Before we discuss the diagnostic plot one by one let’s discuss some important terms: This diagnostic can be used to check whether the assumptions. The above plots can be used to validate and test the above assumptions are part of Regression Diagnostic. The presence of homoscedasticity can be estimated with the plots such as the Scale Location plot, and the Residual vs Legacy plot.We can check for the autocorrelation plot. The presence of correlation between observations is known as autocorrelation.To check for the normality in the dataset, draw a Q-Q plot on the data.If the data contain non-linear trends then it will not be properly fitted by linear regression resulting in a high residual or error rate.To perform a good linear regression analysis, we also need to check whether these assumptions are violated: The data is in homoscedasticity, which means the variance of the residual is the same for each value of the dependent variable.The relationship b/w the independent variable and the mean of the dependent variable is linear.It should be correlated to another observation. Observations are independent of each other.Software Engineering Interview Questions.Top 10 System Design Interview Questions and Answers.Top 20 Puzzles Commonly Asked During SDE Interviews.Commonly Asked Data Structure Interview Questions.Top 10 algorithms in Interview Questions.Top 20 Dynamic Programming Interview Questions.Top 20 Hashing Technique based Interview Questions.Top 50 Dynamic Programming (DP) Problems.Top 20 Greedy Algorithms Interview Questions.Top 100 DSA Interview Questions Topic-wise.
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