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Multiple Linear Regression in R (R Tutorial 5.3) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management
FAQs on Multiple Linear Regression in R (R Tutorial 5.3) Video Lecture - Mastering R Programming: For Data Science and Analytics - Database Management
| 1. What is multiple linear regression in R? |  |
Ans. Multiple linear regression in R is a statistical technique used to model the relationship between multiple independent variables and a dependent variable. It allows us to understand how these independent variables collectively influence the dependent variable. In R, it can be implemented using the lm() function.
| 2. How do you interpret the coefficients in multiple linear regression in R? | |
Ans. In multiple linear regression, the coefficients represent the change in the dependent variable for a one-unit change in the corresponding independent variable, while holding all other independent variables constant. For example, if the coefficient for a variable is 0.5, it means that a one-unit increase in that variable is associated with a 0.5 unit increase in the dependent variable, all else being equal.
| 3. How can we assess the overall model fit in multiple linear regression in R? | |
Ans. To assess the overall model fit in multiple linear regression in R, we can look at the R-squared value. R-squared measures the proportion of the variance in the dependent variable that is explained by the independent variables in the model. A higher R-squared value indicates a better fit of the model to the data.
| 4. What is multicollinearity in multiple linear regression and how can it be diagnosed in R? | |
Ans. Multicollinearity refers to the situation when independent variables in a multiple linear regression model are highly correlated with each other. It can cause problems in the interpretation of individual coefficients and lead to unstable estimates. In R, multicollinearity can be diagnosed using the variance inflation factor (VIF). VIF values greater than 5 or 10 indicate high multicollinearity.
| 5. Can categorical variables be included in multiple linear regression in R? | |
Ans. Yes, categorical variables can be included in multiple linear regression in R. However, they need to be converted into dummy variables or factors before including them in the model. Dummy variables represent different categories of the categorical variable as binary variables (0 or 1). Factors, on the other hand, represent the categories as levels within a single variable.
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Nov 24, 2024 Last updated |
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