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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?A random sample of 10,000 customers was selected over a six month period. The data provides information on their current status with the bank as well as 12 additional attributes describing their demographic and banking information. what are the key predictors the bank should be aware of with their customers? Examples are included, but not limited to:Are female customers leaving more than males? Is there indication of customer departures associated with specific countries?Is there evidence of salary associated departures? what is an appropiate marketing strategy to attract and retain long term customersFor the E-Commerce Retail Sales (Million$) data given in the table below, provide estimates from the 1st Quarter (Q1) of 2016 to the 3rd Quarter (Q3) of 2017 by using two models: 1) Single Exponential Smoothing with α=0.3 2) Moving Average with k=2. Calculate MAPE for each model. Quarter Year Actual Q1 2016 86802 Q2 2016 92004 Q3 2016 93795 Q4 2016 124651 Q1 2017 99491 Q2 2017 106590 Q3 2017 108291 Compare two models above (Single Exponential Smoothing and Moving Average) based on their accuracies. Which forecasting method appears to be better? Using the model you choose, provide forecast for the 4th Quarter (Q4) of 2017. Assuming that this model is overestimating, find the actual value of the Q4 of 2017 based on MAPE value. Some residual graphs from the first forecast model (including wider range of E-Commerce Retail Sales data) are given below. What do these graphs tell about the model? Explain each graph.
- Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month. Use the following weights: 0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 2 388 341.4 3 344 368 4 400 342.8 5 341 378.4The following table shows 6 students' tea consumption and their income. Find the coefficient for the impact of income on tea consumption using simple linear regression. Observations Tea Consumption Income 90 1 2 3 4 5 сл 6 18 14 20 25 8 12 100 80 60 100 110The table below shows the sales figures for a brand of shoe over the last 12 months. Months Sales January 69 February 75 March 86 April 92 May 95 June 100 July 108 August 115 September 125 October 131 November 140 December 150 Using the following, forecast the sales for the months up to January the following year:- 1. A simple three-month moving average. 2. A three-period weighted moving average using weights of 1, 2, and 3. Assign the highest weight to the most recent data. 3. Exponential Smoothing when α= .6 and the forecast for March is 350. 4. Determine which of the three forecasting techniques is the most accurate using MAD.
- The Ladies Professional Golf Association (LPGA) maintains data on performance for members of the LPGA Tour. Scoring average is generally considered the most important statistic in term of a player’s success. To investigate the relationship between scoring average and variables such as driving distance, driving accuracy, greens in regulation, sand saves, and average putts per round, year-end performance data for 140 players on the LPGA Tour for 2012 are contained in the DATAfile named TourLPGA2012 (LPGA website). Each row of the data set corresponds to a LPGA Tour player. Descriptions for the variables in the data set follow. Scoring Average The average number of strokes per completed round. DrDist (Driving Distance) The average number of yards per measured drive. On the LPGA Tour driving distance is measured on two holes per round. Care is taken to select two holes which face in opposite directions to counteract the effect of wind. Drives are measured to the point at which they come to…Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances Demand for Guitars 4 Y = 3 4 5 D 5 6 7 7 7 4 10 6 This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): = 0+0xThe Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the anticipated mileage to be driven next year. The miles driven during the past 5 years are as follows: Year Mileage 1 3,000 2 4,050 3 3,450 4 5 3,800 3,800 miles (round your response to the nearest a) Using a 2-year moving average, the forecast for year 6 = whole number).
- Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (e, afraid to leave their homes). The following table indicates how many patients Dr. Fokhas seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year Year Number of Patients Robbery Rate per 1,000 Population The simple linear regression equation that shows the best relationship between the number of patients and year is (round your responses to three decimal places where y Dependent Variable and x= Independent Variable 1 2 37 32 58.0 60.6 3 39 73.0 4 41 75.2 5 6 40 56 78.5 89.5 7 58 8 53 57 101.1 94.2 102.2 9 10 61 117.2 saveThe Tech power plant burns coal and natural gas to generate steam and electricity for all campus buildings. The annual coal consumption at the plant has increased annually for the past 6 years due to an aggressive campus building program. The following table shows the annual coal consumption: Year Coal Consumption (tons) 1 25000 2 23000 3 28000 4 35000 5 37000 6 42000 Develop a linear trend model to forecast coal consumption for the 7 th year. Don't answer by pen paper and don't use chatgpt otherwise we will give dounvoteThe table below summarizes September 20223 financial performance by two market segments. The dates highlighted in orange indicate occupancy higher than 90% Please find the correct statement about the data analysis based on the table. Market segment 1 shows pattern of peak on Weekdays. Market segment 2 shows a pattern of peak on Weekends. Based on the demand pattern, Market Segment 2 could be leisure travelers. Most guests in the market segment 2 are less price sensitive than those in the market segment 1.