![]() Related: Understanding Heteroscedasticity in Regression Analysis Diagnostic Plot #3: Normal Q-Q Plot We would likely declare that the assumption of equal variance is not violated in this case. In our example we can see that the red line isn’t exactly horizontal across the plot, but it doesn’t deviate too wildly at any point. If the red line is roughly horizontal across the plot, then the assumption of equal variance is likely met. This plot is used to check the assumption of equal variance (also called “homoscedasticity”) among the residuals in our regression model. This means there aren’t any overly influential points in our dataset. In our example we can see that observation #10 lies closest to the border of Cook’s distance, but it doesn’t fall outside of the dashed line. If any points in this plot fall outside of Cook’s distance (the dashed lines) then it is an influential observation. This plot is used to identify influential observations. We can use the plot() command to produce four diagnostic plots for this regression model: #produce diagnostic plots for regression modelĭiagnostic Plot #1: Residuals vs. Suppose we fit a simple linear regression model using ‘hours studied’ to predict ‘exam score’ for students in a certain class: #create data frameĭf <- data. Example: Create & Interpret Diagnostic Plots in R This tutorial explains how to create and interpret diagnostic plots for a given regression model in R. ![]() However, once we’ve fit a regression model it’s a good idea to also produce diagnostic plots to analyze the residuals of the model and make sure that a linear model is appropriate to use for the particular data we’re working with. Linear regression models are used to describe the relationship between one or more predictor variables and a response variable.
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