LINEAR REGRESSION AND SIMPLE EXPONENTIAL SMOOTHING (SES) FORECASTING

 

Year 2	Customers (x)	Actual Y(t)
January	215	220
February	259	243
March	325	270
April	354	320
May	258	300
June	199	260
July	254	277
August	299	331
September	264	281
October	198	199
November	223	200
December	261	250
Totals	259.08	262.58

 

Notes on Module 2

4 / 10 / 2018

Hello All –

Just a couple of notes on Module 2.   Be sure that you read this email, as it answers frequently-asked questions concerning the Module 2 assignments (Case and SLP):

1) In the Module 2 Case, you are given store visits data and sales data. You are advised that there is a positive relationship (or correlation) between the visits for any given month and the sales for that month. Your task is to use linear regression; matching the first 12 months of visits with 12 months of sales. Then, you use the linear equation to forecast sales for Year 2. Listed below are the "actual sales" numbers for Year 2, so that you can compare the sales given by the Linear Regression (LR) equation with the actual (known) sales for Year 2. 

2) In the SLP Assignment, you are asked to use a different method for forecasting and do a comparison of this and the linear regression you used in the case assignment. In order to do that, you need to make sure you are using the same data as your starting point that you had in the Case.

3) Threaded Discussion – Participate in the Threaded Discussion and include reflections on the topic in your papers.  Read and post frequently – not much of a discussion if everything is posted at the last minute.

Get started EARLY - and do NOT skip the background material (it’s hard to complete the Case and SLP correctly without doing both)!

Good luck with this module - take your time and get it right! If you take your time with the background materials, you should have no problem with these assignments. If you need clarification, please be sure to ask. I will not give you the answers, but I will be glad to clarify anything that has not been made clear. Also happy to review your spreadsheets before you submit - just send to me via email.