Linear Regression and Simple Exponential Smoothing (SES) Forecasting, assignment help

User Generated

CvaxrlErq

Business Finance

Description

Module 2 - Case

Linear Regression and Simple Exponential Smoothing (SES) Forecasting

Assignment Overview

Scenario: You are a consultant who works for the Diligent Consulting Group. Your client, the New Star Grocery Company, believes that there may be a relationship between the number of customers who visit the store during any given month (“customer traffic”) and the total sales for that same month. In other words, the greater the customer traffic, the greater the sales for that month. To test this theory, the client has collected customer traffic data over the past 12-month period, and monthly sales for that same 12-month period (Year 1).

Case Assignment

Using the customer traffic data and matching sales for each month of Year 1, create a Linear Regression (LR) equation in Excel, assuming all assumptions for linear regression have been met. Use the Excel template provided (see “Module 2 Case – LR –Year 1” spreadsheet tab), and be sure to include your LR chart (with a trend line) where noted. Also, be sure that you include the LR formula within your chart.

After you have developed the LR equation above, you will use the LR equation to forecast sales for Year 2 (see the second Excel spreadsheet tab labeled “Year 2 Forecast”). You will note that the customer has collected customer traffic data for Year 2. Your role is to complete the sales forecast using the LR equation from Step 1 above.

After you have forecast Year 2 sales, your Professor will provide you with 12 months of actual sales data for Year 2. You will compare the sales forecast with the actual sales for Year 2, noting the monthly and average (total) variances from forecast to actual sales.

To complete the Module 2 Case, write a report for the client that describes the process you used above, and that analyzes the results for Year 2. (What is the difference between forecast vs. actual sales for Year 2—by month and for the year as a whole?) Make a recommendation concerning how the LR equation might be used by New Star Grocery Company to forecast future sales.

Data: Download the Module 2 Case template here: Data chart for BUS520 Case 2. Use this template to complete your Excel analysis.

Assignment Expectations

Excel Analysis

Conduct accurate and complete Linear Regression analysis in Excel. Use Excel support to find information on linear regression in Excel: https://support.office.com/en-us/Search/results?query=linear+regression

Written Report

  • Length requirements: 4–5 pages minimum (not including Cover and Reference pages). NOTE: You must submit 4–5 pages of written discussion and analysis. This means that you should avoid use of tables and charts as “space fillers.”
  • Provide a brief introduction to/background of the problem.
  • Your written (in Word) analysis should discuss the logic and rationale used to develop the LR equation and chart.
  • Provide complete, meaningful, and accurate recommendation(s) concerning how the New Star Grocery Company might use the LR equation to forecast future sales. (For example, how reliable is the LR equation in predicting future sales?) What other recommendations do you have for the client?
  • Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.
  • Have an introduction at the beginning to introduce the topics and use keywords as headings to organize the report.
  • Avoid redundancy and general statements such as "All organizations exist to make a profit." Make every sentence count.
  • Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words.
  • Upload both your written report and Excel file to the case 2 Dropbox.

Here are some guidelines on how to build critical thinking skills.

User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.

Explanation & Answer

ok
Attached.

Running Head: SIMPLE EXPONENTIAL SMOOTHING
1

Simple Exponential Smoothing
Name
Course
Tutor
Date

SIMPLE EXPONENTIAL SMOOTHING

2

Introduction
Exponential smoothing is a method or technique in Statistics that is easily applied in
smoothing a discrete time series with an aim of conducting forecasts. The popularity of this
technique mainly stems from how simple it is in application, its efficiency in computing and the
ease with which it responds to process variations that help with the forecasting process
(Ostertagová & Ostertag, 2011). The procedure’s main conception is to help smooth the time
series in its original form similar to moving average then forecasting the variable in question by
use of the already smoothed time series. However, as it occurs in exponential smoothing, the
most current of values in the time series usually have the most impact on the value that results
after forecast as compared to past observations. This approach is considered to be practical as
well as simplistic in matters to do with forecasting where the value forecast results from the
weighted average of recent observation with the most recent observation in the time series taking
on the least weight while the present observation in the series takes on the largest weight.
There are several exponential techniques in existence. However, Simple Exponential
Smoothing (S.E.S.) is considered to be the simplest of all the techniques since it does not require
a trend in a time series for it to be used in forecasting data. It is applicable where data in question
is in a horizontal pattern where the original data does not have a trend that is pronounced or does
not have a cyclic variation. The technique conducts comparisons between previous actual and
forecasted values which then leads to the gap between these two being applied to the subsequent
immedi...


Anonymous
Just what I was looking for! Super helpful.

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4

Related Tags