EXCEL HOMEWORK ATTACHING THE FILE! 4-19 on page 141. 4-30 on page 143. 4-32 on pages 143-144. 5-28 on page 179. 5-35 on page 180. 4-19 on page 141. 4-30 on page 143. 4-32 on pages 143-144. 5-28 on p

EXCEL HOMEWORK ATTACHING THE FILE!4-19 on page 141.4-30 on page 143.4-32 on pages 143-144.5-28 on page 179.5-35 on page 180.4-19 on page 141.4-30 on page 143.4-32 on pages 143-144.5-28 on page 179.5-35 on page 180.4-19 Bus and subway ridership in Washington, D.C., during the summer months is believed to be heavilytied to the number of tourists visiting the city. During the past 12 years, the following data have beenobtained:(a) Plot these data and determine whether a linear model is reasonable.(b) Develop a regression model.(c) What is expected ridership if 10 million tourists visit the city?YEAR NUMBER OFTOURISTS(1,000,000s)RIDERSHIP(1000,000’S)1 7 152 2 103 6 134 4 155 14 256 15 277 16 248 12 209 14 2710 20 4411 15 3412 7 174-30 In 2012, the total payroll for the New York Yankees was almost $200 million, while the totalpayroll for the Oakland Athletics (a team known for using baseball analytics or sabermetrics) was about$55 million, less than one-third of the Yankees’ payroll. In the following table, you will see the payrolls (inmillions) and the total number of victories for the baseball teams in the American League in the 2012season. Develop a regression model to predict the total number of victories based on the payroll. Usethe model to predict the number of victories for a team with a payroll of $79 million. Based on theresults of the computer output, discuss the relationship be-tween payroll and victories.Team Payroll ($1,000,000s) Number of VictoriesBaltimore Orioles 81.4 93Boston Red Sox 173.2 69Chicago White Sox 96.9 85Cleveland Indians 78.4 68Detroit Tigers 132.3 88Kansas City Royals 60.9 72Los Angeles Angels 154.5 89Minnesota Twins 94.1 66New York Yankees 198.0 95Oakland Athletics 55.4 94Seattle Mariners 82.0 75Tampa Bay Rays 64.2 90Texas Rangers 120.5 93Toronto Blue Jays 75.5 734-32 The closing stock price for each of two stocks was recorded over a 12-month period. Theclosing price for the Dow Jones Industrial Average (DJIA) was also recorded over this same time period.These values are shown in the following table:MONTH DIJA STOCK 1 STOCK 21 11,168 48.5 32.42 11,150 48.2 31.73 11,186 44.5 31.94 11,381 44.7 36.65 11,679 49.3 36.76 12,081 49.3 38.77 12,222 46.1 39.58 12,463 46.2 41.29 12,622 47.7 43.310 12,269 48.3 39.411 12,354 47.0 40.112 13,063 47.9 42.113 13,326 47.8 45.2(a) Develop a regression model to predict the price of stock 1 based on the Dow Jones IndustrialAverage.(b) Develop a regression model to predict the price of stock 2 based on the Dow Jones IndustrialAverage.(c) Which of the two stocks is most highly correlated to the Dow Jones Industrial Average over this timeperiod?5-28 Sales of industrial vacuum cleaners at R. Lowenthal Supply Co. over the past 13 months are asfollows:SALES($1,000s)MONTH SALES($1,000s)MONTHS11 January 14 August14 February 17 September16 March 12 October10 April 14 November15 May 16 December17 June 11 January11 July(a) Using a moving average with three periods, determine the demand for vacuum cleaners for nextFebruary.(b) Using a weighted moving average with three periods, determine the demand for vacuum cleaners forFebruary. Use 3, 2, and 1 for the weights of the most recent, second most recent, and third most recentperiods, respectively. For example, if you were forecasting the demand for February, November wouldhave a weight of 1, December would have a weight of 2, and January would have a weight of 3.(c) Evaluate the accuracy of each of these methods.(d) What other factors might R. Lowenthal consider in forecasting sales?5-35 A major source of revenue in Texas is a state sales tax on certain types of goods and services.Data are com-piled, and the state comptroller uses them to project future revenues for the state budget.One particular category of goods is classified as Retail Trade. Four years of quarterly data (in$1,000,000s) for one particular area of southeast Texas follow:QUARTER YEAR 1 YEAR 2 YEAR 3 YEAR 41 218 225 234 2502 247 254 265 2833 243 255 264 2894 292 299 327 356(a) Compute a seasonal index for each quarter based on a CMA.(b) Deseasonalize the data and develop a trend line on the deseasonalized data.(c) Use the trend line to forecast the sales for each quarter of year 5.(d) Use the seasonal indices to adjust the forecasts found in part (c) to obtain the final forecasts.

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