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Energy drink consumption has continued to gain in popularity since the 1997 debut of Red Bull, the current leader in the energy drink market. Given below are the exam scores and the number of 12-grams energy drinks consumed by 10 college students within a week prior to the exam. mm Number of Drinks 5 3 2 4 2 7 3 1 1. What is the sample covariance between the exam scores and the number of energy drinks consumed? (4 marks) 2. What is the sample correlation coefficient between the exam scores and the number of energy drinks consumed? (4 marks) 3. How will you classify the linear relationship between the exam scores and the number of energy drinks consumed? (4 marks) 4. You will expect a decrease in the amount of energy drink consumed within the week prior to the exam to be associated with [Blank]. (4 marks) 5. Following this statistical analysis, can you conclude that an increase in the amount of energy drink consumed within the week prior to the exam causes an increase in exam scores? Why? (9 marks) [Answer word limit 200 words] It is believed that the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Microsoft Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown. Re . ression statistics Adjusted R Square Standard Error Observations ANOVA Significance F Regression 1 335. 0472 335.04 73 178. 3859 Residual 1. 8782 Total 50 42 7. 0798 Coefcients Standard t Stat p-value Lower 95% Upper Error 95% Intercept -1.8940 0.4018 4.7134 2. 051 E05 2. 7015 -1.0865 Hours 0.9795 0.0733 13.3561 5. 944E18 0.8321 1.1269 Note: 2.051E-05 = 2.051 * 10-0-5 and 5.944E-18 = 5.944 * 10-18. 1. The estimated average change in salary (in thousands of dollars) as a result of spending an extra hour per day studying is: (5 marks) A. 0.9795. B. 0.7845. C. 335.0473. D. 4.8940. 2. The error sum of squares (SSE) of the above regression is: (5 marks) A. 427.079804. B. 92.0325465. C. 1.878215. D. 335.047257. The managers of a brokerage firm are interested in finding out if the number of new customers a broker brings into the rm affects the sales generated by the broker. They sample 12 brokers and determine the number of new customers they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. Broker Clients Sales 1 27 52 2 1 'l 37 3 42 64 4 33 55 5 15 29 6 15 34 7 25 58 8 36 59 9 28 44 1 0 30 48 1 1 1 7 3 1 12 22 38 1. The prediction for the amount of sales (in $1 ,0003) for a person who brings 25 new customers into the rm is [Blank]. (5 marks) 2. Set up a scatter diagram. (5 marks) 3. The coefcient of correlation is [Blank]