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- Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.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?The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.
- .A 45 kW rated solar power system has its power output and the Solar irradiance on the PV system measured during daylight hours over a day. Based on the following data, create a scatter plot of the power output of the PV system vs the solar irradiance and comment on the relationship between the two. Based on the data, can you tell which season this day was in? Solar Irradiance on the PV system (kW/m²) 0 0.079 0.333 0.571 0.751 0.86 0.914 0.93 0.904 0.803 0.606 0.345 0.054 Power output from the PV system (kW) 0 3.098 12.438 20.124 25.703 28.498 29.587 29.69 28.798 26.264 20.434 12.223 2For 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.The manager of Mayo Department Store want to evaluatethe relationship between the sales of air conditioners andthe average weekly temperature. The following weeklysales data along with the average daytime temperaturesfor 10 one-week periods are shown in the following table:Week Air-conditioner Sales(in units) Average Temperature (in degrees Fahrenheit)1 100 782 85 703 103 854 110 905 70 756 115 947 150 1018 120 989 97 8610 85 80 a Develop a linear regression equation for air conditionersales as a function of the average weekly temperature.b Forecast air conditioner sales if the average weeklytemperature is 88 degrees Fahrenheit.c Evaluate the "goodness of fit" of the regressionequation by…
- 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? a. 200 (rounds to) b. 215 (rounds to) c. 230 (rounds to) d. 240 (rounds to)Use the below formula to calculate the CLV for the following: A manager of a cable company wants to determine if it is strategic to acquire the Brett family, by estimating their household-level CLV. The manager estimates that it will cost the company $80 (A) to get the Bretts’ to switch, and the Bretts’ will generate $150 profit each year (M), with a $30 annual marketing cost to retain them (C). The estimated retention rate (r) is 65%, and the current discount rate is 5%.(d) i) CLV= ii) Based on your calculation, are the Brett’s profitable to the cable company?The total number of absents students in the four (4) school terms for a matric class at a high school over a 4-year period are recorded and tabulated as shown below. Year Term Number of absents 1 50 2018 2 38 16 4 1 67 2019 2 40 12 4 8 1 128 2020 95 37 4 20 1 149 2021 2 133 3 78 4 55 Calculate the first centered moving average for this time series data. A. 27.25 В. 29.375 С. 31.5 D. 32
- Attendance at Orlando's newest Disneylike attraction, Lego World, has been as follows Quarter Guests (in thousands) Quarter Summer Year 2 Winter Year 1 58 99 Fall Year 2 149 Winter Year 3 Spring Year 1 Summer Year 1 Fal Year 1 Winter Year 2 75 Spring Year 3 63 Summer Year 3 Spring Year 21 82 Fall Year 3 Based on the given attendance, the seasonal indices for each of the seasons are (round your responses to three decimal places) Season Index Winter Spring Summer Fall 0000 Guests (in thousands) 125 54 99 146 210 99The number of internal disk drives (in millions) made at a plant in Taiwan during the past 5 years follows: Year Disk Drives 1 138 2 156 3 184 4 202 220 a) Using simple linear regression, the forecast for the number of disk drives to be made next year = 211 disk drives (round your response to one decimal place). b) The mean squared error (MSE) when using simple linear regression = drives- (round your response to one decimal place). c) The mean absolute percentage error (MAPE) when using simple linear regression = % (round your response to one decimal %3D place).The number of internal disk drives (in million) made at a plant in Taiwan during the past 5 years follows: Year Disk Drives 1 142 2 156 3 184 4 204 5 210 a) Using simple linear regression the forecast for the number of disk drives to be made next year= 234.4 disk drives b) The mean squared error (mse) when using simple linear regression=[___] drives^2 (round your response to one decimal place)