The table below shows quarterly sales of a product from 2011 to 2015. The compan
ID: 3059825 • Letter: T
Question
The table below shows quarterly sales of a product from 2011 to 2015. The company wants to make a sales forecast for Q1 of 2016. Consider the following methods:
- Six-period moving average
- Simple exponential smoothing, alpha = 0.8 and initial estimate of $14.81 millions
- Linear regression with trend and binary variables for seasonality
Year
Quarter
Sales ($ million)
2011
Q1
14.81
2011
Q2
14.49
2011
Q3
14.87
2011
Q4
14.81
2012
Q1
14.54
2012
Q2
14.40
2012
Q3
14.59
2012
Q4
14.95
2013
Q1
14.70
2013
Q2
14.65
2013
Q3
14.40
2013
Q4
14.45
2014
Q1
14.81
2014
Q2
14.47
2014
Q3
15.07
2014
Q4
14.79
2015
Q1
14.45
2015
Q2
14.48
2015
Q3
14.81
2015
Q4
14.64
Question 1: Which method should be used? Why?
Question 2: Based on your answer in part A), what is the sales prediction for Q1, 2016?
Question 3: How accurate do you expect the forecast in part B) to be? Use appropriate measure(s) to support your answer.
Year
Quarter
Sales ($ million)
2011
Q1
14.81
2011
Q2
14.49
2011
Q3
14.87
2011
Q4
14.81
2012
Q1
14.54
2012
Q2
14.40
2012
Q3
14.59
2012
Q4
14.95
2013
Q1
14.70
2013
Q2
14.65
2013
Q3
14.40
2013
Q4
14.45
2014
Q1
14.81
2014
Q2
14.47
2014
Q3
15.07
2014
Q4
14.79
2015
Q1
14.45
2015
Q2
14.48
2015
Q3
14.81
2015
Q4
14.64
Explanation / Answer
Q1) Moving Average- A simple moving average is formed by computing the average price of a security over a specific number of periods. Most moving averages are based on closing prices. A 5-day simple moving average is the five-day sum of closing prices divided by five. As its name implies, a moving average is an average that moves. Hence to make a sales forecast for Q1 of 2016 this method is not appropriate.
Simple Exponential Smoothening- The simplest of the exponentially smoothing methods is naturally called “simple exponential smoothing” (SES). This method is suitable for forecasting data with no trend or seasonal pattern. As the data mentioned is seasonal hence this method is also not appropriate.
Linear Regression- Linear regression is a common Statistical Data Analysis technique. It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables. There are two types of linear regression, simple linear regression and multiple linear regression.In simple linear regression a single independent variable is used to predict the value of a dependent variable. Hence it can be considered as an appropriate method.
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