(10) You have been asked to analyse a suspected relationship between tutorial attendance and final marks f arks for a university course. The latest set of of data includes final marks y (between 0 and 100) and tutorial attendance X (between 0 and 12) for 65 students. You have done a regression analysis of lataset in R and the output is given below. along with a scatter plot of X and Y. Answer th questions regarding this analysis. 0 000 0 00 000 0 00 90 0 0 00 00 Final Mark O 10 12 Tutorials Attended Figure 3: Q3) Scatterplot of marks against tutorial attendance. Estimate Std. Error t value Pr(> |*1) (Intercept) 14.1517 4.6207 9.555 7.170-14 2.7914 0.5679 4.915 6.65e-06 Table 2: Q10) Regr sion summary. Df Sum Sq Mean Sq F-value p-value X 4393 1393 24.16 6.65e-06 Residuals 63 11455 182 Table 3: Q10) ANOVA table. Normal Q-Q -10 0 10 20 30 Standardized residuals 2 . 50 60 05 70 75 Theoretical Quantiles In(Y - x) Figure 4: Q10) QQ-plot. Figure 5: Q10) Residual plot. (a) What are the independent and dependent variables? (b) From the scatter plot, is there evidence that a linear regression would not be suitable for this dataset? (c) Use the regression summary to write an equation for the line of best fit. (d) At the a = 0.05 significance threshold, is there evidence that tutorial attendance has an impact on final mark? State the relevant null and alternative hypotheses. (e) If I attend 3 more tutorials, what impact do I expect this to have on my final mark? (f) Is there evidence of non-normality or unequal variance in the residuals? What does this lead you to say about the validity of the model? (g) The R' value for the model fit was 0.11. Write a sentence about what this represents with respect to the data