linear regression model, you've found the following relationship:
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You work as a sales operations analyst in a company that makes 3D printers. Your manager has asked you to determine if a salesperson's sales volume (in terms of the number of 3D printers they sell in a year) depends on the number of client calls they make. After analyzing past data and creating a linear regression model, you've found the following relationship:
No. of printers sold = 18.47 + 1.13 times the number of client calls.
1. Based on this, how many client calls will a salesperson need to make to sell 245 printers next year?
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- Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.From the following annual data of sales (in 000 .$.) Find the trend values by using least square method. Also estimate the sales of 2014. Year 2004 2005 2006 2007 2008 2009 2010 Sales {In 000 $} 77 88 94 85 91 98 90The following table shows the number of televisions sold over the last ten years at a local electronic store. Year TV Sales 1. 150 2. 300 3. 480 4. 600 5. 630 6. 640 7. 700 8. 825 9. 900 10. 980 Using trend projection, develop a formula to predict sales for years 11 and 12. You have to show all working. You will need to develop a table to calculate the slope and the intercept. [9 marks] 2. Use that formula to forecast television sales for years 11 and 12. [2 marks]
- year quarterly sales (000 units) Q1 Q2 Q3 Q4 2016 1300 1500 1200 2000 2017 1600 1800 1100 2200 2018 1700 1900 1300 2300 2019 1800 2100 1400 2500 Using a simple regression analysis, determine the trend equation of the sales and use it to estimate the number of units of clothing sold throughout the fiscal year 2020. Assume that Q1 of 2016 is 1, Q2 of 2016 is 2, etc. Show all relevant cakculation detailRegression line. A large midwestern retailer has developed a graph that summarizes the effect of advertising expenditures on sales volume. Using the graph, determine an equation of the form y = a + bx that describes this relationship.cosmetics manufacturer's marketing department has developed a linear trend equation that can be used to predict annual sales of its popular Hand&Foot Cream. F t = 80 + 15t where Ft = Annual sales (000 bottles) t is in years Are annual sales increasing or decreasing? By how much?
- Please answer parts i, ii and iii of the below question. The 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:- i) A simple three month moving average. ii) A three period weighted moving average using weights of 1, 2 and 3. Assign the highest weight to the most recent data. iii) Exponential Smoothing when α= .6 and the forecast for March is 350.A pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales duringthe last 15 days wereDay: 1 2 3 4 5 6 7 8 9Number sold: 36 38 42 44 48 49 50 49 52Day: 10 11 12 13 14 15Number sold: 48 52 55 54 56 57a. Using linear trend equation method, predict future sales from the given data.b. If you learn that on some days the store ran out of the specific pain reliever, would that knowledge cause you any concern? Explain.Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks? Week 1 2 3 4 5 Sales 150 157 162 166 177
- The following data are for calculator sales in units at an electronics store over the past nine weeks: Week Sales Week Sales 45 6 53 50 7 59 44 8 59 51 64 57 Use trend projection with regression to forecast sales for weeks 10 - 13. What are the error measures (CFE, MSE, G. MAD, and MAPE) for this forecasting procedure? How about ?? Obtain the trend projection with regression forecast for weeks 10 - 13. (Enter your responses rounded to two decimal places.) Period Forecast, F 10 64.81 11 67.06 12 69.31 13 71.56 Obtain the error measures. (Enter your responses rounded to two decimal places.) CFE MSE MAD MAPE 6.72 2.75 2.1 4.1 % Find the coefficient of determination (). The coefficient of determination ? = (Enter your response rounded to two decimal places.)After graduation, you take a position at Top-Slice, a well-known manufacturer of golf balls. One of your duties is to forecast monthly demand for golf balls. Using the following data, you developed a regression model that expresses monthly sales as a function of average temperature for the month: Monthly sales=-202.2+86.5x (average temperature) Click the icon to view the additional data. a. Show how the a and 6 values of -202.2 and 86.5 were calculated. Calculate the slope coefficient, b (enter your responses as whole numbers). (745) 12 12 2 =86.5 More Info March 2017 April May June July August September October November December January 2018 February Monthly Sales 5,300 3,990 5,700 6,520 7,600 7,420 Print 6,710 4,870 3,990 3,900 3,320 2,670 Temperature 51 56 Done 64 80 79 87 83 67 58 41 39 40 XRefer again to the gasoline sales time series data in the following table. Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent, and 1/6 for third most recent, compute a three-week weighted moving average for the time series. Use rounded for two decimal places values for intermediate calculations. Use minus sign for negative values. Compute the MSE for the weighted moving average in part a. Do you prefer this weighted moving average to the unweighted moving average? Remember that the MSE for the unweighted moving average is 10.22. Round your answer to two decimal places. Suppose you are allowed to choose any weights as long as they sum to 1. Could you always find a set of weights that would make the MSE smaller for a weighted moving average than for an unweighted moving average? Why or why not?