You wish to determine if there is a linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.05 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 33 21 23 21 21 34 72 26 78 32 17 32 27 30 Ho: p = 0 Ha: p = 0 Find the Linear Correlation Coefficient Find the p-value p-value = The p-value is O Greater than o O Less than (or equal to) o The p-value leads to a decision to O Do Not Reject Ho O Accept Ho O Reject Ho The conclusion is O There is a significant positive linear correlation between driver age and number of driver deaths. O There is a significant negative linear correlation between driver age and number of driver deaths. O There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. O There is a significant linear correlation between driver age and number of driver deaths.2 What is the relationship between the attendance at a major league ball game and the total number of runs scored? Attendance figures (in thousands) and the runs scored for 12 randomly selected games are shown ments below. Runs 19 13 18 12 34 49 15 29 48 28 9 a. Find the correlation coefficient: r = Round to 2 decimal places. . The null and alternative hypotheses for correlation are Ho: ? Y = H1 : 2 0 The p-value is: (Round to four decimal places) c. Use a level of significance of o = 0.05 to state the conclusion of the hypothesis test in the context of the study. O There is statistically significant evidence to conclude that a game with a higher attendance will have more runs scored than a game with lower attendance. O There is statistically significant evidence to conclude that a game with higher attendance will have fewer runs scored than a game with lower attendance. O There is statistically insignificant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the use of the regression line is not appropriate. There is statistically significant evidence to conclude that there is a correlation between the attendance of baseball games and the runs scored. Thus, the regression line is useful. (Round to two decimal places) (Round to two decimal places) e. Interpret r : There is a 63% chance that the regression line will be a good predictor for the runs scored based on the attendance of the game. O 63% of all games will have the average number of runs scored. O Given any fixed attendance, 63% of all of those games will have the predicted number of runs scored. There is a large variation in the runs scored in baseball games, but if you only look at games with a fixed attendance, this variation on average is reduced by 63%. f. The equation of the linear regression line is: y = I (Please show your answers to two decimal places) g. Use the model to predict the runs scored at a game that has an attendance of 32,000 people. Runs scored = (Please round your answer to the nearest whole number.) h. Interpret the slope of the regression line in the context of the question: As x goes up, y goes up. For every additional thousand people who attend a game, there tends to be an average increase of 0.20 runs scored. O The slope has no practical meaning since the total number runs scored in a game must be positive. i. Interpret the y-intercept in the context of the question: O The best prediction for a game with 0 attendance is that there will be 1 runs scored. The y-intercept has no practical meaning for this study. The average runs scored is predicted to be 1. O If the attendance of a baseball game is 0, then 1 runs will be scored