A linear regression model was fitted to estimate the response variable Roll using the predicting variable Unem. There are in total 29 observations. Here is the model summary, with some values missing: Coefficients: Estimate Std. Error t value Pr(>It|) (Intercept) 3957.0 4000.1 0. 989 0.3313 UNEM 1133.8 513.1 0.0358 * Signif. codes: 0 (*** 0.001 (**> 0.01 > 0.05 '.' 0.1 1 Residual standard error: 3049 on X degrees of freedom Multiple R-squared: 0.1531, Adjusted R-squared: 0.1218 F-statistic: 4.883 on 1 and 27 DF, p-value: 0.03579 Question 13 2 pts What are the degrees of freedom used to calculate the mean squared error (LaTex: MSE)? O 29 O 28 O 27 30 Question 14 2 pts What is the value of the mean squared error (LaTeX: MSE) for this model? O 9296401 O 3049 O 55.2177 O 112.9259 Question 15 2 pts What is the correlation coefficient between the response variable, Roll and the predictor, UNEM? O 0.3913 O 0.1531 O 0.02343 O -0.3913 Question 16 2 pts Using the model output above, what is the t-value of the coefficient for UNEM, labeled X? O 2.210 O 112.926 O 5.693 O 3.476 Question 17 2 pts For a significance level LaTeX: \\alpha=1\\%, what can you say about the statistical significance of the predictor UNEM? O The predictor is statistically significant. O The predictor is not statistically significant. O The result is inconclusive. Question 18 2 pts Based on the output, what can you say about this model? O A low portion of the variability of the response variable is explained by the predicting variable in this model. The model does a good job in explaining the variability of the response variable. O An increase of one point in UNEM causes, on average, a decrease of 1133.8 units in Roll. The predicting variable is not independent