In a short paragraph, describe the trend and seasonal variation of the number of customers (in thousand)in this coffee shop by referring to the following time series plot:(b) Find the trend by calculating the 4-period moving average.(Present your work in a table format and correct all answers to 4 decimal places.) The table below shows information about the number of customers (in thousand) of a coffee shop per quarterin years 2018 to 2020.Year2018201920201 2 312 30.9 | 0.711.6| 1.2 0.9 2.3Quarter44234Number of customers 1.21.81.51.10.8 2.1(a) In a short paragraph, describe the trend and seasonal variation of the number of customers (in thousand)in this coffee shop by referring to the following time series plot:Number of Customers (in thousand)2.62.42.21.81.61.41.20.80.60.40.2201820182018201820192019201920192020202020202020Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4(b) Find the trend by calculating the 4-period moving average.(Present your work in a table format and correct all answers to 4 decimal places.)

Find the Given details below: Given details: Year Quarters 1 2 3 4 2018 1.2 0.9 0.7 1.8…

The table below shows information about the number of customers (in thousand) of a coffee shop per quarter in years 2018 to 2020. Year 2018 2019 2020 1 2 3 4|1 2 3 41 2| 3 | 4 Number of customers 1.2 0.9 | 0.7 | 1.8 | 1.5 1.1| 0.8 | 2.1| 1.6| 1.2 0.9 | 2.3 Quarter (a) In a short paragraph, describe the trend and seasonal variation of the number of customers (in thousand) in this coffee shop by referring to the following time series plot: Number of Customers (in thousand) 2.6 2.4 2.2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 2018 2018 2018 2018 2019 2019 2019 2019 2020 2020 2020 2020 Q1 02 03 Q4 Q1 02 Q3 Q4 Q1 Q2 Q3 Q4 (b) Find the trend by calculating the 4-period moving average. (Present your work in a table format and correct all answers to 4 decimal places.)

The table below shows information about the number of customers (in thousand) of a coffee shop per quarter in years 2018 to 2020. Year 2018 2019 2020 1 2 3 4|1 2 3 41 2| 3 | 4 Number of customers 1.2 0.9 | 0.7 | 1.8 | 1.5 1.1| 0.8 | 2.1| 1.6| 1.2 0.9 | 2.3 Quarter (a) In a short paragraph, describe the trend and seasonal variation of the number of customers (in thousand) in this coffee shop by referring to the following time series plot: Number of Customers (in thousand) 2.6 2.4 2.2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 2018 2018 2018 2018 2019 2019 2019 2019 2020 2020 2020 2020 Q1 02 03 Q4 Q1 02 Q3 Q4 Q1 Q2 Q3 Q4 (b) Find the trend by calculating the 4-period moving average. (Present your work in a table format and correct all answers to 4 decimal places.)

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

See similar textbooks

Similar questions

Under what conditions might a firm use multiple forecasting methods?
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 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?
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
Following data on the demand for sewing machines manufactured by Taylor and Son Co. have been compiled for the past 10 years. Year 1971 1972 1973 1974 1975 78 1976 1977 1978 1979 99 1980 106 Demand 58 65 73 76 87 88 93 in (1000 units) Please estimate the value of demand for next 3 years using trend analysis.
The quarterly sales in millions for AutoKo Corporation, an automobile dealership company over the past three years are given in the following table. Compute for the seasonal factor of Quarter 3. YEAR 1 YEAR 2 YEAR 3 Quarter 1 1710 1820 1830 Quarter 2 960 910 1090 Quarter 3 2720 2840 2900 Quarter 4 2430 2200 2590
For 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 number of patients coming to the Healthy Start Maternity clinic has been increasing steadily over the past eight months. You are provided with some historical data as follows:Month Clinic attendance (in thousands) 1 3.42 3.93 4.54 5.05 5.86 5.97 6.58 6.7a. Identify and give a brief explanation of the:-i. Dependent variableii. Independent variable b. Use simple linear regression to forecast annual demand for months 9 and 10 by using the tabular method to:-i. Derive the values for the intercept and slope. ii. Derive the linear regression equation.iii. Plot the linear regression line. iv. Develop a forecast of the clinic attendance for months 9 and 10.
The director of the Riley County, Kansas, library system would like to forecast evening patron usage for next week. Below are the data for the past 4 weeks: Mon Tue Wed Thu Fri Sat Week 1 210 178 250 215 160 180 Week 2 215 180 250 213 165 185 Week 3 220 176 260 220 175 190 Week 4 225 178 260 225 176 190 a) Calculate a seasonal index for each day of the week (enter your responses rounded to three decimal places). Day of the week Mon Tue Wed Thu Fri Sat Seasonal index
The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that.Ft = 40 – 6.5t + 2t2whereFt = UnitSales t = 0 at, the first quarter of last yearQuarter Relative1 1.12 1.03 .64 1.3
The table below shows the sales for a plastics manufacturer recorded over the past year. The seasonal indexes for each quarter are also provided). To track the trend for these four quarters, use the indexes to deseasonalize the sales data. Quarter 1 Sales 738 Index 75.0 Deseasonalized Sales 234 1012 104.0 1196 121.0 962 100.0 What are deseasonalized sales for quarter 2? 973.1 1052.5 1291.5 1012.0 1027.7