he number of plumbing repair jobs performed by Augur’s Plumbing Service during each month last year. Draw the scatter diagram. Now briefly explain which of the following patterns exist in this data and which do not: trend, cycle, seasonal. Cite evidence that supports your conclusion. Month Jobs January 112 February 105 March 89 April 96 May 97

he number of plumbing repair jobs performed by Augur’s Plumbing Service during each month last year. Draw the scatter diagram. Now briefly explain which of the following patterns exist in this data and which do not: trend, cycle, seasonal. Cite evidence that supports your conclusion. Month Jobs January 112 February 105 March 89 April 96 May 97

Contemporary Marketing

18th Edition

ISBN:9780357033777

Author:Louis E. Boone, David L. Kurtz

Publisher:Louis E. Boone, David L. Kurtz

Chapter14: Pricing Strategies

Section14.2: Forecasting Demand

Problem 1LO

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Similar questions

The following table shows the quarterly sales (RM'000) for Afeef Café for the past 3 years. Quarter Year 1 2 4 2019 11 17 10 18 2020 19 24 17 29 2021 23 38 31 33 Based on the above data. (b) Calculate the seasonal indices using the multiplicative model.
. Describe trend, seasonality, random variation, and cycle as applied to forecasting.
Explain 4 methods of time series in demand forecasting with an examples.
Asvnch Problem - Statistical Forecasting Data Set – Eunice BC Fashion Monthly Sales, in million units. Year Total Sales Year Total Sales 2010 38 2016 43 2011 41 2017 40 2012 40 2018 45 2013 45 2019 47 2014 50 2020 42 2015 42 2021 48 Questions: a. Find the naïve forecast. b. Use the 3 years moving average forecast. c. Have a 5 years weighted moving average. d. Develop forecast using exponential smoothing with a = 0.2. e. Determine the trend line equation and present the forecast. f. Find the best forecast for year 2022. Note: Use the first 5 years as the training samples and the last 5 years as the forecasting samples. Solve it in Excel Sheet/Sheet with Equations as possible.
What is linear trend forecast.
15 5 Two independent methods of forecasting based on judgment and experience have been prepared each month for the past 10 months. The forecasts and actual sales are as follows: 1 Month Sales Forecast 1 835 845 2 1 2 3 4 5 6 7 8 9 10 Forecast 850 820 785 840 815 840 785 800 Forecast 835 830 840 790 815 785 770 835 a. Compute the MSE and MAD for each forecast. (Round your answers to 2 decimal places.) MSE MAPE F1 MAPE F2 805 785 Answer is complete but not entirely correct. MAD 2,942.50 x Forecast 2 765 830 1,125.60 x 815 830 800 806 775 810 815 805 4.2153 X % 3.4380 % 22.50✔ Return to question 28.40✔ b. Compute MAPE for each forecast. (Round your intermediate calculations to 5 decimal places and final answers to 4 decimal places.) > Answer is complete but not entirely correct. c. Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. (Negative values should…
B). Using exponential smoothing, calculate the forecasts for months 2, 3, 4, 5, and 6. The smoothing constant is 0.2, and the old forecast for month I is 245. Month Actual Demand Forecast Demand 1 260 2. 230 3 225 4 245 5 250 6. C). Given the following average demand for each month, calculate the seasonal indices for each month. Month Average Demand Seasonal Index January 30 February 50 March 85 April 110 May 125 June 245 July 255 August 135 September 100 October 90 November 50 December 30 Total 3 D). Using the data in part A and the seasonal indices you have calculated, calculate expected monthly demand if the annual forecast is 2000 units. Month Seasonal Forecast Index January February March April May June July August September October November December
State and explain the value of seasonala indices in forecasting and how are seasonal patterns different from cyclical patterns ?
What is the linear trend forecast for September? Month Month Sales Feb 1 19 Mar 2 18 Apr 3 15 May 4 20 Jun 5 18 Jul 6 22 Aug 7 20
Quarter 1 2 31 11 14 172 20 23 263 29 32 354 38 41 44Compute seasonal relatives for this quarterly data.a. Use the SA method.
4 .Substitute for Problem # 4 on in attachment)-Calculate the MAD,revised MAD and the Revised Forecast for months 1-6 for the following three groups,where the Forecast stands for Forecast Demand.Has your forecast improved ? Tell me how would you go about improving the revised forecast( for each of the three groups)without actually doing so? Estate Planning Group Auditing and Accounting Group Business Consulting Group Month Actual Demand Forecast Actual Demand Forecast Actual Demand Forecast 1 100 125 270 230 140 135 2 90 125 240 230 130 135 3 110 125 280 230 160 135 4 115 125 260 230 180 135 5 130 125 300 230 200 135 6 115 125 220 230 190 135
2. An operation specialist used two forecast methods to forecast demand for a certain word processing software package. Actual demand and his corresponding predictions are shown below: Month Sales Forecast A Forecast B January 60 February 25 60 March 54 50 42 April 36 30 55 May 45 45 35 June 60 20 Which forecast method would you recommend if using MAD as the accuracy measurement? Select one: a. Forecast A b. Forecast B c. No preference.