A data scientist is preparing to build a predictive model and needs to validate a critical assumption for several linear regression techniques: the normality of the model's residuals. After fitting an initial model, the residuals have been extracted. Which of the following visualization methods is the most precise for graphically assessing whether the residuals conform to a normal distribution?
A Quantile-Quantile (Q-Q) plot is the most appropriate tool for this task. A Q-Q plot graphically compares the quantiles of a sample distribution (the residuals) with the quantiles of a theoretical distribution (normal). If the residuals are normally distributed, the points will fall roughly along a 45-degree reference line. Histograms and density plots display the overall shape of the distribution but provide only subjective visual cues of fit, especially with small samples. A box-and-whisker plot summarizes only five statistics, so it cannot show detailed agreement with the theoretical curve.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a residual in linear regression?
Open an interactive chat with Bash
How does a Q-Q plot test for normality?
Open an interactive chat with Bash
Why is a histogram less precise than a Q-Q plot for checking normality?