squared statistics are summaries of the overall value of the model residuals. A residual is the difference between the value of a data point predicted by a model and its actual observed value.

  • The results of a linear regression model include regression coefficients. These coefficients represent the effect their respective predictor variable has on the model’s outcome variable.

  • In a simple linear regression, the regression coefficient represents the effect of a one-unit increase in the predictor variable.

  • The intercept coefficient represents the value of the outcome variable given that the predictor variable is equal to zero; this coefficient isn’t always meaningful and depends on the situation being modeled.

  • The p-value associated with a regression coefficient helps us understand whether the effect of a variable is statistically significant.

  • Multiple linear regression is similar to simple regression, except that it includes multiple predictor variables.

  • In a multiple linear regression, the regression coefficient represents the effect of a one-unit increase in the respective predictor variable, given that all other predictor variables are held constant.

  • In both simple and multiple linear regression, boolean categorical variables represent the total effect of switching from one category to another.

Instructions

We’ve included the advertising and conversion datasets in the workspace, along with some sample code that builds a multiple linear regression model.

Take some time to extend the model by adding more predictor variables; or, come up with a robust assessment of its current fit to the data and see if adjustments to the model improve the fit. How good of a fit can you achieve?

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