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Checking Linear Regression Assumptions in R (R Tutorial 5.2) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management
FAQs on Checking Linear Regression Assumptions in R (R Tutorial 5.2) Video Lecture - Mastering R Programming: For Data Science and Analytics - Database Management
| 1. What are the assumptions of linear regression? |  |
Ans. The assumptions of linear regression include linearity, independence, homoscedasticity, normality, and absence of multicollinearity. Linearity assumes that there is a linear relationship between the dependent variable and the independent variables. Independence assumes that the observations are independent of each other. Homoscedasticity assumes that the variance of errors is constant across all levels of the independent variables. Normality assumes that the errors follow a normal distribution. Absence of multicollinearity assumes that there is no high correlation between the independent variables.
| 2. How can I check the assumption of linearity in linear regression? | |
Ans. To check the assumption of linearity in linear regression, you can plot the observed values of the dependent variable against the predicted values. If the plot shows a straight line with no discernible pattern, then the assumption of linearity is satisfied. Additionally, you can also use diagnostic plots such as partial regression plots and component plus residual plots to assess linearity.
| 3. What is homoscedasticity in linear regression? | |
Ans. Homoscedasticity refers to the assumption in linear regression that the variance of the errors is constant across all levels of the independent variables. In other words, it assumes that the spread of the residuals is the same for all predicted values. Homoscedasticity is important because violating this assumption can lead to biased and inefficient estimates of the regression coefficients.
| 4. How can I check the assumption of normality in linear regression? | |
Ans. To check the assumption of normality in linear regression, you can examine the distribution of the residuals. You can use a histogram or a normal probability plot to assess whether the residuals follow a normal distribution. Additionally, you can also perform statistical tests such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test to formally test for normality.
| 5. What is multicollinearity in linear regression? | |
Ans. Multicollinearity refers to the presence of high correlation between independent variables in a linear regression model. It can cause issues in the interpretation of the regression coefficients. High multicollinearity can make it difficult to determine the individual effect of each independent variable on the dependent variable. It can also lead to unstable and unreliable estimates of the regression coefficients. To detect multicollinearity, one can calculate the variance inflation factor (VIF) for each independent variable, with values above 5 or 10 indicating a high level of multicollinearity.
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