Replicating the Analysis: Load the dataset into R. Perform linear regression analysis with the following variables: Dependent Variable: Oil Usage Independent Variables: Degree Days, Number of People, Home Index Ensure you handle any missing values appropriately. Use the regression output to extract the coefficients, standard errors, t-values, p-values, and other relevant statistics. Interpreting the Results: a. Residuals: - Min, 1Q, Median, 3Q, Max: Describe the distribution of residuals, indicating the range and quartiles of the residuals. b. Coefficients: - Intercept, Degree Days, Number People, Home Index: Interpret the coefficients and their significance in predicting Oil Usage. Note any variables that are statistically significant (p < 0.05). c. Significance: - Explain the significance codes (**, ***, etc.) and their implications. Identify which variables are statistically significant predictors of Oil Usage. d. Residual Standard Error: - Define the residual standard error and its role in assessing the model's goodness of fit. e. Multiple R-squared and Adjusted R-squared: - Interpret these metrics as measures of how well the independent variables explain the variability in Oil Usage. Discuss whether the model is a good fit for the data. f. F-statistic and p-value: - Explain the F-statistic and its significance in assessing the overall significance