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- The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?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 detailPlease 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.
- 3. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.Quarterly sales of a local department store over a 4-year period are given below: Quarters 2016 Q1 Sales (in millions) 500 Quarters 2018 Q1 Sales (in millions) 560 2016 Q2 490 580 2018 Q2 2018 Q3 2018 Q4 2019 Q1 2019 Q2 2016 Q3 570 630 2016 Q4 2017 Q1 2017 Q2 750 720 480 540 500 590 2017 Q3 580 2019 Q3 620 2017 Q4 740 2019 Q4 790 The data shows sign of seasonality. By computing seasonal indices for each quarter, forecast the sales for each quarter in year 2020, given that the estimated sales in 2020 isThe following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. QUARTER SALES 1 215 2 240 3 205 4 190 5 160 6 195 7 150 8 140 Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to 1 decimal place.
- Develop Linear Regression Model. Forecast sales for 8, 14 and 17 years of education. Sales(1000s) Years of Education 24 10 18 9 20 11 22 6 26 13 23 16 21 18The 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.1- A pharmacist has been monitoring sales of certain over-the-counter pain reliever. Daily sales during the last 15 days were as follows: Day 1 2 3 4 7 8 9 Number sold 36 38 42 44 48 49 50 49 52 Day 10 11 12 13 14 15 Number sold 48 52 55 54 56 57 a: By using linear regression method predict the future sales for the next 5 days (please do all calculations in excel and send the excel file with answers included as your response) b: calculate the MSE for the prediction of these 15 days. (again by using Excel) 1- Explain how QFD can be employed for designing a new restaurant in your region in Iraq? (at least 100 words) 2- A producer of inkjet printers is planning to add a new production line of printers, and you have been asked to balance the process, given the following task times and precedence relationships. Assume the cycle time is to be minimum possible (based on the bottleneck). Immediate Length (minutes) Task (Predecessor) a 0.2 b 0.4 a 0.3 d 1.3 b, c e 0.1 f 0.8 e g 0.3 d, f 1.2 g a…
- Year Season Sales 2018 Winter 40 2018 Spring 29 2018 Summer 31 2018 Fall 40 2019 Winter 102 2019 Spring 87 2019 Summer 96 2019 Fall 132 2020 Winter 105 2020 Spring 93 2020 Summer 105 2020 Fall 117 2021 Winter 141 2021 Spring 39 2021 Summer 114 2021 Fall 123 What is the slope of the trend equation obtained by linear regression? Round to two decimal digits. What is the intercept of the trend equation obtained by linear regression? Round to two decimal digits. What is the seasonal index for Spring? Round to two decimal digits. The quarter number for Winter of 2018 is 1. What is the quarter number for Spring of 2025? What is the trend based forecast for Spring of 2025. Round to a whole number. What is the seasonally adjusted trend based forecast for Spring of 2025? Please do not use excel to find the slope and intercept, thank you so much!The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. QUARTER 1 2 5678AWN 3 4 8 SALES 219 237 208 171 169 Quarter 9 10 11 12 192 157 132 Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to the nearest whole number. ForecastGlobal Bikes Inc. (a bicylce manufacturing firm) collected the following data during a study of consumer buying patterns. Observation 1 2 3 4 5 6 7 y= X y 15 70 28 79 38 89 35 86 49 97 42 97 30 82 y = Observation 0.721 + 1890123 a. Create a linear regression line (equation) for the data.(Round your Intermediate calculations and final answers to 3 decimal places.) X 14 16 14 24 21 29 63.229 X y 82 70 68 84 83 91 b. Use the equation determined in part b to predict the expected value of y for x = 49. (Round your Intermediate calculations and final answers to 3 decimal places.)
