1. If extreme values occur in the data set, which of the following measures of central tendency are the most appropriate A. B. C. D. E. 2. The The The The The mean median mode variance standard deviation Which measure of central tendency is most affected by extreme values? A. B. C. D. E. The mean The median The mode The standard deviation All are equally affected 3. The most frequently occurring data value in a data set is the A. B. C. D. E. 4. A measure of central location which splits the data set into two equal groups is called the A. B. C. D. E. 5. Median Arithmetic mean Population parameter Range Mode mean mode median standard deviation none of the above The sum of the deviation of the individual data elements from their mean is always A. B. C. D. E. equal to zero equal to one negative positive none of the above 6.The mean is a measure of: A. B. C. D. E. variability. position. skewness. central tendency. symmetry. 7.Suppose the daily sales of a small corner shop are as follows: Daily sales: Monday: $100, Tuesday: $60, Wednesday: $75, Thursday: $90, Friday: $50. Please choose the correct answer: A. B. C. D. E. mean =75, median = 75, mode mean =75, median = 75, mode mean =75, median = 75, mode mean =75, median = 75, mode None of the previous answers = = = = 75 50 does not exist 100 8.Suppose the daily sales of a small corner shop are as follows: Daily sales: Monday: $100, Tuesday: $60, Wednesday: $75, Thursday: $90, Friday: $50. The range of the data is? A. B. C. D. E. 100 200 50 375 None of the previous answers 9.Which of the following statements is TRUE regarding the standard deviation A. B. C. D. E. It cannot assume a negative value If it is zero, then all the data values are the same it is in the same units as the mean All of the answers (A,B,C) are correct Only A and B are true 10. The following statistics were collected on two groups of shipments Group A Group B sample size sample mean sample std. dev 45 100 lbs 8 lbs 30 80 lbs 7 lbs Which of the following statements is correct? A. Group A is less variable than Group B because Group A's standard deviation is larger B. Group A is relatively less variable than Group B because Group A??s coefficient of variation is smaller C. Group A is less variable than Group B because the std deviation per animal is smaller D. Group A is relatively more variable than Group B because the sample mean is larger E. Group A is more variable than Group B because the sample size is larger 11. Suppose the daily sales of a small corner shop are as follows. Daily sales: Monday: $200, Tuesday: $120, Wednesday: $150, Thursday: $180, Friday: $100. Please choose the correct answer: A. B. C. D. E. mean =150, median = 150, mode mean =150, median = 150, mode mean =150, median = 150, mode mean =150, median = 150, mode None of the previous answers = = = = 150 100 does not exist 200 12. Suppose the daily sales of a small corner shop are as follows. Daily sales: Monday: $200, Tuesday: $120, Wednesday: $150, Thursday: $180, Friday: $100. The range of the data is? A. B. C. D. E. 13. 100 200 150 180 None of the previous answers Which of the following is a quantitative variable? A. B. C. D. E. The manufacturer of a cell phone A person's gender Price of a car Whether a person is a college graduate Whether a person has a charge account 14. When developing a frequency distribution, the class (group) intervals should be ___________. A. Large B. C. D. E. Small Integer Mutually exclusive Equal 15. If the mean, median, and mode for a given population all equal 25, then we know that the shape of the distribution of the population is ____________. A. B. C. D. E. Bimodal Skewed to the right Symmetrical Skewed to the left there is not enough information to answer this question 16. A method of assigning probabilities which assumes that the experimental outcomes are equally likely is referred to as the A. B. C. D. objective method classical method subjective method none of the above 17. If two events are mutually exclusive, then their intersection probability A. B. C. D. 18. will always be zero can have any value larger than zero must be larger than zero, but less than one none of the choices are correct The union of events A and B is A. the same as the intersection of events A and B B. always equal to zero C. that event which contains all sample points belonging to A and B D. that event which contains all sample points belonging to A or B or both 19. If the outcome of event A is not affected by event B, then events A and B are said to be A. B. C. D. mutually exclusive. statistically independent. collectively exhaustive. None of the above 20. The automobile industry sold 657,000 vehicles in the United States during January 2009 (The Wall Street Journal, Feb 2009). The Big Three U.S. automakers - GM, Ford and Chrysler sold 280,000 vehicles (87,000 personal cars and 193,000 light trucks). The nonU.S. manufacturers are led by Toyota, Honda and Nissan sold 377,000 vehicles (229,000 personal cars and 148,000 light trucks). The category light truck includes pickup, minivan, SUV and crossover models. What is the probability that the sold vehicle was a personal car produced in the U.S.? A. B. C. D. E. 42.61% 27.53% 68.93% 90.01% 13.24% 21. The automobile industry sold 657,000 vehicles in the United States during January 2009 (The Wall Street Journal, Feb 2009). The Big Three U.S. automakers - GM, Ford and Chrysler sold 280,000 vehicles (87,000 personal cars and 193,000 light trucks). The non-U.S. manufacturers are led by Toyota, Honda and Nissan sold 377,000 vehicles (229,000 personal cars and 148,000 light trucks). The category light truck includes pickup, minivan, SUV and crossover models. Given that vehicle was NOT manufactured by one of the U.S. automakers, what is the probability that the vehicle sold was a light truck? A. B. C. D. E. 60.74% 54.74% 43.40% 25.87% 39.25% 22. If two events are mutually exclusive, then the probability of intersection A. B. C. D. E. Will always be zero Can have any value larger than zero Must be larger than zero but less than one Will be the sum of the probability of these two events None of the answers is correct 23. Which of the following best expresses the addition rule of probabilities? A. B. C. D. E. P(A or B) = P(A) + P(B) P(A or B) = P(A) + P(B) + P(A B) P(A or B) = P(A) + P(B) - P(A B) P(A or B) = P(A)* P(B) None of the answers is correct 24. The automobile industry sold 597,000 vehicles in the United States during June 2009 (The Wall Street Journal, Aug 2009). The Big Three U.S. automakers - GM, Ford and Chrysler sold 270,000 vehicles (107,000 personal cars and 163,000 light trucks). The nonU.S. manufacturers are led by Toyota, Honda and Nissan sold 327,000 vehicles (179,000 personal cars and 148,000 light trucks). The category light truck includes pickup, minivan, SUV and crossover models. What is the probability that the sold vehicle was a light truck produced in the U.S.? A. B. C. D. E. 27.30% 35.45% 68.93% 90.01% 12.12% 25. The automobile industry sold 597,000 vehicles in the United States during June 2009 (The Wall Street Journal, Aug 2009). The Big Three U.S. automakers - GM, Ford and Chrysler sold 270,000 vehicles (107,000 personal cars and 163,000 light trucks). The nonU.S. manufacturers are led by Toyota, Honda and Nissan sold 327,000 vehicles (179,000 personal cars and 148,000 light trucks). The category light truck includes pickup, minivan, SUV and crossover models. Given that vehicle was NOT manufactured by one of the U.S. automakers, what is the probability that the vehicle sold was a personal car? A. B. C. D. E. 60.74% 54.74% 10.67% 25.87% 45.23% 26. In a sample of 1,000 representing a survey from the entire population, 650 people were from Laketown, and the remaining 350 from River City. 19 people have some form of cancer. 13 of them were from Lake Town. What is the probability that the randomly selected person was from River City and had some form of cancer? A. B. C. D. E. 0.6%. 1.45%. 19.12%. 30.01%. 12.12%. 27. In a sample of 1,000 representing a survey from the entire population, 650 people were from Laketown, and the remaining 350 from River City. 19 people have some form of cancer. 13 of them were from Lake Town. Given that the person is from Laketown, what is the probability that the chosen person has cancer? A. B. C. D. E. 10.74% 30.34% 18.67% 25.87% 2.00% 28. The set of all possible experimental outcomes is called a(n) ____________. A. B. C. D. E. Sample space Event Experiment Probability Random Variable 29. A(n) ____________ is the probability that one event will occur given that we know that another event already has occurred. A. B. C. D. E. Sample space outcome Subjective probability Complement of events Long-run relative frequency Conditional probability 30. The _____________ of an event is a number that measures the likelihood that an event will occur when an experiment is carried out. A. B. C. D. E. Outcome Probability Intersection Observation The sample space 31. The probability distribution for a discrete random variable which is used to compute the probability of x successes in n trials is known as the A. normal probability distribution B. standard normal distribution C. binomial probability distribution D. none of the above 32. Which of the following is NOT a true statement about the binomial probability distribution? A. B. C. D. E. The random variable of interest is continuous Each outcome is independent of each other Each outcome can be classified as either success or failure The probability of success must be constant from trial to trial The probability of success is known 33. A Harris Interactive survey for InterContinental Hotels & Resorts asked respondents, \"When traveling internationally, do you generally venture out on your own to experience culture, or stick with your tour group and itineraries?\" The survey found that 23% of the respondents stick with their tour group (USA Today, January 21, 2004). In a sample of six international travelers, what is the probability that two will stick with their tour group? A. B. C. D. E. 37.35% 11.10% 27.89% 10.67% 2.48% 34. A candidate for the public office has claimed that 60% of the voters will vote for her. If 5 registered voters were sampled, what is the probability that exactly 3 would vote her? A. B. C. D. E. 34.56% 50% 60% 10.67% 29.65% 35. Suppose that a firm producing truck tires wants to control the quality of the tires produced, and that tires that are of bad quality cannot be sold. The probability that a tire coming out of the production chain is of bad quality is 15%. What is the probability of having zero bad tires, if we pick five? A. 44.37% B. 77.94% C. 32.06% D. 0.02% E. None of the answers (A,B,C,D) is correct. 36. Suppose that a firm producing truck tires wants to control the quality of the tires produced, and that tires that are of bad quality cannot be sold. The probability that a tire coming out of the production chain is of bad quality is 15%. What is the probability of having one or less bad tires, if we pick six? A. B. C. D. E. 77.64% 27.94% 63.12% 12.45% None of the answers (A,B,C,D) is correct. 37. A candidate for Mayor has claimed that 55% of the voter will vote for her. If 5 registered voters were sampled, what is the probability that exactly 3 would vote for her? A. B. C. D. E. 55% 60% 33.7% 0.03% None of the previous answers 38. According to a recent Time/CNN poll of U.S. adults, compared with their parents, 70 percent are better off financially. If we select 8 adults, what is the likelihood exactly 4 are better off financially than their parents? A. B. C. D. E. 21% 50% 10.67% 13.61% None of the previous answers 39. The probability that a given computer chip will fail is 0.03. Find the probability that of 5 delivered chips, exactly 2 will fail. A.0.9039 B.0.0922 C.0.0038 D. 0.0082 E.0.015 40. The probability that a given computer chip will fail is 0.05. Find the probability that of 5 delivered chips, exactly 0 will fail. A.0.9039 B.0.7737 C.0.0038 D. 0.0082 E.0.015 41. In the last election, 67% of people voted. What is the probability that in the group of 10 people 6 will vote in the next elections? A.0.9039 B.0.2252 C.0.0038 D. 0.0082 E.0.015 42. A study conducted by a local university found that 25 percent of college freshmen support increased spending on environmental issues. If 6 college freshmen are randomly selected, find the probability that fewer than 5 support increased spending on environmental issues. A.0.0330 B.0.7844 C.0.9624 D. 0.9953 E.0.2563 43. A study conducted by a local university found that 25 percent of college freshmen support increased spending on environmental issues. If 6 college freshmen are randomly selected, find the probability that 3 support increased spending on environmental issues. A.0.0330 B.0.7844 C.0.9624 D. 0.1318 E.0.2563 44. The Asia Company buys mobile phones from a Chinese supplier. The probability of a defective phone is 10 percent. If 3 phones are selected at random, what is the probability that one of the mobile phones will be defective? A.0.0702 B.0.0010 C.0.2430 D. 0.7290 E.0.1256 45. The Asia Company buys mobile phones from a Chinese supplier. The probability of a defective phone is 10 percent. If 3 phones are selected at random, what is the probability that all 3 mobile phones will be good quality? A.0.0702 B.0.0010 C.0.2430 D. 0.7290 E.0.1256 46. A standard normal distribution is a normal distribution A. B. C. D. E. with a mean of 1 and a standard deviation of 0 with any mean and any standard deviation with a mean of 0 and any standard deviation with a mean of 0 and a standard deviation of 1 none of the above 47. A normal probability distribution A. B. C. D. E. is a discrete probability distribution is a continuous probability distribution can be either continuous or discrete must always have a mean of zero none of the above 48. Which of the following statements is correct regarding the standard normal distribution? A. It is also called the Z distribution B. Any normal distribution can be converted to the standard normal distribution C. The mean is 0 and the standard deviation is 1 D. All of the previous answers are true E. None of the answers (A,B,C) is true 49. Is lack of sleep causing traffic fatalities? A study conducted under the auspices of the National Highway Traffic Safety Administration found that the average number of fatal crashes caused by sleepy drivers each year was 1550 (BusinessWeek, January 26, 2004). Assume the annual number of fatal crashes per year is normally distributed with a standard deviation of 300.What is the probability of fewer than 1000 fatal crashes in a year? A. 3.36% B. 96.64% C. 15.87% D. 3.34% E. 61.45% 50. Is lack of sleep causing traffic fatalities? A study conducted under the auspices of the National Highway Traffic Safety Administration found that the average number of fatal crashes caused by sleepy drivers each year was 1550 (BusinessWeek, January 26, 2004). Assume the annual number of fatal crashes per year is normally distributed with a standard deviation of 300. What is the probability that a number of fatal crashes will be between 1000 and 2000 for a year? A. B. C. D. E. 93.32% 3.36% 89.96% 25.89% 34.13% 51. A normal probability distribution A. B. C. D. E. is a discrete probability distribution is a continuous probability distribution can be either continuous or discrete must always have a mean of zero None of the answers is correct 52. In an article about the cost of health care, Money magazine reported that a visit to a hospital emergency room for something as simple as a sore throat has a mean cost of $400 (Money, Jan 2009). Assume that the cost for this type of hospital emergency room visit is normally distributed with a standard deviation of $100. What is the probability than the cost will be higher than $500? A. B. C. D. E. 91.22% 22.78% 15.87% 3.34% 61.45% 53. In an article about the cost of health care, Money magazine reported that a visit to a hospital emergency room for something as simple as a sore throat has a mean cost of $400 (Money, Jan 2009). Assume that the cost for this type of hospital emergency room visit is normally distributed with a standard deviation of $100. What is the probability than the cost will be between $300 and $400? A. 13.61% B. C. D. E. 18.45% 7.23% 25.89% 34.13% 54. According to the Sleep Foundation, the average night's sleep is 6.8 hours (Fortune, March 20, 2006). Assume the standard deviation is 0.6 hours and that the probability distribution is normal. What is the probability that a randomly selected person sleeps more than 8 hours? A.51.22% B.32.78% C. 15.87% D. 3.34% E.2.28% 55. According to the Sleep Foundation, the average night's sleep is 6.8 hours (Fortune, March 20, 2006). Assume the standard deviation is 0.6 hours and that the probability distribution is normal. What is the probability that a randomly selected person sleeps 6 hours or less? A.9.12% B.30.85% C.53.12% D. 14.45% E.80.01% 56. According to the Sleep Foundation, the average night's sleep is 6.8 hours (Fortune, March 20, 2006). Assume the standard deviation is 0.6 hours and that the probability distribution is normal. Doctors suggest getting between 7 and 8 hours of sleep each night. What percentage of the population gets this much sleep? A.13.61% B.18.45% C.17.23% D. 34.66% E.44.13% 57. A certain jogger runs over one hour per day and burns an average of 500 calories with a standard deviation of 10. Assume that calories burned follow a normal distribution. What is the probability than the calories burned will be higher than 505? A. 91.22% B. 30.85% C. 15.87% D. 3.34% E. 61.45% 58. A certain jogger runs over one hour per day and burns an average of 500 calories with a standard deviation of 10. Assume that calories burned follow a normal distribution. What is the probability than the calories burned will be lower than 490? A. B. C. D. E. 7.02% 30.85% 63.12% 12.45% 15.87% 59. In an article about the cost of health care, Money magazine reported that a visit to a hospital emergency room for something as simple as a sore throat has a mean cost of $400 (Money, Jan 2009). Assume that the cost for this type of hospital emergency room visit is normally distributed with a standard deviation of $100. What is the probability than the cost will be higher than $500? A.91.22% B.22.78% C.15.87% D. 3.34% E.61.45% 60. In an article about the cost of health care, Money magazine reported that a visit to a hospital emergency room for something as simple as a sore throat has a mean cost of $400 (Money, Jan 2009). Assume that the cost for this type of hospital emergency room visit is normally distributed with a standard deviation of $100. What is the probability than the cost will be lower than $350? A.7.02% B.30.85% C.63.12% D. 12.45% E.90.01% 61. In regression analysis, the variable that is being predicted is A. the independent variable B. the dependent variable C. usually denoted by x D. none of the above 62. The value of the coefficient of determination ranges between A. B. -1 to +1 -1 to 0 C. D. 63. 1 to infinity 0 to +1 A multiple regression model has A. B. C. D. only one independent variable more than one dependent variable more than one independent variable none of the above 64. If two variables, x and y, have a very strong linear relationship, then A. B. C. D. E. there is evidence that x causes a change in y there is evidence that y causes a change in x there might not be any causal relationship between x and y there must be a causal relationship between x and y None of these alternatives is correct 65. ButlerTrucking Company, an independent trucking company in southern California. A major portion of Butler's business involves deliveries throughout its local area. To develop better work schedules, the managers want to estimate the total daily travel time for their drivers. They relate the total time travelled (Y in hours) to miles travelled (X1 in miles) and number of deliveries (X2). (Question 21 and 22) Time = -0.869 + 0.0611*X1 + 0.923*X2. The interpretation of the sample estimate for the number of deliveries is A. The expected increase in travel time corresponding to one additional delivery is 0.923 hours B. The expected increase in travel time corresponding to an increase of one delivery when the number of miles traveled is held constant is 0.923 hours C. The expected increase in travel time corresponding to an increase of one delivery when the number of miles travelled is held constant is 0.0611 hours D. The expected increase in travel time corresponding to an increase of one delivery is 0.0611 hours E. None of the previous answers 6. ButlerTrucking Company, an independent trucking company in southern California. A major portion of Butler's business involves deliveries throughout its local area. To develop better work schedules, the managers want to estimate the total daily travel time for their drivers. They relate the total time travelled (Y in hours) to miles travelled (X1 in miles) and number of deliveries (X2). (Question 21 and 22) Time = -0.869 + 0.0611*X1 + 0.923*X2. Estimate the travel time for 90 miles travelled and 2 deliveries A. Predicted time is 1.8 hours. B. Predicted time is 9.3 hours. C. You cannot calculate the predicted value since it is only a speculation D. Predicted time is 6.5 E. Predicted time is 5.5 67. You are analyzing how an athlete's performance in a one kilometer run is affected by such factors as age, height, and weight. The dependent variable, in this case is the amount of time it takes an individual to run a kilometer. The independent variables are the variables that influence an athlete's speed in this example are age (in years), height (in cm) and weight (in kg). You have 10 observations. The output of the regression shows the table below: SUMMARY OUTPUT Regression Statistics Multiple R 0,997084531 R Square 0,994177562 Adjusted R Square 0,991266343 Standard Error 1,50677227 Observations 10 ANOVA df Regression Residual Total Intercept Age 3 6 9 SS MS F Significance F 2325,977824 775,3259413 341,4987175 4,30836E-07 13,62217604 2,270362674 2339,6 Coefficients Standard Error t Stat P-value -51,13841379 17,60066456 -2,904563782 0,011232747 0,836742791 0,100501622 8,32566451 0,000162949 Height Weight 0,179274099 0,937466055 0,103805847 1,727013498 0,134910603 0,346549023 2,705147017 0,035333297 How can we assess the quality of the model? A. Poor quality since we have only 10 observations used B. Standard error is 1.50, low so we have a good model C. The model is good since it explains 99% of the variation in the dependent variable D. The model is good since it explains 99% of the variation in the independent variables E. There is no sufficient data to assess the quality of the model 68. You are analyzing how an athlete's performance in a one kilometer run is affected by such factors as age, height, and weight. The dependent variable, in this case is the amount of time it takes an individual to run a kilometer. The independent variables are the variables that influence an athlete's speed in this example are age (in years), height (in cm) and weight (in kg). You have 10 observations. The output of the regression shows the table below: SUMMARY OUTPUT Regression Statistics Multiple R 0,997084531 R Square 0,994177562 Adjusted R Square 0,991266343 Standard Error 1,50677227 Observations 10 ANOVA df Regression Residual Total Intercept Age Height Weight 3 6 9 SS MS F Significance F 2325,977824 775,3259413 341,4987175 4,30836E-07 13,62217604 2,270362674 2339,6 Coefficients Standard Error t Stat P-value -51,13841379 17,60066456 -2,904563782 0,011232747 0,836742791 0,100501622 8,32566451 0,000162949 0,179274099 0,103805847 1,727013498 0,134910603 0,937466055 0,346549023 2,705147017 0,035333297 Which statement is true (assuming 5% level of significance) A. Height is not statistically significant since p-value is larger than 5% B. Height is statistically significant C. Height is statistically significant since the standard error is small D. Height is not statistically significant because the estimated coefficient is very low E. None of them is true 69. You are analyzing how an athlete's performance in a one kilometer run is affected by such factors as age, height, and weight. The dependent variable, in this case is the amount of time it takes an individual to run a kilometer. The independent variables are the variables that influence an athlete's speed in this example are age (in years), height (in cm) and weight (in kg). You have 10 observations. The output of the regression shows the table below: SUMMARY OUTPUT Regression Statistics Multiple R 0,997084531 R Square 0,994177562 Adjusted R Square 0,991266343 Standard Error 1,50677227 Observations 10 ANOVA df Regression Residual Total Intercept Age Height Weight 3 6 9 SS MS F Significance F 2325,977824 775,3259413 341,4987175 4,30836E-07 13,62217604 2,270362674 2339,6 Coefficients Standard Error t Stat P-value -51,13841379 17,60066456 -2,904563782 0,011232747 0,836742791 0,100501622 8,32566451 0,000162949 0,179274099 0,103805847 1,727013498 0,134910603 0,937466055 0,346549023 2,705147017 0,035333297 Which variable/s would you remove (if recommended) in the final model (assuming 5% level of significance)? A. B. C. D. E. Age Weight Height Intercept All 70. A shoe store developed the following estimated regression equation relating sales to inventory investment (X1) and advertising expenditures (X2): Sales = 25 + 10*X1 + 8*X2 The interpretation of the sample estimate for inventory investment is: A. As the inventory investment increases by $1 so sales increase by $8 B. As the inventory investment increases by the $1 so sales increase by $10 C. As the inventory investment increases by the $1 so sales increase by $10 keeping the advertising expenditure constant D. As the inventory investment increases by the $1 so sales increase by $8 keeping the advertising expenditure constant E. None of the above 71. A shoe store developed the following estimated regression equation relating sales to inventory investment (X1) and advertising expenditures (X2): Sales = 25 + 10*X1 + 8*X2 Estimate sales resulting from a $15 investment in inventory and an advertising budget of $10. A. Predicted Sales are $100 B. Predicted Sales are $255 C. You cannot calculate the predicted value since it is only a speculation D. Predicted Sales are $245 E. Predicted Sales are $25 72. A micro economist wants to determine how corporate sales are influenced by capital and wage spending by companies. She proceeds to randomly select 25 large corporations and record information in millions of dollars. The output from the multiple regression: How can we assess the quality of the model? A. Standard error is 17502, very high so the model is not good. B. The model is good since it explains 69% of the variation in the dependent variable C. The model is OK since it explains 69% of the variation in the independent variables D. Poor quality since we have only 25 observations used E. There is no sufficient data to assess the quality of the model. 73. A micro economist wants to determine how corporate sales are influenced by capital and wage spending by companies. She proceeds to randomly select 25 large corporations and record information in millions of dollars. The output from the multiple regression: Which statement is true (assuming 5% level of significance) A. Capital is statistically significant since p-value is less than 5% B. Capital is not statistically significant since p-value is larger than 5% C. Capital is statistically significant since the standard error is small D. Capital is not statistically significant because the estimated coefficient is very low E. None of these alternatives is correct 74. A micro economist wants to determine how corporate sales are influenced by capital and wage spending by companies. She proceeds to randomly select 25 large corporations and record information in millions of dollars. The output from the multiple regression: Which variable/s would you remove (if recommended) in the final model? A. B. C. D. E. Capital Intercept Wage All None 75. A shoe store developed the following estimated regression equation relating sales to inventory investment (X1) and advertising expenditures (X2) Sales = 25 + 6*X1 + 4*X2. The interpretation of the sample estimate for inventory investment is: A. As the inventory investment increases by $1 so sales increase by $31 B. As the inventory investment increases by the $1 so sales increase by $4 C. As the inventory investment increases by the $1 so sales increase by $6, keeping the advertising expenditure constant D. As the inventory investment increases by the $1 so sales increase by $35 E. None of the above 76. An economist wants to determine how the number of crimes in a given neighborhood depends on the number of policemen in the neighborhood, the average income of households living in the neighborhood, and the number of hours that people spend watching tv. He selects 15 neighborhoods in London and measures (1) the number of crimes per 10,000 residents; (2) the number of policemen per 10,000 residents; (3) the average income per capita (in thousands of euro); (4) the average number of hours spent watching TV per day. The output from the multiple regression is reported in the table below. Linear Regression Regression Statistics R R Square Adjusted R Square S Total number of observations 0,97804 0,95656 0,94472 24,35451 15 ANOVA d.f. Regression Residual Total SS 143.679,83695 6.524,56305 150.204,4 MS 47.893,27898 593,1421 Coefficients Standard Error 524,57551 46,23553 -21,86669 5,542 -1,07721 1,07874 -0,55872 1,3919 LCL 398,90367 -36,93028 -4,00931 -4,34201 3, 11, 14, Intercept Police Income TV F 80,74503 p-level 0,0000 UCL 650,24735 -6,80311 1,85489 3,22456 t Stat 11,34572 -3,94563 -0,99858 -0,40141 p-level 0,0000 0,00229 0,33946 0,6958 ! How can we assess the quality of the model? A. Two coefficients are not significant, so the model is not good. B. The model is good since it explains about 95% of the variation in the dependent variable. C. The model is bad since it cannot explain 94.4% of the variation in the dependent variable. D. We do not have information to assess the quality of the model E. None of the previous answers is correct. 77. An economist wants to determine how the number of crimes in a given neighborhood depends on the number of policemen in the neighborhood, the average income of households living in the neighborhood, and the number of hours that people spend watching tv. He selects 15 neighborhoods in London and measures (1) the number of crimes per 10,000 residents; (2) the number of policemen per 10,000 residents; (3) the average income per capita (in thousands of euro); (4) the average number of hours spent watching TV per day. The output from the multiple regression is reported in the table below. Linear Regression Regression Statistics R R Square Adjusted R Square S Total number of observations 0,97804 0,95656 0,94472 24,35451 15 ANOVA d.f. Regression Residual Total SS 143.679,83695 6.524,56305 150.204,4 MS 47.893,27898 593,1421 Coefficients Standard Error 524,57551 46,23553 -21,86669 5,542 -1,07721 1,07874 -0,55872 1,3919 LCL 398,90367 -36,93028 -4,00931 -4,34201 3, 11, 14, Intercept Police Income TV F 80,74503 p-level 0,0000 UCL 650,24735 -6,80311 1,85489 3,22456 t Stat 11,34572 -3,94563 -0,99858 -0,40141 p-level 0,0000 0,00229 0,33946 0,6958 ! The regression line is: A. B. C. D. Crime= 524.58 + 21.87 * Police + 1.08*Income + 0.56*TV Crime= 21.87 + Police + 1.08 +Income + 0.56 + TV Crime = 524.58 - 21.87 * Police - 1.08*Income - 0.56*TV Crime = 524.58 *(21.87 * Police - 1.08*Income - 0.56*TV) + error E. None of the previous answers is correct. 78. An economist wants to determine how the number of crimes in a given neighborhood depends on the number of policemen in the neighborhood, the average income of households living in the neighborhood, and the number of hours that people spend watching tv. He selects 15 neighborhoods in London and measures (1) the number of crimes per 10,000 residents; (2) the number of policemen per 10,000 residents; (3) the average income per capita (in thousands euro); (4) the average number of hours spent watching TV per day. The output from the multiple regression is reported in the table below. Linear Regression Regression Statistics R R Square Adjusted R Square S Total number of observations 0,97804 0,95656 0,94472 24,35451 15 ANOVA d.f. Regression Residual Total SS 143.679,83695 6.524,56305 150.204,4 MS 47.893,27898 593,1421 Coefficients Standard Error 524,57551 46,23553 -21,86669 5,542 -1,07721 1,07874 -0,55872 1,3919 LCL 398,90367 -36,93028 -4,00931 -4,34201 3, 11, 14, Intercept Police Income TV F 80,74503 p-level 0,0000 UCL 650,24735 -6,80311 1,85489 3,22456 t Stat 11,34572 -3,94563 -0,99858 -0,40141 ! Which variable(s) would you drop from the regression? A. B. C. D. E. Income TV Income and TV All None of the previous is correct. 79. An economist wants to determine how the number of crimes in a given neighborhood depends on the number of policemen in the neighborhood, the average income of households living in the neighborhood, and the number of hours that people spend watching tv. He selects 15 neighborhoods in London and measures (1) the number of crimes per 10,000 residents; (2) the number of policemen per 10,000 residents; (3) the average income per capita (in thousands of euro); (4) the average number of hours spent watching TV per day. The output from the multiple regression is reported in the table below. p-level 0,0000 0,00229 0,33946 0,6958 Linear Regression Regression Statistics R R Square Adjusted R Square S Total number of observations 0,97804 0,95656 0,94472 24,35451 15 ANOVA d.f. SS 143.679,83695 6.524,56305 150.204,4 MS 47.893,27898 593,1421 Coefficients Standard Error 524,57551 46,23553 -21,86669 5,542 -1,07721 1,07874 -0,55872 1,3919 LCL 398,90367 -36,93028 -4,00931 -4,34201 Regression Residual Total 3, 11, 14, Intercept Police Income TV F 80,74503 p-level 0,0000 UCL 650,24735 -6,80311 1,85489 3,22456 t Stat 11,34572 -3,94563 -0,99858 -0,40141 p-level 0,0000 0,00229 0,33946 0,6958 ! Which statement is true? A. Police is not statistically significant since p-value is less than 5% B. Police is statistically significant since p-value is smaller than 5% C. Police is not statistically significant since the standard error is small D. TV is not statistically significant because the estimated coefficient is the smallest E. None of them is true. 80. Suppose that the government wants to see whether increasing the share of renewable energy sources like solar and wind is positive for consumers. In particular, they want to see if the electricity bill would decrease by increasing the production of energy from the sun and the wind. They relate the electricity price (P) to the market share of solar (X1) and the market share of wind (X2) and they estimate the following multiple regression: P = 13.098 - 2.804*X1 - 2.230*X2 What is the forecasted electricity price if the share of solar is 10% (0.1) and that of wind is 20%? A. Predicted electricity price is 13.10 B. Predicted price is 12.37 C. You cannot calculate the predicted value since it is only a speculation. D. Predicted price is 8.10. E. None of the above