1- Bicycling World, a magazine devoted to cycling, reviews hundreds of bicycles throughout the year. Its “Road-Race” category contains reviews of bicycles used by riders primarily interested in racing. One of the most important factors in selecting a bicycle for racing is the weight of the bicycle. The following data show the weight (pounds) and price ($) for ten racing bicycles reviewed by the magazine.Click on the datafile logo to reference the data. ModelWeight (lb)Price ($) Fierro 7B17.92,200 HX 500016.26,350 Durbin Ultralight15.08,470 Schmidt16.06,300 WSilton Advanced17.34,100 Bicyclette vélo13.28,700 Supremo Team16.36,100 XTC Racer17.22,680 D’Onofrio Pro17.73,500 Americana #614.28,100(a)Choose a scatter chart below with weight as the independent variable.(i)(ii)(iii)(iv)- Select your answer -Chart (i)Chart (ii)Chart (iii)Chart (iv)Item 1 What does the scatter chart indicate about the relationship between the weight and price of these bicycles?The scatter chart indicates there may be a – Select your answer -positivenegativeItem 2 linear relationship between weight and price. (b)Use the data to develop an estimated regression equation that could be used to estimate the price for a bicycle given its weight. What is the estimated regression model?Let x represent the bicycle weight.If required, round your answers to two decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x (c)Test whether each of the regression parameters β0 and β1 is equal to zero at a 0.05 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable?(i)We can conclude that neither β0 nor β1 are equal to zero, where β0 is the estimated change in price for a one pound weight increase and β1 is the estimated price for a bicycle weighing zero pounds. Neither interpretations are reasonable.(ii)We cannot conclude that either β0 or β1 are equal to zero, where β0 is the estimated price for a bicycle weighing zero pounds and β1 is the estimated change in price for a one pound weight increase. Both interpretations are reasonable.(iii)We can conclude that neither β0 nor β1 are equal to zero, where β0 is the estimated price for a bicycle weighing zero pounds and β1 is the estimated change in price for a one pound weight increase. The interpretation of β0 is not reasonable but the interpretation of β1 is reasonable.(iv)We can conclude that β0 = 0 but β1 ≠ 0, where β0 is the estimated price for a bicycle weighing zero pounds and β1 is the estimated change in price for a one pound weight increase. The interpretation of β0 is not reasonable but the interpretation of β1 is reasonable.- Select your answer -Option (i)Option (ii)Option (iii)Option (iv)Item 5 (d)How much of the variation in the prices of the bicycles in the sample does the regression model you estimated in part (b) explain? If required, round your answer to two decimal places. % (e)The manufacturers of the D’Onofrio Pro plan to introduce the 16.8 lb D’Onofrio Elite bicycle later this year. Use the regression model you estimated in part (b) to predict the price of the D’Ononfrio Elite. If required, round your answer to the nearest whole number. Do not round intermediate calculations.$2- Dixie Showtime Movie Theaters, Inc., owns and operates a chain of cinemas in several markets in the southern U.S. The owners would like to estimate weekly gross revenue as a function of advertising expenditures. Data for a sample of eight markets for a recent week follow. MarketWeekly Gross Revenue($100s)Television Advertising($100s)Newspaper Advertising($100s) Mobile101.3 5.01.5 Shreveport51.9 3.03.0 Jackson74.8 4.01.5 Birmingham126.2 4.34.3 Little Rock137.8 3.64.0 Biloxi101.4 3.52.3 New Orleans237.8 5.08.4 Baton Rouge219.6 6.95.8(a)Use the data to develop an estimated regression equation with the amount of television advertising as the independent variable.Let x represent the amount of television advertising.If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x Test for a significant relationship between television advertising and weekly gross revenue at the 0.05 level of significance. What is the interpretation of this relationship?There – Select your answer -isis notItem 3 a significant relationship between the amount spent on television advertising and weekly gross revenue. The estimated regression equation is the best estimate of the – Select your answer -amount spent on television advertisingweekly gross revenueItem 4 given the – Select your answer -amount spent on television advertisingweekly gross revenueItem 5 . (b)How much of the variation in the sample values of weekly gross revenue does the model in part (a) explain?If required, round your answer to two decimal places. % (c)Use the data to develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables.Let x1 represent the amount of television advertising.Let x2 represent the amount of newspaper advertising.If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x1 + x2 Test whether each of the regression parameters β0, β1, and β2 is equal to zero at a 0.05 level of significance.We – Select your answer -cancannotItem 10 conclude that β0 = 0.We – Select your answer -cancannotItem 11 conclude that β1 = 0.We – Select your answer -cancannotItem 12 conclude that β2 = 0. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable?(i)β0 is the estimate of the weekly gross revenue when television and newspaper advertising are both zero. β1 is the estimate of change in the weekly gross revenue if newspaper advertising is held constant and there is a $100 increase in television advertising. β2 is the estimate of change in the weekly gross revenue if television advertising is held constant and there is a $100 increase in newspaper advertising. The interpretation of β0 is not reasonable but the interpretations of β1 and β2 are reasonable.(ii)β0 is the estimate of the weekly gross revenue when television and newspaper advertising are both zero. β1 is the estimate of change in the weekly gross revenue if television advertising is held constant and there is a $100 increase in newspaper advertising. β2 is the estimate of change in the weekly gross revenue if newspaper advertising is held constant and there is a $100 increase in television advertising. The interpretation of β0 is not reasonable but the interpretations of β1 and β2 are reasonable.(iii)β0 is the estimate of change in the weekly gross revenue if newspaper advertising is held constant and there is a $100 increase in television advertising. β1 is the estimate of change in the weekly gross revenue if television advertising is held constant and there is a $100 increase in newspaper advertising. β2 is the estimate of the weekly gross revenue when television and newspaper advertising are both zero. The interpretation of β0, β1, and β2 are all reasonable. – Select your answer -Option (i)Option (ii)Option (iii)Item 13 (d)How much of the variation in the sample values of weekly gross revenue does the model in part (c) explain?If required, round your answer to two decimal places. % (e)Given the results in part (a) and part (c), what should your next step be? Explain.The input in the box below will not be graded, but may be reviewed and considered by your instructor. (f)What are the managerial implications of these results?Management can feel confident that increased spending on – Select your answer -both television and newspaperonly newspaperItem 16 advertising results in increased weekly gross revenue. The results also suggest that – Select your answer -newspapertelevision Item 17 advertising may be slightly more effective than – Select your answer -newspapertelevision Item 18 advertising in generating revenue.3- The National Football League (NFL) records a variety of performance data for individuals and teams. To investigate the importance of passing on the percentage of games won by a team, the following data show the conference (Conf), average number of passing yards per attempt (Yds/Att), the number of interceptions thrown per attempt (Int/Att), and the percentage of games won (Win%) for a random sample of 16 NFL teams for the 2011 season (NFL web site, February 12, 2012).Click on the datafile logo to reference the data.TeamConferenceYds/AttInt/AttWin%Arizona CardinalsNFC6.50.04250.0Atlanta FalconsNFC7.10.02262.5Carolina PanthersNFC7.40.03337.5Cincinnati BengalsAFC6.20.02656.3Detroit LionsNFC7.20.02462.5Green Bay PackersNFC8.90.01493.8Houstan TexansAFC7.50.01962.5Indianapolis ColtsAFC5.60.02612.5Jacksonville JaguarsAFC4.60.03231.3Minnesota VikingsNFC5.80.03318.8New England PatriotsAFC8.30.02081.3New Orleans SaintsNFC8.10.02181.3Oakland RaidersAFC7.60.04450.0San Francisco 49ersNFC6.50.01181.3Tennessee TitansAFC6.70.02456.3Washington RedskinsNFC6.40.04131.3Let x1 represent Yds/Att.Let x2 represent Int/Att.(a)Develop the estimated regression equation that could be used to predict the percentage of games won, given the average number of passing yards per attempt. If required, round your answer to three decimal digits. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)ŷ = + x1What proportion of variation in the sample values of proportion of games won does this model explain? If required, round your answer to one decimal digit. %(b)Develop the estimated regression equation that could be used to predict the percentage of games won, given the number of interceptions thrown per attempt. If required, round your answer to three decimal digits. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)ŷ = + x2What proportion of variation in the sample values of proportion of games won does this model explain? If required, round your answer to one decimal digit. %(c)Develop the estimated regression equation that could be used to predict the percentage of games won, given the average number of passing yards per attempt and the number of interceptions thrown per attempt. If required, round your answer to three decimal digits. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)ŷ = + x1 + x2What proportion of variation in the sample values of proportion of games won does this model explain? If required, round your answer to one decimal digit. %(d)The average number of passing yards per attempt for the Seattle Seahawks during the 2011 season was 6.8, and the team’s number of interceptions thrown per attempt was 0.028. Use the estimated regression equation developed in part (c) to predict the percentage of games won by the Seattle Seahawks during the 2011 season. (Note: For the 2011 season, the Seattle Seahawks’ record was 7 wins and 9 loses.)If required, round your answer to one decimal digit. Do not round intermediate calculations. %Compare your prediction to the actual percentage of games won by the Seattle Seahawks. If required, round your answer to one decimal digit.The Seattle Seahawks performed – Select your answer -betterworseItem 12 than what we predicted by %.(e)Did the estimated regression equation that uses only the average number of passing yards per attempt as the independent variable to predict the percentage of games won provide a good fit?Based on the coefficient of determination from the two models using the average number of passing yards per attempt as the independent variable, the model using only the average number of passing yards per attempt as the independent variable – Select your answer -isis notItem 14 the best fit.4- A sample containing years to maturity and (percent) yield for 40 corporate bonds is contained in the DATAfile named CorporateBonds (Barron’s, April 2, 2012).Click on the datafile logo to reference the data.(a)Choose a scatter chart below with years to maturity as the independent variable.(i) (ii) (iii) (iv)- Select your answer -Chart (i)Chart (ii)Chart (iii)Chart (iv)Item 1 Does a simple linear regression model appear to be appropriate?A – Select your answer -linearcurvilinearItem 2 regression model appears to be more appropriate. (b)Develop an estimated quadratic regression equation with years to maturity and squared values of years to maturity as the independent variables.If required, round your answers to four decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x + x2How much variation in the sample values of yield is explained by this regression model?If required, round your answer to one decimal places. %Test the relationship between each of the independent variables and the dependent variable at a 0.05 level of significance. How would you interpret this model?- Select your answer -Reject Fail to rejectItem 7 β1 = 0 and conclude there – Select your answer -isis notItem 8 a relationship between years to maturity and yield. – Select your answer -Reject Fail to rejectItem 9 β2 = 0 and conclude there – Select your answer -isis notItem 10 a relationship between squared values of years to maturity and yield. (c)Use Excel to create a plot of the linear and quadratic regression lines overlaid on the scatter chart of years to maturity and yield. Choose the correct chart below.(i) (ii) (iii) (iv)- Select your answer -Chart (i)Chart (ii)Chart (iii)Chart (iv)Item 11 Does this helps you better understand the difference in how the quadratic regression model and a simple linear regression model fit the sample data?The graph shows – Select your answer -a substantiallittleItem 12 difference between the fit of the linear and quadratic regression models to the data.Which model does this chart suggest provides a superior fit to the sample data?- Select your answer -Linear regressionQuadratic regressionItem 13 (d)What other independent variables could you include in your regression model to explain more variation in yield? The input in the box below will not be graded, but may be reviewed and considered by your instructor. 5- The American Association of Individual Investors (AAII) On-Line Discount Broker Survey polls members on their experiences with electronic trades handled by discount brokers. As part of the survey, members were asked to rate their satisfaction with the trade price and the speed of execution, as well as provide an overall satisfaction rating. Possible responses (scores) were no opinion (0), unsatisfied (1), somewhat satisfied (2), satisfied (3), and very satisfied (4). For each broker, summary scores were computed by computing a weighted average of the scores provided by each respondent. A portion the survey results follow (AAII website, February 7, 2012). BrokerageSatisfaction withTrade PriceSatisfaction withSpeed of ExecutionOverall Satisfaction withElectronic TradesScottrade, Inc.3.23.13.2Charles Schwab3.33.13.2Fidelity Brokerage Services3.13.34.0TD Ameritrade2.83.53.7E*Trade Financial2.93.23.0(Not listed)2.43.22.7Vanguard Brokerage Services2.73.82.7USAA Brokerage Services2.43.73.4Thinkorswim2.62.62.7Wells Fargo Investments2.32.72.3Interactive Brokers3.73.94.0Zecco.com2.52.52.5Firstrade Securities3.03.03.0Banc of America Investment Services1.04.02.0(a)Develop an estimated regression equation using trade price and speed of execution to predict overall satisfaction with the broker.Let x1 represent satisfaction with Trade Price.Let x2 represent satisfaction with speed of execution.If required, round your answers to four decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x1 + x2What is the coefficient of determination?If required, round your answers to four decimal places.Interpret the coefficient of determination. If required, round your answer to one decimal places.The regression model explains approximately % of the variation in the values of overall satisfaction in the sample. (b)Use the t test to determine the significance of each independent variable. What are your conclusions at the 0.05 level of significance?We – Select your answer -cancannotItem 6 conclude that β1 = 0. That is, there – Select your answer -isis notItem 7 a relationship between satisfaction with trade price and overall satisfaction with the electronic trade.We – Select your answer -cancannotItem 8 conclude that β2 = 0. That is, there – Select your answer -isis notItem 9 a relationship between satisfaction with speed of execution and overall satisfaction with the electronic made. (c)Interpret the estimated regression parameters. Are the relationships indicated by these estimates what you would expect?(i)β0 is the estimated overall satisfaction with the electronic trade when satisfaction with trade price and speed of execution are both 0. β1 is the estimated change in overall satisfaction with the electronic trade if satisfaction with speed of execution is held constant and there is a 1 point increase in satisfaction with trade price. β2 is the is the estimated change in overall satisfaction with the electronic trade if satisfaction with trade price is held constant and there is a 1 point increase in satisfaction with speed of execution.(ii)β0 is the estimated overall satisfaction with the electronic trade when satisfaction with trade price and speed of execution are both 1. β1 is the estimated change in overall satisfaction with the electronic trade if satisfaction with speed of execution is held constant and there is a 1 point increase in satisfaction with trade price. β2 is the is the estimated change in overall satisfaction with the electronic trade if satisfaction with trade price is held constant and there is a 1 point increase in satisfaction with speed of execution.(iii)β0 is the estimated overall satisfaction with the electronic trade when satisfaction with trade price and speed of execution are both 1. β1 is the estimated change in overall satisfaction with the electronic trade if satisfaction with trade price is held constant and there is a 1 point increase in satisfaction with speed of execution. β2 is the estimated change in overall satisfaction with the electronic trade if satisfaction with speed of execution is held constant and there is a 1 point increase in satisfaction with trade price.(iv)β0 is the estimated overall satisfaction with the electronic trade when satisfaction with trade price and speed of execution are both 0. β1 is the estimated change in overall satisfaction with the electronic trade if satisfaction with trade price is held constant and there is a 1 point increase in satisfaction with speed of execution. β2 is the estimated change in overall satisfaction with the electronic trade if satisfaction with speed of execution is held constant and there is a 1 point increase in satisfaction with trade price. – Select your answer -Option (i)Option (ii)Option (iii)Option (iv)Item 10 (d)Finger Lakes Investments has developed a new electronic trading system and would like to predict overall customer satisfaction assuming they can provide satisfactory levels of service levels (2) for both trade price and speed of execution. Use the estimated regression equation developed in part (a) to predict overall satisfaction level for Lakes Investments if they can achieve these performance levels.If required, round your answer to one decimal places. Do not round intermediate calculations. (e)What concerns (if any) do you have with regard to the possible responses the respondents could select on the survey?The input in the box below will not be graded, but may be reviewed and considered by your instructor.6- A marketing professor at Givens College is interested in the relationship between hours spent studying and total points earned in a course. Data collected on 156 students who took the course last semester are provided in the file MktHrsPts.Click on the datafile logo to reference the data.(a)Choose a scatter chart below with Hours Spent Studying as the independent variable.(i)(ii)(iii)(iv)- Select your answer -Graph (i)Graph (ii)Graph (iii)Graph (iv)Item 1What does the scatter chart indicate about the relationship between hours spent studying and total points earned?The scatter chart indicates there may be a – Select your answer -positivenegativeItem 2 linear relationship between hours spent studying and total points earned. Students who spend more time studying generally earn more points, and this scatter chart is consistent with what is expected.(b)Develop an estimated regression equation showing how total points earned is related to hours spent studying. What is the estimated regression model?Let x represent the hours spent studying.If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x(c)Test whether each of the regression parameters β0 and β1 is equal to zero at a 0.01 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable?(i)We can conclude that both β0 and β1 are equal to zero, where β0 is the estimated change in total points earned for a one hour increase in time spent studying and β1 is the estimated total points earned when the hours spent studying is zero. The interpretation of β0 is not reasonable but the interpretation of β1 is reasonable.(ii)We can conclude that neither β0 nor β1 are equal to zero, where β0 is the estimated total points earned when the hours spent studying is zero and β1 is the estimated change in total points earned for a one hour increase in time spent studying. The interpretation of β0 is not reasonable but the interpretation of β1 is reasonable.(iii)We cannot conclude that neither β0 nor β1 are equal to zero, where β0 is the estimated total points earned when the hours spent studying is zero and β1 is the estimated change in total points earned for a one hour increase in time spent studying. The interpretation of β0 is reasonable but the interpretation of β1 is not reasonable.(iv)We can conclude that β0 = 0 but β1 ≠ 0, where β0 is the estimated change in total points earned for a one hour increase in time spent studying and β1 is the estimated total points earned when the hours spent studying is zero. Both interpretations are reasonable.- Select your answer -Option (i)Option (ii)Option (iii)Option (iv)Item 5(d)How much of the variation in the sample values of total point earned does the model you estimated in part (b) explain? If required, round your answer to two decimal places. %(e)Mark Sweeney spent 95 hours studying. Use the regression model you estimated in part (b) to predict the total points Mark earned.If required, round your answer to the nearest whole number. Do not round intermediate calculations. $7- The Dow Jones Industrial Average (DJIA) and the Standard & Poor’s 500 (S&P 500) indexes are used as measures of overall movement in the stock market. The DJIA is based on the price movements of 30 large companies; the S&P 500 is an index composed of 500 stocks. Some say the S&P 500 is a better measure of stock market performance because it is broader based. The closing price for the DJIA and the S&P 500 for 15 weeks, beginning with January 6, 2012, follow (Barron’s website, April 17, 2012).Click on the datafile logo to reference the data.DateDJIAS&PJanuary 612,3601,278January 1312,4221,289January 2012,7201,315January 2712,6601,316February 312,8621,345February 1012,8011,343February 1712,9501,362February 2412,9831,366March 212,9781,370March 912,9221,371March 1613,2331,404March 2313,0811,397March 3013,2121,408April 513,0601,398April 1612,8501,370(a)Choose a scatter chart below with DJIA as the independent variable.(i)(ii)(iii)(iv)- Select your answer -Chart (i)Chart (ii)Chart (iii)Chart (iv)Item 1 What does the scatter chart indicate about the relationship between DJIA and S&P 500?The scatter chart indicates there may be a – Select your answer -positivenegativeItem 2 linear relationship between DJIA and S&P 500. (b)Use the data to develop an estimated regression equation showing how S&P 500 is related to DJIA. What is the estimated regression model?Let x represent the DJIA indexes.If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x (c)What is the 95% confidence interval for the regression parameter β1?If required, round your answer to three decimal places.Lower 95% = Upper 95% = Based on this interval, what conclusion can you make about the hypotheses that the regression parameter β1 is equal to zero? Because the interval – Select your answer -includesdoes not includeItem 7 0, – Select your answer -reject do not rejectItem 8 the hypothesis that β1 = 0. (d)What is the 95% confidence interval for the regression parameter β0?If required, round your answer to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)Lower 95% = Upper 95% = Based on this interval, what conclusion can you make about the hypotheses that the regression parameter β0 is equal to zero? Because the interval – Select your answer -includesdoes not includeItem 11 0, – Select your answer -reject do not rejectItem 12 the hypothesis that β0 = 0. (e)How much of the variation in the sample values of S&P 500 does the model estimated in part (b) explain?If required, round your answer to two decimal places. % (f)Suppose that the closing price for the DJIA is 13,200. Estimate the closing price for the S&P 500.If required, round your answer to the nearest whole number. Do not round intermediate calculations. (g)Should we be concerned with the DJIA value used to predict the S&P 500 value in part (f)? The maximum DJIA in the sample data is 13,233, so when the DJIA value of 13,200 is used to predict the S&P 500 value in part (f) the regression model has – Select your answer -beennot beenItem 15 extrapolated beyond the experimental region of the data, so you should – Select your answer -benot beItem 16 concerned about this prediction.8- Consider the example of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the dataset over the past year (y), and the independent variables are the customer’s annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3).Click on the datafile logo to reference the data.(a)Estimate the corresponding simple linear regression with the customer’s annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable.x1 represents customer’s annual household income.If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x1 Interpret the estimated relationship between the customer’s annual household income and credit card charges accrued over the past year. Choose the correct answer below.(i)As a customer’s annual income decreases by $1000, the credit card charges accrued by the customer over the past year will be higher.(ii)As a customer’s annual income increases by $1000, the credit card charges accrued by the customer over the past year will be higher.(iii)As a customer’s annual income increases by $1000, the credit card charges accrued by the customer over the past year will be lower.(iv)As a customer’s annual income decreases by $1000, the credit card charges accrued by the customer over the past year will be lower. – Select your answer -Option (i)Option (ii)Option (iii)Option (iv)Item 3How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model?If required, round your answer to two decimal places. % (b)Estimate the corresponding simple linear regression with the number of members in the customer’s household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable.x2 represents number of members of the household.If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x2Interpret the estimated relationship between the number of members in the customer’s household and credit card charges accrued over the past year. Choose the correct answer below.(i)As the number of members in the customer’s household decreases by one, the credit card charges accrued by the customer over the past year will be higher.(ii)As the number of members in the customer’s household increases by one, the credit card charges accrued by the customer over the past year will be lower.(iii)As the number of members in the customer’s household increases by one, the credit card charges accrued by the customer over the past year will be higher.(iv)As the number of members in the customer’s household decreases by one, the credit card charges accrued by the customer over the past year will be lower. – Select your answer -Option (i)Option (ii)Option (iii)Option (iv)Item 7 How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model?If required, round your answer to two decimal places. % (c)Estimate the corresponding simple linear regression with the customer’s number of years of post-high school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. x3 represents number of years of post-high school education. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) = + x3 Interpret the estimated relationship between the customer’s number of years of post-high school education and credit card charges accrued over the past year. Choose the correct answer below. (i)As a customer’s years of post-high school education increases by one year, the credit card charges accrued by the customer over the past year will be lower.(ii)As a customer’s years of post-high school education decreases by one year, the credit card charges accrued by the customer over the past year will be lower.(iii)As a customer’s years of post-high school education decreases by one year, the credit card charges accrued by the customer over the past year will be higher.(iv)As a customer’s years of post-high school education increases by one year, the credit card charges accrued by the customer over the past year will be higher.- Select your answer -Option (i)Option (ii)Option (iii)Option (iv)Item 11 How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? If required, round your answer to two decimal places. % (d)Consider the multiple regressions with credit card charges accrued by a customer over the past year as the dependent variable and customer’s annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. = 2051.639 + 120.632×1 + 533.846×2 – 505.632×3 Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? – Select your answer -YesNoItem 13 What does this tell you about multicollinearity in the multiple regression model shown above? This – Select your answer -stabilitylarge differenceItem 14 in the values of the parameter estimates suggests – Select your answer -considerablelittleItem 15 multicollinearity in the multiple linear regression. (e)Consider the multiple regressions with credit card charges accrued by a customer over the past year as the dependent variable and customer’s annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. The coefficient of determination for the multiple regression model is 36.35%. = 2051.639 + 120.632×1 + 533.846×2 – 505.632×3 Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model shown above? What does this tell you about multicollinearity in the multiple regression model shown above? There is – Select your answer -considerablealmost noItem 16 multicollinearity in the estimated multiple linear regression from the figure. (f)Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his/her credit limit in past 12 months as independent variables to the multiple regression model shown in part (d). Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his/her credit limit in past 12 months as 1 if the customer has exceeded his/her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model? Explain. The coefficient of determination for the new multiple regression model is – Select your answer -only slightlyvastlyItem 17 greater than that of the model in part (d). Thus, adding independent variables for age, sex, and whether a customer has exceeded his/her credit limit in past 12 months – Select your answer -doesdoes notItem 18 substantially improve the fit of the model. Company Ticker Years
Yield
GE
1
0.767
MS
1
1.816
WFC
1.25
0.797
TOTAL
1.75
1.378
TOTAL
3.25
1.748
GS
3.75
3.558
MS
4
4.413
JPM
4.25
2.31
C
4.75
3.332
RABOBK
4.75
2.805
TOTAL
5
2.069
MS
5
4.739
AXP
5
2.181
MTNA
5
4.366
BAC
5
3.699
VOD
5
1.855
SHBASS
5
2.861
AIG
5
3.452
HCN
7
4.184
MS
9.25
5.798
GS
9.25
5.365
GE
9.5
3.778
GS
9.75
5.367
C
9.75
4.414
BAC
9.75
4.949
RABOBK
9.75
4.203
WFC
10
3.682
TOTAL
10
3.27
MTNA
10
6.046
LNC
10
4.163
FCX
10
4.03
NEM
10
3.866
PAA
10.25
3.856
HSBC
12
4.079
GS
25.5
6.913
C
25.75
8.204
GE
26
5.13
GE
26.75
5.138
T
28.5
4.93
BAC
29.75
5.903
Hours Spent Studying Total Points Earned
22
12
20
18
29
25
24
25
43
28
46
29
39
35
40
39
51
40
43
43
39
43
51
49
52
50
65
51
57
53
56
53
52
55
66
55
63
57
68
57
67
59
42
59
65
59
69
60
72
60
66
61
53
61
45
61
58
62
81
62
60
62
57
62
77
62
78
63
67
64
78
64
72
64
58
64
71
65
76
65
79
66
83
66
65
66
72
71
52
78
70
81
80
79
72
75
91
65
84
77
78
72
84
83
67
80
78
72
70
94
92
84
98
78
78
84
74
90
83
84
83
78
93
80
101
81
83
91
83
93
66
66
67
67
67
67
68
68
68
68
68
68
69
69
70
70
70
70
70
71
71
72
72
72
72
73
73
73
73
73
74
74
74
74
75
75
75
75
76
76
76
76
76
76
78
78
65
84
97
88
93
93
95
95
91
95
94
95
102
105
83
99
97
79
101
88
93
95
94
104
88
80
98
83
91
90
83
92
88
99
101
101
99
102
84
110
93
105
76
77
77
77
77
77
78
78
78
79
79
79
79
80
80
80
80
80
81
81
81
82
83
85
85
85
85
86
86
86
86
87
87
88
88
89
90
90
90
90
90
91
91
91
109
91
104
95
98
91
104
104
106
95
106
92
101
95
109
95
101
105
104
104
105
95
109
110
101
91
92
92
92
92
93
93
94
95
95
95
95
96
96
96
96
96
97
97
97
98
99
100
100
100
Account Annual Income Household Years of Post-High
Hours Per Week
Number
($1000)
Size
School Education Watching Television
23313578
21.8
4.0
5.0
31.0
14168784
65.5
7.0
3.0
48.0
498076
54.2
3.0
2.0
20.0
12286258
73.7
6.0
0.0
46.0
14458626
110.4
7.0
5.0
39.0
6587932
22.1
8.0
3.0
41.0
28612775
39.6
5.0
4.0
40.0
23618891
90.6
8.0
5.0
27.0
6620536
38.7
1.0
4.0
17.0
25744803
60.5
3.0
1.0
5.0
26373139
104.3
4.0
5.0
58.0
18241851
44.5
3.0
5.0
21.0
29558287
67.1
6.0
1.0
33.0
14427717
72.3
3.0
3.0
27.0
19206708
114.8
5.0
3.0
23.0
13775672
96.0
6.0
2.0
36.0
28207255
90.2
4.0
0.0
59.0
26411517
54.6
4.0
0.0
8.0
22411321
97.4
4.0
4.0
30.0
29587727
45.9
7.0
3.0
46.0
25774035
61.8
4.0
4.0
55.0
202180
38.6
2.0
3.0
59.0
1412960
68.9
3.0
3.0
29.0
7418245
73.4
7.0
1.0
9.0
10107002
22.6
6.0
0.0
19.0
26588030
50.5
7.0
5.0
22.0
26296381
81.6
5.0
1.0
34.0
26868036
37.3
1.0
4.0
20.0
13898458
66.1
4.0
3.0
40.0
11423261
24.3
5.0
3.0
48.0
29896806
32.5
8.0
4.0
16.0
29336958
65.7
8.0
1.0
18.0
25494293
51.4
1.0
3.0
18.0
25239019
101.8
8.0
5.0
22.0
13964728
31.8
4.0
4.0
19.0
12936145
120.9
8.0
1.0
16.0
9742628
102.8
7.0
3.0
53.0
25804252
70.4
5.0
2.0
22.0
21467566
43.2
6.0
0.0
13.0
2948494
33.6
6.0
2.0
23.0
859874
32.9
3.0
2.0
58.0
Age
47
60
47
41
36
52
49
46
40
60
53
30
35
33
29
19
24
39
28
33
65
43
29
26
60
54
50
55
23
61
42
56
56
36
40
54
60
56
27
38
46
6733609
18368006
28865347
23958869
25490051
14065877
21437302
24184521
14880022
24346545
25139767
8424632
4781882
28239888
15048884
4659321
23075630
4724635
7794950
372971
11008291
5458297
9805512
1046640
25767927
12663249
8459859
21754363
29355312
2000496
8799951
27033290
18638518
6013029
22400666
25744338
27186018
25944181
29429534
6101923
14995462
3034485
14497613
1653114
97.9
101.7
77.2
86.2
111.9
59.8
74.5
56.8
39.9
96.6
53.5
126.9
49.9
52.0
114.8
126.9
60.3
42.3
99.5
34.9
71.5
51.6
90.7
16.4
47.0
58.7
81.4
77.8
99.2
27.6
82.3
118.3
67.5
78.6
36.9
30.4
24.1
49.2
76.4
15.9
59.6
105.7
56.3
74.7
5.0
3.0
5.0
5.0
6.0
6.0
6.0
1.0
6.0
4.0
1.0
5.0
7.0
6.0
1.0
6.0
5.0
4.0
2.0
4.0
3.0
8.0
4.0
5.0
7.0
4.0
6.0
6.0
1.0
3.0
5.0
6.0
7.0
4.0
6.0
1.0
3.0
2.0
5.0
3.0
1.0
3.0
7.0
7.0
3.0
2.0
4.0
4.0
0.0
3.0
2.0
3.0
2.0
2.0
0.0
0.0
4.0
0.0
1.0
2.0
4.0
3.0
4.0
3.0
0.0
2.0
0.0
2.0
4.0
1.0
4.0
5.0
2.0
1.0
5.0
4.0
2.0
5.0
2.0
3.0
2.0
0.0
1.0
4.0
2.0
4.0
0.0
4.0
23.0
17.0
59.0
56.0
56.0
23.0
40.0
48.0
44.0
9.0
16.0
43.0
25.0
38.0
16.0
5.0
5.0
12.0
20.0
56.0
34.0
23.0
60.0
29.0
8.0
46.0
51.0
41.0
6.0
18.0
34.0
15.0
10.0
50.0
19.0
15.0
43.0
31.0
12.0
51.0
28.0
56.0
20.0
40.0
26
57
38
53
44
57
37
43
30
25
34
56
42
57
28
40
46
41
46
38
36
45
57
34
41
31
36
38
54
57
46
60
57
50
57
57
63
40
62
49
51
28
30
48
15982419
13122131
29416507
19826540
23912034
22227523
5481221
1552548
6470048
25487931
25007131
6568944
6455160
16014897
17814563
11443767
28375031
15967543
24035991
2640129
27069259
1716370
6740574
16904952
4820296
15176396
2533454
16790273
28054301
10206351
28421385
3348962
7887894
2214342
14259159
4646074
1780092
16313800
10125132
11065808
3361747
19820027
20977364
15171096
30.7
103.8
60.1
23.3
77.3
69.7
106.8
104.1
80.3
89.9
82.6
45.5
45.7
76.1
91.3
105.8
43.0
66.7
48.1
29.7
98.7
119.5
111.9
30.5
49.9
54.7
116.4
68.4
28.3
71.7
69.8
78.3
85.2
22.6
43.1
109.6
56.7
125.5
52.0
40.0
46.0
101.3
101.1
83.1
1.0
5.0
7.0
6.0
3.0
2.0
6.0
2.0
2.0
2.0
2.0
2.0
7.0
5.0
4.0
4.0
8.0
5.0
2.0
1.0
2.0
5.0
3.0
6.0
6.0
2.0
8.0
5.0
5.0
7.0
6.0
8.0
7.0
3.0
7.0
4.0
5.0
1.0
3.0
7.0
5.0
3.0
4.0
7.0
3.0
1.0
3.0
4.0
5.0
2.0
3.0
4.0
4.0
3.0
3.0
4.0
1.0
5.0
2.0
5.0
3.0
0.0
3.0
4.0
3.0
3.0
5.0
2.0
0.0
3.0
2.0
1.0
4.0
1.0
1.0
1.0
1.0
4.0
5.0
1.0
4.0
0.0
1.0
2.0
2.0
4.0
3.0
0.0
38.0
10.0
30.0
36.0
50.0
9.0
59.0
27.0
19.0
43.0
8.0
13.0
2.0
49.0
14.0
25.0
59.0
38.0
24.0
43.0
57.0
24.0
53.0
49.0
36.0
42.0
59.0
55.0
48.0
57.0
45.0
5.0
32.0
37.0
6.0
1.0
22.0
23.0
56.0
41.0
5.0
40.0
11.0
8.0
60
59
57
50
42
51
42
41
37
27
48
55
43
44
55
43
45
46
44
39
26
44
45
32
36
34
52
36
39
41
43
56
47
57
32
41
44
47
33
43
43
43
34
51
7972824
22689265
22596549
4690743
29540159
27334786
1157829
20466474
10315180
21421097
11409800
19144052
23887312
9339989
26825633
6370873
11898332
25950666
3691035
9017267
12604097
5364010
11614563
5364429
27966373
7169046
6604608
17485929
11108099
8783277
22386048
11910983
12403389
3462500
21311106
17225837
28381633
11128055
10592247
21344014
858023
17289872
10071020
10660681
69.2
47.4
82.0
76.1
63.9
87.8
67.1
30.5
69.6
31.9
36.2
96.3
50.3
97.7
70.6
71.9
22.9
68.9
30.0
116.9
82.0
29.9
117.5
66.5
60.0
50.8
77.8
100.2
63.8
46.4
36.5
74.9
80.1
111.1
70.6
42.9
102.5
46.3
50.5
31.4
64.7
38.4
100.0
44.3
1.0
8.0
7.0
8.0
5.0
3.0
2.0
4.0
4.0
6.0
3.0
4.0
2.0
7.0
5.0
3.0
7.0
2.0
8.0
1.0
2.0
7.0
3.0
3.0
5.0
4.0
6.0
2.0
7.0
7.0
2.0
8.0
5.0
2.0
5.0
2.0
2.0
1.0
4.0
5.0
5.0
1.0
6.0
5.0
1.0
4.0
2.0
4.0
5.0
2.0
1.0
2.0
3.0
3.0
5.0
4.0
5.0
5.0
2.0
4.0
0.0
5.0
4.0
5.0
4.0
5.0
3.0
0.0
2.0
3.0
4.0
0.0
4.0
4.0
1.0
5.0
4.0
3.0
5.0
1.0
4.0
1.0
0.0
4.0
4.0
3.0
5.0
2.0
7.0
35.0
59.0
1.0
57.0
58.0
21.0
53.0
46.0
55.0
23.0
33.0
44.0
16.0
51.0
48.0
45.0
50.0
49.0
7.0
35.0
7.0
32.0
37.0
9.0
23.0
39.0
17.0
44.0
22.0
1.0
17.0
37.0
19.0
25.0
47.0
52.0
59.0
51.0
6.0
59.0
1.0
57.0
42.0
37
43
41
41
33
37
45
61
40
55
33
46
40
63
34
27
43
48
43
37
62
37
49
21
41
29
46
32
32
58
39
33
30
46
51
52
42
58
53
54
32
55
39
33
20912185
17714166
11359809
13588645
4182608
25449976
470034
16100943
24632777
2863287
24108493
24032391
27841690
4217331
8166299
21519170
15798021
27027310
3208474
24068063
27216781
25922110
12083473
18642306
15799821
522469
23326942
24813663
24614746
27174609
7975265
29284445
23835489
19873471
2832605
28791886
8946352
9586093
6064144
16831983
29898521
12419195
21170016
23116435
47.8
15.0
83.5
75.4
95.3
104.9
92.2
54.0
87.7
34.6
41.2
60.0
102.8
41.3
45.8
45.1
91.2
81.8
38.1
62.4
36.1
43.8
67.2
47.6
50.1
116.9
74.4
109.1
108.1
94.5
65.0
70.1
99.5
46.4
53.6
93.5
112.5
96.5
98.8
45.6
104.5
33.2
36.8
32.0
2.0
8.0
6.0
1.0
1.0
6.0
2.0
6.0
6.0
4.0
8.0
6.0
6.0
2.0
3.0
2.0
3.0
6.0
1.0
6.0
5.0
7.0
4.0
6.0
2.0
4.0
5.0
2.0
7.0
7.0
3.0
5.0
4.0
3.0
2.0
7.0
5.0
5.0
1.0
4.0
2.0
4.0
3.0
6.0
1.0
1.0
4.0
4.0
5.0
3.0
5.0
2.0
0.0
4.0
2.0
2.0
5.0
4.0
0.0
4.0
4.0
4.0
2.0
1.0
3.0
3.0
4.0
5.0
0.0
1.0
4.0
4.0
0.0
2.0
4.0
1.0
2.0
2.0
4.0
1.0
4.0
2.0
5.0
4.0
4.0
3.0
2.0
5.0
42.0
29.0
20.0
4.0
20.0
48.0
5.0
52.0
53.0
53.0
54.0
54.0
47.0
49.0
50.0
60.0
26.0
37.0
10.0
48.0
33.0
42.0
57.0
28.0
53.0
39.0
28.0
48.0
49.0
39.0
49.0
7.0
16.0
20.0
20.0
6.0
50.0
35.0
51.0
24.0
28.0
9.0
60.0
16.0
51
43
35
58
48
59
64
44
29
62
57
29
46
44
62
34
29
44
39
34
61
35
55
37
61
58
47
35
41
48
42
52
31
54
60
48
54
34
24
39
27
34
35
64
3879735
4301232
13830546
7605648
7802284
23868893
10234698
25895270
4865561
2876697
21086787
14053343
14868163
18125641
20596328
23886945
17613761
20088003
25081553
8782240
5915423
27309256
11516648
15882749
16552992
21000261
29265684
5479388
264488
26794917
23131363
10867011
20965615
1471765
10345179
27230199
18112326
26113887
26595273
5142725
9519933
23921489
22153601
18211317
87.2
63.5
60.2
76.6
20.8
92.1
73.6
28.4
78.7
35.7
87.8
106.3
77.3
39.9
93.6
75.6
105.3
104.2
87.4
54.8
89.5
71.5
107.1
60.0
37.9
59.7
84.2
104.2
54.3
116.4
54.9
41.5
88.0
67.7
22.8
61.1
85.9
80.5
42.8
53.0
67.8
46.6
28.6
85.4
5.0
5.0
7.0
4.0
8.0
1.0
7.0
4.0
5.0
7.0
4.0
3.0
7.0
7.0
4.0
5.0
4.0
3.0
7.0
3.0
3.0
8.0
7.0
6.0
6.0
1.0
7.0
3.0
2.0
5.0
4.0
6.0
3.0
1.0
5.0
6.0
6.0
2.0
7.0
5.0
7.0
6.0
5.0
6.0
2.0
2.0
2.0
1.0
1.0
3.0
3.0
4.0
3.0
2.0
3.0
1.0
2.0
5.0
4.0
5.0
4.0
4.0
4.0
5.0
2.0
3.0
2.0
1.0
5.0
3.0
2.0
4.0
3.0
3.0
2.0
4.0
4.0
1.0
5.0
5.0
1.0
5.0
4.0
4.0
5.0
4.0
3.0
4.0
2.0
15.0
61.0
12.0
24.0
9.0
50.0
30.0
27.0
11.0
8.0
7.0
29.0
54.0
30.0
31.0
42.0
12.0
7.0
3.0
41.0
19.0
33.0
10.0
8.0
55.0
38.0
29.0
3.0
37.0
13.0
38.0
30.0
56.0
17.0
9.0
48.0
56.0
58.0
39.0
14.0
36.0
19.0
44.0
27
59
61
45
49
38
34
46
39
37
51
43
36
46
45
37
45
39
39
30
29
40
46
47
39
47
37
53
42
31
43
30
54
49
43
61
37
52
47
55
33
51
55
49
5349406
28414346
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25327791
24405475
11465863
18040814
7650734
23301122
2760522
10106427
24986269
21001445
63.8
84.5
53.4
14.3
85.7
51.7
47.5
97.2
48.4
92.7
57.3
74.7
96.0
55.0
131.7
42.0
25.2
47.3
51.9
41.1
26.9
116.8
66.2
127.1
42.9
40.3
105.5
44.2
83.5
45.8
100.4
106.0
62.3
114.3
63.9
62.7
84.3
48.2
72.5
38.9
52.2
42.9
104.5
85.5
8.0
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43.0
18.0
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43.0
21.0
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31
42
51
42
44
24
37
66
60
61
42
35
46
52
48
26
42
25
42
48
54
52
41
64
55
60
30
62
43
37
38
58
47
54
52
42
25
40
46
30
30
30
47
59
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27306192
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21699465
11237377
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220265
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25517016
98.2
64.5
107.8
85.7
73.8
90.0
75.5
51.4
107.7
18.3
59.4
30.4
54.4
103.2
34.1
88.8
53.5
22.4
71.0
55.3
68.5
99.7
48.9
107.3
42.1
42.0
81.6
64.0
45.9
79.6
100.2
105.9
69.3
68.5
51.1
55.8
66.2
103.1
55.3
85.0
53.7
90.1
29.2
92.3
7.0
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23.0
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16.0
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62.0
9.0
38.0
30.0
49.0
27.0
37.0
21.0
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27.0
45.0
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22.0
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56.0
53.0
29.0
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53.0
14.0
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42.0
52.0
55.0
10.0
53
42
33
51
24
60
59
42
50
41
36
67
37
42
32
47
52
56
45
38
50
62
42
36
40
48
52
51
42
44
48
50
51
47
50
40
29
57
50
34
51
41
31
49
18430470
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19872887
5322783
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22589206
11367433
3536867
72.1
73.8
48.4
45.0
77.2
55.2
117.2
46.6
8.0
108.0
56.1
95.3
53.2
38.9
78.4
41.0
99.9
90.0
79.3
24.6
75.1
61.6
92.2
120.7
48.7
97.4
44.5
94.6
9.9
79.3
98.1
51.9
89.2
70.2
27.5
106.7
98.4
104.1
45.2
82.7
46.8
27.0
83.1
17.5
4.0
7.0
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27.0
40.0
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24.0
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51.0
7.0
36.0
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43.0
38.0
9.0
45.0
57.0
41.0
10.0
51.0
27.0
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14.0
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56.0
44.0
41.0
35.0
45
41
50
63
38
56
55
64
36
52
36
45
58
49
40
30
57
65
22
36
50
52
37
55
51
44
56
33
57
59
27
56
65
34
43
46
22
31
34
38
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66
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5750686
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4708626
17315701
14965825
6911380
12967743
4791310
4573215
42.9
71.8
80.1
58.8
84.3
71.8
50.4
59.6
47.6
66.3
98.0
115.8
65.2
22.4
71.5
82.5
93.0
25.0
85.1
18.2
25.6
91.5
90.9
53.0
20.3
60.0
89.3
40.1
64.0
62.5
42.3
85.9
54.9
43.1
36.1
34.7
44.1
94.4
33.0
55.9
86.5
55.1
39.5
86.6
3.0
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39.0
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23.0
43.0
47.0
9.0
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32.0
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33.0
40.0
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25.0
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30.0
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58
43
50
59
51
48
32
49
39
24
40
57
34
56
36
54
46
41
53
27
61
39
35
54
45
37
56
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52
56
43
45
48
43
52
36
63
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2491879
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4490936
18463582
17236813
27338385
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16307426
2339312
42.6
69.3
31.8
72.5
52.4
15.0
52.0
34.4
117.3
71.5
106.1
114.4
29.8
47.2
36.9
80.2
87.8
83.8
75.3
42.7
25.8
71.7
91.5
73.1
31.6
55.7
102.7
39.3
72.0
53.8
46.4
50.5
93.0
50.1
44.6
54.7
113.4
41.6
55.4
29.5
104.7
93.5
31.5
107.1
5.0
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51
31
32
27
54
31
59
53
40
38
53
40
39
56
35
32
37
48
38
39
35
53
59
51
40
43
37
48
40
30
62
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44
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66
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40
35
22
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88.6
45.2
72.7
40.0
80.0
126.8
78.8
67.9
47.1
43.1
76.1
40.7
50.3
45.3
71.4
53.9
45.2
71.3
85.9
101.8
73.6
88.1
20.8
23.9
92.5
52.3
35.0
72.3
44.9
81.2
97.4
78.2
113.4
117.3
39.2
55.5
92.6
39.4
87.7
49.1
39.0
69.4
103.2
90.5
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23.0
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41.0
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44
34
67
26
47
54
22
38
56
34
37
56
32
52
40
56
56
43
50
42
51
48
60
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29
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22
51
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38
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24
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21437378
28616552
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11200269
6126958
5937769
17766925
100.2
46.9
122.9
75.4
101.0
75.6
120.4
114.1
34.6
73.3
57.4
87.1
90.5
110.4
81.1
74.6
25.2
15.2
39.9
44.7
82.4
65.2
101.4
54.3
51.6
68.2
49.9
75.7
43.3
109.6
5.8
39.7
52.5
30.7
25.0
114.5
73.6
75.0
65.8
76.0
78.9
45.9
112.7
96.4
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90.9
83.6
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78.7
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45.1
116.0
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34.7
67.9
20.2
50.2
36.2
62.1
68.9
24.5
55.7
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54.5
100.2
49.9
31.8
72.1
110.5
58.8
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85.3
50.2
42.5
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37.6
54.3
58.3
29.6
54.3
52.1
36.9
117.8
68.2
99.0
89.2
71.2
93.1
44.3
112.3
7.7
50.5
50.8
21.4
51.2
57.3
32.2
87.3
63.1
69.7
53.5
36.2
36.3
41.4
41.7
85.1
49.3
74.3
41.7
108.8
42.6
45.2
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32
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28
40
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28
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95.5
67.3
115.2
31.7
125.1
36.9
93.0
46.1
75.0
108.6
115.7
87.6
96.3
66.1
99.1
27.7
92.9
78.9
55.9
25.9
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44.7
44.2
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126.2
82.7
52.5
66.6
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87.8
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56.4
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102.5
110.9
32.4
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17.4
45.5
42.8
24.4
32.3
41.4
28.6
37.4
18.4
80.4
33.3
113.1
85.8
62.3
96.0
44.1
74.4
49.8
85.9
107.3
57.4
101.5
49.9
107.8
92.7
37.7
66.5
55.6
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95.7
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65.4
60.8
84.0
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89.2
123.4
46.4
105.7
31.6
109.2
100.8
99.0
78.8
65.5
107.1
108.9
50.9
77.4
93.6
24.6
83.5
42.9
124.1
70.8
70.4
101.0
71.6
74.2
29.4
50.6
37.1
23.0
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37
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58
70
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65
41
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90.8
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78.6
92.6
103.5
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91.9
112.5
10.7
46.2
113.0
54.7
28.5
103.3
101.4
18.4
26.1
31.7
44.8
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67.8
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62.2
82.7
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42.7
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17.6
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67.0
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35
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34
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21506343
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44.8
54.6
62.8
90.2
51.4
94.3
51.5
68.0
47.6
98.6
39.6
58.4
78.9
54.6
91.1
70.1
48.3
47.1
55.1
24.1
58.0
92.6
53.3
21.3
102.0
41.9
45.6
124.4
40.6
76.1
119.5
72.7
43.3
60.3
44.3
68.3
91.7
88.5
102.3
115.7
29.5
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36
38
66
52
52
43
31
30
38
42
61
32
49
57
50
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60
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99.0
119.3
112.1
38.0
34.9
69.8
123.5
91.7
79.8
28.8
48.8
35.5
99.5
87.9
76.4
44.7
92.4
42.9
33.0
57.1
99.6
58.8
54.0
105.8
35.3
46.5
52.6
23.6
33.5
64.2
40.6
97.3
110.2
26.7
48.0
71.1
53.1
53.8
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42
38
48
35
44
51
43
33
57
50
41
44
30
53
30
57
56
58
52
55
23
28
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40
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66
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53
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35
34
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51
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28
47
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26699258
7641702
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5310864
17394500
29.2
41.7
33.5
63.6
119.2
47.1
30.6
108.2
74.4
74.9
29.5
39.3
68.8
83.4
30.1
105.3
37.3
44.8
53.1
61.9
79.5
95.4
88.3
48.4
46.2
56.9
101.2
39.9
33.2
83.5
77.7
41.2
101.4
29.9
99.4
52.6
59.9
107.7
14.6
64.8
115.5
49.2
45.0
76.0
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49
39
36
28
28
35
41
40
67
48
20
61
22
43
20
52
49
32
24
47
32
44
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47
32
32
41
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26
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44
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7630045
91.4
92.6
108.1
86.2
87.6
90.7
57.5
79.9
59.1
62.9
113.2
92.3
59.7
93.3
46.2
49.4
57.5
73.3
51.7
59.0
84.7
18.3
14.5
102.3
63.8
33.7
35.4
101.3
81.6
115.5
95.0
62.5
102.4
93.3
30.5
47.7
84.4
47.6
61.7
35.4
10.0
121.8
97.1
80.7
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47
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56
41
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43
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36
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31.0
31.6
35.9
42.7
85.6
51.9
53.6
48.1
27.2
54.5
81.3
33.5
60.9
91.9
46.7
59.5
78.6
42.9
53.5
72.7
109.2
86.1
17.9
81.1
51.7
73.3
39.6
72.0
75.2
47.0
46.1
77.7
98.2
89.8
71.4
64.5
111.9
82.8
92.9
43.0
58.8
99.8
46.2
66.7
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43
49
47
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39
57
57
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60
28
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48
51
41
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40.6
100.3
46.7
124.1
41.1
48.9
45.1
35.2
91.6
57.0
81.9
72.4
51.2
27.9
98.6
89.6
42.3
67.9
99.3
58.8
92.5
48.6
54.0
100.2
84.9
93.4
53.1
60.2
29.5
110.4
97.3
61.6
16.0
107.8
67.5
26.8
96.5
65.3
11.8
88.7
87.1
63.2
51.5
39.7
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41
38
50
49
43
64
32
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30
56
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67
40
43
24
61
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28854864
83.5
35.3
110.5
20.3
124.6
41.6
27.6
76.1
43.6
44.7
108.8
46.2
94.9
89.2
52.4
30.2
53.1
51.1
97.9
35.4
49.1
49.0
114.6
33.3
107.5
116.1
44.9
103.7
77.7
39.4
109.2
106.4
54.8
84.6
85.5
15.3
31.6
79.9
29.5
54.7
84.3
99.8
31.6
67.1
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53.9
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86.1
104.1
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32
56
46
58
31
43
49
34
62
57
41
49
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36
60
58
59
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38
61
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51
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33
49
32
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31
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23
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13828681
41.9
103.2
56.6
71.6
39.1
95.6
75.3
55.6
31.8
23.7
39.9
113.7
49.5
103.9
34.2
49.1
50.0
74.5
16.6
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97.0
59.5
87.5
74.2
58.5
62.6
72.3
96.7
90.8
43.5
63.4
116.1
101.8
83.2
99.8
107.0
28.1
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44.7
54.5
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26
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36
35
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51
30
30
56
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28.7
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41.5
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45.8
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104.0
16.9
58.2
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94.8
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49.7
43.5
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96.9
30.6
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55.2
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53.5
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76.3
40.4
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35
45
25
45
35
53
45
35
51
35
50
33
62
53
55
39
55
32
33
50
22
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38
56
44
45
47
26
58
45
34
43
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55
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50
38
34
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66
55
63
41
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74.5
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37.4
91.9
74.4
73.0
45.1
60.9
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31.3
57.6
25.7
34.7
124.3
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93.1
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57.7
70.0
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42
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32
29
36
26
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56
66
62
63
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4809697
20181148
55.5
123.1
87.2
50.6
101.1
44.1
48.0
36.1
83.1
82.5
90.1
59.1
77.0
33.3
38.9
45.0
56.0
22.6
92.6
100.9
67.4
94.6
52.1
47.0
87.9
17.5
59.3
65.2
52.5
32.9
76.4
44.2
95.4
84.9
114.4
84.5
31.7
100.0
126.1
23.3
52.8
34.2
95.7
54.2
7.0
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44
69
30
55
41
27
53
52
49
59
56
35
24
55
63
50
50
46
38
50
48
45
51
39
38
37
41
58
45
44
34
48
48
25
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44
29
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39
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28838347
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23803814
89.0
53.6
66.8
30.5
38.2
101.7
121.1
46.5
72.3
99.8
85.4
77.9
56.3
76.6
73.8
63.5
115.4
102.4
59.5
109.7
64.6
63.2
42.6
99.1
123.9
98.0
81.8
82.6
82.4
93.5
102.1
91.3
37.7
12.2
87.2
53.7
115.1
70.9
44.1
118.3
91.4
46.9
60.0
48.4
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51
56
28
47
31
35
48
43
46
42
43
42
44
32
36
28
28
30
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53.6
61.3
66.3
49.4
96.9
65.5
84.9
84.8
84.6
82.0
31.7
91.8
93.1
100.2
89.7
91.7
119.1
87.0
102.2
54.9
68.8
38.5
46.6
47.7
37.2
79.5
86.0
64.4
77.4
108.0
87.7
84.0
51.5
68.5
13.2
129.2
78.0
27.2
39.4
27.3
23.8
68.3
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23.0
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39.0
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40.0
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42.0
15.0
43.0
43.0
58.0
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1.0
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37.0
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39
58
52
48
37
46
54
31
44
59
44
51
35
57
37
44
47
56
59
34
43
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39
23
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66
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33
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20
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24092269
20042424
24422650
8213802
29522157
2360146
90.4
83.4
76.1
53.9
64.7
119.6
44.3
53.4
54.8
120.8
119.4
52.5
69.0
82.9
83.6
42.9
50.1
107.3
79.8
97.0
102.6
66.6
58.6
45.0
110.9
38.6
117.5
22.8
101.1
118.3
24.6
70.7
87.5
41.6
91.5
48.9
84.0
68.1
73.2
65.5
57.3
48.8
73.9
111.5
2.0
4.0
7.0
1.0
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5.0
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6177513
14936822
16657910
20419788
22763378
18059037
19170214
6623698
6360167
129.8
71.4
104.4
28.3
79.4
32.7
45.3
22.0
91.2
52.7
61.3
98.9
44.3
38.7
92.9
74.2
100.4
122.3
69.2
101.9
56.2
104.7
110.0
115.5
104.1
53.5
70.6
52.6
49.7
97.3
96.1
74.9
79.9
92.4
103.2
79.7
28.0
10.1
29.5
58.4
86.4
78.4
114.4
72.7
6.0
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17.0
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62.0
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49.0
21.0
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61
52
50
62
37
48
39
54
39
37
61
37
67
56
35
43
46
58
54
48
53
53
39
40
62
42
43
51
60
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25
57
44
37
46
33
52
42
65
44
63
65
56
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25529096
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22037205
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21254283
5708520
13819277
27795592
21748292
46.8
40.9
75.2
73.2
101.0
60.5
64.5
93.7
56.3
46.3
100.2
84.2
101.6
62.6
42.9
67.9
56.8
70.4
61.2
57.8
63.5
45.9
71.5
51.7
44.3
49.7
64.6
98.2
73.0
54.9
44.3
45.3
43.7
46.3
94.8
94.5
49.9
46.0
56.0
91.2
102.4
28.0
51.8
79.3
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51
46
33
50
34
41
44
67
32
50
47
48
44
45
44
47
42
62
54
29
36
21
37
56
62
42
27
28
61
54
25
34
42
39
41
52
48
31
24
28
48
34
33
2459615
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4212180
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94.2
24.6
44.8
79.8
71.1
95.3
34.1
96.9
50.0
49.9
31.6
81.7
59.0
84.4
69.2
35.9
103.7
68.4
59.8
44.5
96.1
61.9
37.9
36.3
60.4
26.7
28.4
72.7
101.8
53.0
116.0
92.2
55.7
126.5
108.3
55.6
130.7
75.2
58.3
55.6
44.4
71.8
116.7
41.5
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34
47
39
58
25
36
49
43
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54
53
62
47
32
67
62
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27
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56.5
87.5
92.1
70.7
93.9
80.1
60.5
66.8
63.5
121.1
50.2
39.6
88.8
101.2
52.0
100.8
70.6
88.9
89.4
82.7
41.0
68.5
109.2
98.5
28.6
61.5
41.3
116.6
110.2
44.2
79.0
49.5
120.5
45.6
60.6
69.9
48.1
44.4
46.2
53.7
79.0
48.9
45.2
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49
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39
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26
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62
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113.2
64.0
45.5
103.5
129.4
50.5
61.1
111.9
53.3
37.5
95.7
104.4
95.2
17.9
67.0
45.0
17.0
56.0
83.9
44.8
36.0
45.6
60.4
9.1
96.2
107.1
104.0
108.2
84.5
117.9
62.4
40.1
58.9
33.6
38.4
24.8
72.4
109.7
74.0
63.0
80.7
38.2
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63
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41
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29
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24
44
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30
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27
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79.4
97.0
118.3
74.7
88.7
83.2
51.3
44.8
81.7
47.6
101.5
77.5
93.1
39.0
105.3
38.1
109.2
113.0
21.6
72.9
41.9
103.3
108.0
52.1
80.1
98.8
21.7
119.9
85.4
24.0
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45.4
72.7
21.5
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107.1
94.6
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91.0
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95.6
33.6
101.7
23.3
28.0
50.3
77.5
44.7
74.2
53.4
79.3
91.2
98.9
23.4
43.0
54.6
94.1
46.7
85.2
98.8
80.3
52.5
93.9
102.2
52.0
69.7
37.4
51.9
14.6
37.1
35.4
70.9
58.9
77.0
44.7
78.5
48.8
78.3
50.6
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104.8
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63.6
57.2
43.7
46.3
85.9
92.4
67.9
119.3
50.3
81.8
40.5
88.2
126.8
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69.9
113.9
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81.0
113.1
56.4
40.5
112.8
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30.1
49.3
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112.1
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33.2
35.4
39.3
73.1
108.6
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66.8
96.7
115.0
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41.6
41.6
38.4
41.0
30.2
39.3
110.2
122.1
54.3
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33.7
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28878193
21869…
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