Marketing
14th Edition
ISBN: 9781259924040
Author: Roger A. Kerin, Steven W. Hartley
Publisher: McGraw-Hill Education
expand_more
expand_more
format_list_bulleted
Concept explainers
Question
Chapter 8, Problem 8AMK
Summary Introduction
To determine: Whether the annual population of Country U or annual sales of cars produced in County U by Company F has more accurate linear trend extrapolation and the reason behind that.
Introduction:
The process of defining the marketing issue and opportunity, systematically analyzing and collecting the facts, suggesting actions to decrease the risk, and thereby improving the decisions in the market is known as the marketing research.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
Cell phone sales for a California-based firm over the last 10 weeks are shown in the following table. Plot the data, and visually check to see if a linear trend line would be appropriate.Then determine the equation of the trend line, and predict sales for weeks 11 and 12.Week Unit Sales1 7002 7243 7204 7285 7406 7427 7588 7509 77010 775
Answer in Excel:
Consider the data below for the sales of widgets: 1. Using seasonal percentages or seasonal indexes, forecast the sales for each season in year 4, if the annual widgets sales is predicted to be 1500. 2. Develop a regression equation that captures both the trend and seasonality in this data. Use this equation to forecast the sales for each season in year 4.
Season
Year 1
Year 2
Year 3
Fall
505
240
210
Winter
555
460
365
Spring
400
310
204
Summer
560
450
394
From the following annual data of sales (in 000 .$.) Find the trend values by using least square method. Also estimate the sales of 2014.
Year
2004 2005 2006 2007 2008 2009 2010
Sales {In 000 $}
77 88
94
85
91
98
90
Chapter 8 Solutions
Marketing
Ch. 8.1 - Prob. 8.1LOCh. 8.2 - Prob. 8.2LOCh. 8.2 - Prob. 8.1LRCh. 8.2 - Prob. 8.2LRCh. 8.2 - Prob. 8.3LRCh. 8.3 - Prob. 8.3LOCh. 8.3 - Prob. 8.4LRCh. 8.3 - Prob. 8.5LRCh. 8.4 - Prob. 8.4LOCh. 8.5 - Prob. 8.5LO
Ch. 8.5 - Prob. 8.6LRCh. 8.5 - Prob. 8.7LRCh. 8.5 - Prob. 8.8LRCh. 8.5 - Prob. 8.9LRCh. 8.5 - Prob. 8.10LRCh. 8.6 - Prob. 8.6LOCh. 8.6 - Prob. 8.11LRCh. 8.6 - Prob. 8.12LRCh. 8 - Prob. 1AMKCh. 8 - Prob. 2AMKCh. 8 - Prob. 3AMKCh. 8 - Prob. 4AMKCh. 8 - Prob. 5AMKCh. 8 - Prob. 6AMKCh. 8 - Look back at Figure 8-6A. (a) Run the percentages...Ch. 8 - Prob. 8AMKCh. 8 - Prob. 1BYMPCh. 8 - Prob. 2BYMPCh. 8 - Prob. 3BYMPCh. 8 - Prob. 1VCCh. 8 - Prob. 2VCCh. 8 - Prob. 3VCCh. 8 - Prob. 4VCCh. 8 - Prob. 5VC
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, marketing and related others by exploring similar questions and additional content below.Similar questions
- Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks? Week 1 2 3 4 5 Sales 150 157 162 166 177arrow_forwardPlease 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.arrow_forwardConsider the following quarterly demand level for electricity (in 1000 megawatts) in Mankweng from 2018 to 2022: 2018 2019 2020 2021 2022 Jan – March 21 35 49 60 10 - Apr – June 42 54 55 64 05 July – Sept Oct – Dec 60 91 95 99 12 14 74 80 1. Find the least squares trend line for electricity demand using Exx=0 method. 2. Find the seasonal index for each quarter. 3. Find adjusted seasonal index. 4. Find the De-seasonalized values for Quarter 1, 2,3 and 4 for the year 2023.arrow_forward
- Consider the monthly sales data of a company for last year as well as first six month data for current year. Use a three- quarter weighted moving average, Forecast the sales of company for 3rd Quarter of current year. Use Weights of 4/7, 2/7 and 1/7, giving more weight to more recent data. Note, the 1ist quarter is Jan, Feb and March , 2nd quarter is Apr, May, June, 3rd quarter is July, Aug and Sept, and 4th quarter is Oct, Nov and Dec. Month Jan Feb Mar April May June July Aug Sept Oct Nov DecLast Year 100 125 135 175 185 200 150 140 130 200 225 250Current Year 125 135 135 190 200 190arrow_forwardThe marketing manager of a company that manufactures and distributes farming equipment (such as combined harvesters, ploughs and tractors) recorded the number of farming units sold quarterly for the period 2016 to 2019. Using the four period moving average and sequential numbering system starting with x=1 in period 1. determine the following : a) median seasonal index of summer: b) median seasonal index of spring: c) total median seasonal index: d) the adjustment factor:arrow_forwardUse the sales data given below to determine: (a) the least squares trend line, and (b) the predicted value for 2003 sales. Year Sales (Units) 1996 100 1997 110 1998 122 1999 130 2000 139 2001 152 2002 164 To minimize computations, transform the value of x (time) to simpler numbers. In this case, designate year 1996 as year 1, 1997 as year 2, etc.arrow_forward
- B. The following table shows the number of televisions sold over the last ten years at a local electronic store. year TV sales 1 150 2 300 3 480 4 600 5 630 6 640 7 700 8 825 9 900 10 980 i. Using the trend projection, develop a formula to predict sales for years 11 and 12. Develop a table to calculate the slope and intercept. Please Show All Working. Use that formula to forecast television sales for years 11 and 12arrow_forwardConsider the monthly sales data of a company for last year as well as first six-month data for current year. Use a three- quarter weighted moving average, Forecast the sales of company for 3rd Quarter of current year. Use Weights of 4/7, 2/7 and 1/7, giving more weight to more recent data. Note, the 1ist quarter is Jan, Feb and March, 2nd quarter is Apr, May, June, 3rd quarter is July, Aug and Sept, and 4th quarter is Oct, Nov and Dec. Month Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Last Yr 100 125 135 175 185 200 150 140 130 200 225 250 Current Yr 125 135 135 190 200 190arrow_forwardFor 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.arrow_forward
- Find the five-month moving average of the sales of soaps for this year.arrow_forwardA pharmacist has been monitoring sales of a certain over-the-counter pain reliever. Daily sales during the last 15 days were as follows:Day 1 2 3 4 5 6 7 8 9Number sold 36 38 42 44 48 49 50 49 52Day 10 11 12 13 14 15Number sold 48 52 55 54 56 57a. Which method would you suggest using to predict future sales—a linear trend equation or trend-adjusted exponential smoothing? Why? b. If you learn that on some days the store ran out of the specific pain reliever, would that knowledge cause you any concern? Explain c. Assume that the data refer to demand rather than sales. Using trend-adjusted smoothing with an initial forecast of 50 for day 8, an initial trend estimate of 2, and α = β = .3, develop forecasts for days 9 through 16. What is the MSE for the eight forecasts for which there are actual data?arrow_forwardFind Trend adjusted exponential forecast for predicting the sales of the data below for period 11 and 12. Initialize in period 9. The value of alpha = 0.3 and beta = 0.2 1 700 2 724 3 720 4 728 5 740 6 742 7 758 8 750 9 770 10 775arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Single Exponential Smoothing & Weighted Moving Average Time Series Forecasting; Author: Matt Macarty;https://www.youtube.com/watch?v=IjETktmL4Kg;License: Standard YouTube License, CC-BY
Introduction to Forecasting - with Examples; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=98K7AG32qv8;License: Standard Youtube License