SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. 1) Mary examined a random sample of Friday withdrawals from a college campus 1) ATM. She hypothesized that the mean was less than $100. The results shown below are from MegaStat. What would Mary conclude from this test? Explain carefully. Hypothesis Test: Mean vs. Hypothesized Value 100.000 hypothesized value 85.882 mean Withdrawal 85.078 std. dev. 20.634 std. error 17 n 16 df -0.68 t .2518 p-value (one-tailed, lower) 2) The table below compares two samples taken to compare the mean age of 2) individuals who purchased the iphone 3G at two AT&T store locations. Very briefly, what conclusions can you draw? t-test, pooled variance t-test, unequal variance Ann Arbor Livonia Ann Arbor Livonia 25.8171 31.2480 mean 25.8171 31.2480 mean 3.3891 1.8740 std. dev. 3.3891 1.8740 std. dev. 10 n 10 n 15 df 8 df -5.43086 difference (Ann Arbor - Livonia) -5.43086 difference (Ann Arbo 6.70157 pooled variance 1.41141 standard error of diff 2.58874 pooled std. dev. 0 hypothesized differe 1.27574 standard error of difference 0 hypothesized difference -3.85 t 0024 p-value (one-tailed, -4.26 t 0003 p-value (one-tailed, lower)3) The regression equation Salary = 45,000 + 1500 YearsExperience + 2800 YearsCollege 3) describes employee salaries at Terminus Fissile Labs. The standard error is 2500. Lars has 15 years' experience and 4 years of college. His salary is $70,500. If this regression is valid, we conclude that: A) Lars is overpaid, and his salary is an outlier. B) Lars is overpaid, but is not an outlier. C) Lars is underpaid, and his salary is an outlier. D) Lars is underpaid, but is not an outlier. SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. 4) Pedro became interested in vehicle fuel efficiency, so he performed a simple 4) regression using 93 cars to estimate the model CityMPG = Bo + B1 Weight, where Weight is the weight of the vehicle in pounds. His results are shown below. Write a brief analysis of these results, using what you have learned in this chapter. Is the intercept meaningful in this regression? Make a prediction of CityMPG when Weight = 3000 and also when Weight = 4000. Do these predictions seem believable? If you could make a car 1000 pounds lighter, what change would you predict in its CityMPG? Regression Analysis 12 0.711 n 93 r -0.843 K 1 Std. Error 3.038 Dep. Var. CityMPG ANOVA table Source SS df MS F p-value Regression 2,065.5191 2,065.5191 223.75 2.97E-26 Residual 840.0508 91 9.2313 Total 2,905.5699 92 Regression output confidence intervi variables coefficients std. error t (df=91) p-value 95% lower 95% up Intercept 47.0484 1.6799 28.006 1.63E-46 43.7114 50.3 Weight -0.0080 0.0005 -14.958 2.97E-26 -0.0091 -0.05) Bob analyzed the trend in the value of his investment portfolio over the last 16 5) quarters. (1) Translate each trend into plain English that a business person could understand. (2) Use each fitted trend to make a mathematical prediction for Bob's investment portfolio's value in period 20. (3) Which trend model seems most reasonable (or more than one)? What would Occam's Razor say? Value of Bob's Investment Portfolio 2500 y= 67.578x + 792.43 2000 R2 = 0.8488 1500 1000 500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Quarter Value of Bob's Investment Portfolio 2500 y = 882.87 - 0.048x 2000 R2 = 0.8815 1500 1000 500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Quarter\f