QUESTION 1 Which of the following descriptions of confidence interval is correct? if) A. If a 95% confidence interval contains 0. then the 90% confidence interval contains 0 CT;- 9. If a 95% confidence interval contains 1. then the 90% confidence interval contains 1 C- C. If a 99% condence interval contains 1. then the 95% confidence interval contains 1 if) D. If a 99% condence interval contains 0. then the 99.9% confidence interval contains 0 QUESTION 2 _.__is the probability of a Type ff error; and ____ is the probability of correctly rejecting a false null hypothesis. 53' a. 1-3; B (3" 5- E; 1-H If} C. :1; I3 5:) D-l3; :1 QUESTION 3 ___ is the probability of rejecting a false null hypothesis. 1:) Ft. HE; C" 5- E; L: C. a; 5:) D. 1- c; QUESTION 4 When you conduct a hypothesis testing. at which of the following P-value. you feel least condent to reject the null hypothesis? (:2- A. 0.90 If} E. 0.95 53' 0.0.05 i::;- [1.001 QUESTION 18 Is snowfall in the Sierra Nevada mountains associated with stream runoff in southern California? If so the amount of snowfall can be used to predict the volume of stream runoff, one factor that is known to affect the water supply in California. In this problem you are tasked with using a regression model to explore the relationship between snowfall (in inches) in the Sierra Nevadas and stream runoff volume (in acre-feet) near Bishop, California. A scatterplot of snowfall versus stream runoff and regression output from statistical software are given below and should be used to answer the following questions. The data set consists of 42 years of precipitation measurements at a site near Owens Valley in the Sierra Nevadas and stream runoff volume near Bishop, California. 120000 100000 - Stream runoff (acre-feet) 80000 - 60000 40000- 10 20 Snowfall (inches) 5 = 8991 Adj. R= = 0.8529 R' = 0.8565 Term Estimate Std. Error Intercept 26184.4 3517.3 snowfall 3824.8 247.5 Approximately what percentage of the variation in stream runoff does the regression model explain? O A. 68% B. 85.7% C. 92.6% O D. 100%QUESTION Ty s snowfall in the Sierra Nevada mountains associated with stream runoff in southern California? If so the amount of snowfall can be used to predict the volume of stream runoff, one factor that is known to affect the water supply in California. In this problem you are tasked with using a regression model to explore the relationship between snowfall (in inches) in the Sierra Nevadas and stream runoff volume (in acre-feet) near Bishop, California. A scatterplot of snowfall versus stream runoff and regression output from statistical software are given below and should be used to answer the following questions. The data set consists of 42 years of precipitation measurements at a site near Owens Valley in the Sierra Nevadas and stream runoff volume near Bishop, California. 120000- 100000 Stream runoff (acre-feet) 80000 - 60000 40000 - 10 20 Snowfall (inches) s = 8991 Adj. R* = 0.8529 R = 0.8565 Term Estimate Std. Error Intercept 26184.4 3517.3 snowfall 3824.8 247.5 For each additional inch of snowfall, steam runoff: O A. increases by 3824 acre-feet, on average. O B. decreases by 3824 acre-feet, on average. O C. increases by 26, 184 acre-feet, on average. O D. decreases by 26, 184 acre-feet, on average.QUESTION 21 The regression output for this simple linear regression model is given below: Regression Statistics: y = 96.2 f = 57.1 5. = 11.3 5.. = 20.6 s = 12 Adj. R' = 0.6613 R' = 0.6669 ANOVA Source DE sum of Mean Square F Value Prob > F Squares Regression 17003.4 17003.4 118.13 |4 Intercept 181.1443 7.9652 22.74 p2. The director's sample results did not reject the null hypothesis. If the new training is in fact effective in reducing work- related injuries, the director: O A. has made the correct decision. O B. is not very good at her job. C. made a Type | error. D. made a Type II error. QUESTION 7 Truckloads of apples arriving at a processing plant are inspected for quality. The truckload is considered acceptable if it has no more than 10% "defective" apples. If the quality control check determines that more than 10% of the apples are "defective," the whole truckload is rejected. The quality control inspector takes a random sample of 200 apples in order to test the hypotheses HO: p = 0.10. If a truckload of apples really has 8% defective apples, but it is rejected on the basis of the quality check, the inspector: O A. will likely lose his job. O B. has committed a Type | error. O C. has committed a Type II error. O D. has committed the Power error.QUESTION & Which of the following is TRUE about the correlation if we convert the distance to kilometers? Note that there is 0.6214 mile per kilometer. O A. The correlation would be smaller since a mile is shorter than a kilometer. O B. The correlation would be the same since correlation is not affected by units of measure. O C. The correlation would be larger since there are more kilometers in each distance measure. O D. The correlation would become negative. QUESTION 9 What is the difference between correlation analysis and regression analysis? O A. There is no difference between the two since they both provide information about the strength of relationship between two variables. B. regression provides a description of how one variable causes the other to change, while correlation analysis only describes the strength of the relationship between two variables. C. regression requires that one variable be explained or predicted by the other, whereas correlation analysis makes no distinction between the response and explanatory variables. O D. All of the answers are correct. QUESTION 10 Which description of the least-squares regression line is correct? O A. It is the line that minimizes the sum of errors of prediction in the data. B. It is the line that evenly splits the points so that the number of dots above the line and below the line are even. O C. It is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible. O D. It is the line that connects all dots in the scatter plot.QUESTION 11 Which of the following statements concerning the least squares regression of Y on X depicted in the graph below is TRUE? 65 10 20 30 50 60 70 X & A. The point (25, 60) is influential and has a positive and smaller residual. B. The point (25, 60) is influential and has a positive and larger residual. O C. The point (25, 60) is influential and has a negative and smaller residual. O D. The point (25, 60) is influential and has a negative and larger residual. QUESTION 12 A regression analysis between two variables X and Y resulted in a sample correlation coefficient () equal to 0.23. From this, you may conclude that: O A. 23% of the variability in Y is explained by the regression model. O B. 23% of the variability in X is explained by the regression model. O C. 5.29% of the variability in Y is explained by the regression model. O D. none of the above.QUESTION 13 Suppose that an analyst has calculated the correlation between the salary of bank employees (y) and number of years of experience (x) and found it to be 0. Frustrated, he then fits a regression line to better understand the relationship between x and y. He will find that the estimated slope is: O A. positive. O B. negative. O C. zero. O D. not enough information to tell.QUESTION 14 You are interested in starting a specialty coffee shop, but you need to establish how the volume of production will affect your average total costs before you can complete your business plan. You have a friend with connections in the industry, and she obtains the average total cost per cup for a random sample of several establishments in your region. The final data set your friend compiles contains information on 61 coffee shops including the average total cost of production (in cents per cup) and the rate of output (in cups per hour). 150 126 100 - Average Total Cost (cents/cup) 75 50 50 60 70 90 Output Rate (cups/hr) What is the explanatory variable? O A. revenue O B. coffee shops O C. output rate O D. average total costQUESTION 15 You are interested in starting a specialty coffee shop, but you need to establish how the volume of production will affect your average total costs before you can complete your business plan. You have a friend with connections in the industry, and she obtains the average total cost per cup for a random sample of several establishments in your region. The final data set your friend compiles contains information on 61 coffee shops including the average total cost of production (in cents per cup) and the rate of output (in cups per hour). 150 - 126 100- Average Total Cost (cents/cup) 75 50 - 40 50 60 70 80 90 Output Rate (cups/hr) What is the response variable? O A. revenue O B. coffee shops O C. output rate O D. average total costQUESTION 16 You are interested in starting a specialty coffee shop, but you need to establish how the volume of production will affect your average total costs before you can complete your business plan. You have a friend with connections in the industry, and she obtains the average total cost per cup for a random sample of several establishments in your region. The final data set your friend compiles contains information on 61 coffee shops including the average total cost of production (in cents per cup) and the rate of output (in cups per hour). 150 126 100 - Average Total Cost (cents/cup) 75 - 50 - 40 50 60 70 80 90 Output Rate (cups/hr) Which of the following BEST describes the association between average total cost and output rate? O A. strong, positive, linear association O B. strong, negative, linear association O C. weak, negative, linear association O D. weak, positive, linear associationQUESTION 17 Is snowfall in the Sierra Nevada mountains associated with stream runoff in southern California? If so the amount of snowfall can be used to predict the volume of stream runoff, one factor that is known to affect the water supply in California. In this problem you are tasked with using a regression model to explore the relationship between snowfall (in inches) in the Sierra Nevadas and stream runoff volume (in acre-feet) near Bishop, California. A scatterplot of snowfall versus stream runoff and regression output from statistical software are given below and should be used to answer the following questions. The data set consists of 42 years of precipitation measurements at a site near Owens Valley in the Sierra Nevadas and stream runoff volume near Bishop, California 120000 100000 Stream runoff (acre-feet) 80000 50000 40000 - 10 20 Snowfall (inches) S = 8991 Adj. R = 0.8529 R2 = 0.8565 Term Estimate Std. Error Intercept 26184.4 3517.3 snowfall 3824.8 247.5 Which of the following BEST describes the association between stream runoff and snowfall? O A. no association O B. positive nonlinear association O C. negative linear association O D. positive linear association