Drawing diagnostic plots

It's time for you to draw these diagnostic plots yourself. Let's go back to the Taiwan real estate dataset and the model of house prices versus number of convenience stores.

Recall that autoplot() lets you specify which diagnostic plots you are interested in.

1 residuals vs. fitted values
2 Q-Q plot
3 scale-location

mdl_price_vs_conv is available, and ggplot2 and ggfortify are loaded.

This exercise is part of the course

Introduction to Regression in R

Exercise instructions

Plot the three diagnostic plots (numbered 1 to 3) for mdl_price_vs_conv. Use a layout of three rows and one column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Plot the three diagnostics for mdl_price_vs_conv
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Edit and Run Code

This exercise is part of the course

Introduction to Regression in R

IntermediateSkill Level

4.8+

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You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

Exercise 1: A tale of two variables Exercise 2: Which one is the response variable?Exercise 3: Visualizing two variables Exercise 4: Fitting a linear regression Exercise 5: Estimate the intercept Exercise 6: Estimate the slope Exercise 7: Linear regression with lm()Exercise 8: Categorical explanatory variables Exercise 9: Visualizing numeric vs. categorical Exercise 10: Calculating means by category Exercise 11: lm() with a categorical explanatory variable

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Exercise 1: Making predictions Exercise 2: Predicting house prices Exercise 3: Visualizing predictions Exercise 4: The limits of prediction Exercise 5: Working with model objects Exercise 6: Extracting model elements Exercise 7: Manually predicting house prices Exercise 8: Using broom Exercise 9: Regression to the mean Exercise 10: Home run!Exercise 11: Plotting consecutive portfolio returns Exercise 12: Modeling consecutive returns Exercise 13: Transforming variables Exercise 14: Transforming the explanatory variable Exercise 15: Transforming the response variable too

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Exercise 1: Quantifying model fit Exercise 2: Coefficient of determination Exercise 3: Residual standard error Exercise 4: Visualizing model fit Exercise 5: Residuals vs. fitted values Exercise 6: Q-Q plot of residuals Exercise 7: Scale-location Exercise 8: Drawing diagnostic plots

Current Exercise

Exercise 9: Outliers, leverage, and influence Exercise 10: Leverage Exercise 11: Influence Exercise 12: Extracting leverage and influence

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Exercise 1: Why you need logistic regression Exercise 2: Exploring the explanatory variables Exercise 3: Visualizing linear and logistic models Exercise 4: Logistic regression with glm()Exercise 5: Predictions and odds ratios Exercise 6: Probabilities Exercise 7: Most likely outcome Exercise 8: Odds ratio Exercise 9: Log odds ratio Exercise 10: Quantifying logistic regression fit Exercise 11: Calculating the confusion matrix Exercise 12: Measuring logistic model performance Exercise 13: Accuracy, sensitivity, specificity Exercise 14: Congratulations