The following table shows a tool and die company’s quarterly sales for the current year. What sales would you predict for the first quarter of next year? Quarter relatives are SR1 = 1.10, SR2 = .99, SR3 = .90, and SR4 = 1.01.Quarter 1 2 3 4Sales 88 99 108 141.4
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The following table shows a tool and die company’s quarterly sales for the current year. What sales would you predict for the first quarter of next year? Quarter relatives are SR1 = 1.10, SR2 = .99, SR3 = .90, and SR4 = 1.01.
Quarter 1 2 3 4
Sales 88 99 108 141.4
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- To better plan for future growth of the restaurant, Karen needs to develop a system that will enable her to forecast food and beverage sales by month for up to one year in advance. Table shows the value of food and beverage sales ($1000s) for the first three years of operation. Food and Beverage Sales for the Vintage Restaurant ($1000s) Month First Year Second Year Third Year January 242 263 282 February 235 238 255 March 232 247 265 April 178 193 205 May 184 193 210 June 140 149 160 July 145 157 166 August 152 161 174 September 110 122 126 October 130 130 148 November 152 167 173 December 206 230 235 Managerial Report Perform an analysis of the sales data for the Vintage Restaurant. Prepare a report for Karen that summarizes your findings, forecasts, and recommendations. Include the following: A time series plot. Comment on the underlying pattern in the time series.…For the E-Commerce Retail Sales (Million$) 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 following table shows quarterly sales (in thousand units) for a product over 4 years. The overall sales average for the entire 4 years is 126.0625 (thousand units).Fill in the missing values assuming there is no trend.Round seasonal indexes to 4 decimal places.Report Averages accurate to at most 2 decimal places.Report Actual sales ('000) accurate to the nearest integer. Year 1 Year 2 Year 3 Year 4 Average Seasonal Index Quarter 1 92 93 95 106 Quarter 2 105 106 105 101 Quarter 3 197 187 196 1.5211 Quarter 4 106 116 121 0.8865 PART BSuppose actual quarterly sales (in thousands) for Year 5 are 93, 123, 206, and 126 respectively. Assuming there is no trend, forecast sales for the four quarters of Year 5.Use seasonal indexes (rounded to 4 decimal places) from Part A.Report forecast answers as integers accurate to the nearest thousand.Quarter 1 forecast = thousandQuarter 2 forecast = thousandQuarter 3 forecast = thousandQuarter 4 forecast =…
- The sales and profit of a clothing organization are represented in the table below. This given data is used in forecasting and mainly decision making, accordingly: Sales , x ( in millions of dollars) Profits, y (in millions of dollars) 1.5 0.1 3.5 0.12 5.4 0.13 6.5 0.17 8 0.21 10 0.27 11 0.25 12 0.24 15 0.28 16 0.30 16.5 0.356 21 0.44 22 0.475 State three advantages of forecasting within an organization. What type of forecasting is being done within this organization? Explain why. pleasse make sure the answer is correct 100%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 detailMovieflix, an online movie streaming service that offers a wide variety of award-winning TV shows, movies, animes, and documentaries, would like to determine the mathematical trend of memberships in order to project future needs. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021Membership (000s) 17 16 16 21 20 20 23 25 24 1. What are the issues associated with qualitative forecasting, and how are these overcome?
- Movieflix, an online movie streaming service that offers a wide variety of award-winning TV shows, movies, animes, and documentaries, would like to determine the mathematical trend of memberships in order to project future needs. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021Membership (000s) 17 16 16 21 20 20 23 25 24 1. Forecast 2023 membership.Movieflix, an online movie streaming service that offers a wide variety of award-winning TV shows, movies, animes, and documentaries, would like to determine the mathematical trend of memberships in order to project future needs. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021Membership (000s) 17 16 16 21 20 20 23 25 24 1. Provide an example of qualitative forecasting and explain the shortcomings.Use this data and build a model to forecast annual maintenance expenses based on the weekly usage in hours. If the machine is used for 38.4 hours, what should I expect my annual maintenance expenses to be? (Keep one decimal place) Weekly Usage (hours) Annual Maintenance Expense 13 17.0 10 22.0 20 30.0 28 37.0 32 47.0 17 30.5 24 32.5 31 39.0 40 51.5 38 40.0
- This type of analysis is most appropriate when the past is a good predictor of the future.Curry Rubber manufactures rubber bands for retail compaies. The accounting manager has performed a regression analysis of past data. You notice that the formula has an R-squared of .6, at-valueof 2.3, and a standard error of the estimate of $200,000. The estimate for next quarter costs is $2,584,072. What do these statistics tell you about the reliability and precision of his regression analysis?Macomb Inc. used Excel to run a least-squares regression analysis on the production cost data resulting in the following output: Regression StatisticsMultiple R 0.9834 R Square 0.9596 Observations 30 Coefficients Standard Error T Stat P-ValueIntercept 174,980 61,580 2.84 0.021 Production units (X) 11.53 0.9265 12.44 0.000 What total cost would Macomb predict for a month in which production is 2,000 units? Multiple Choice $174,900 $63,433 $198,040 $23,037