B231A Market Forecasting

Rose Orchard, Wednesday 6 to 9:45, Code:  31309

 

Course Description:  Provides a practical guide to market research and a non-technical survey of forecasting methods supported by software such as Excel and SPSS with an option to use SAS or R.  Topics include survey design, the use of focus groups, cross-tabulation, times series analysis, regression, discriminant analysis, factor analysis, conjoint analysis, and data mining – the major market analysis methods. 

 

Instructor:  Kent Webb,  webb_k@cob.sjsu.edu  408 924-1348.

 

Texts:  Marketing Research Essentials by Carl McDaniel and Roger Gates, 6th edition.  Wiley.  This book includes a copy of SPSS, an analysis software that will be used with along with Microsoft Excel for some of the cases and the research project.  SAS or R can be used in place of SPSS.  Also, the free online text, StatSoft will be used.

 

Meeting

Chapter

Topic (topics in Italics are discussed in the StatSoft online text).  Click on topic to download PowerPoints or other files.

3/5

1, 2, 12

Role of Market Research.  Problem Definition,   Data Processing and Analysis. Case 1 Distributed.  NASDAQ Forecast 1.

3/12

3, 4, 13

Secondary Data and Databases,   Correlation and Regression, Qualitative Research and Focus Groups.    Time Series (Statsoft Link)    Time series analysis (SPSS PDF),  Regression with SPSS,   Case 2 distributed.  Case 1 due. NASDAQ Forecast 2.

3/19

5

Survey Research.  Time series analysis, continued, powerpoint slides  .  In class focus group. Case 3 Distributed   Conjoint Analysis (SPSS PDF).  Conjoint Analysis tutorial.   Case 2 due.  NASDAQ Forecast 3.

3/26

6,7

Primary Data Collection.   Experiments.  Case 3 (focus group report and exponential smoothing) due. Review for exam.     NASDAQ Forecast 4.

3/29

Sat.

All Day

Exam

 

8,9

The exam is short answer, essay, and multiple choice.  Lasts from 9 am to noon.  Class starts again at 1 pm.

Measurement, Questionnaire Design.  Factor Analysis,   Factor Analysis and Cluster Analysis (SPSS PDF),  Cluster Analysis  Case 4 distributed.  

 

4/2

10

Sampling.      Case 5 distributed    Discriminant Analysis (SPSS PDF) Discriminant Analysis. Case 4 due. NASDAQ Forecast 65

4/9

11, 14

Sample Size,  Estimation Examples, Communicating Results,   Classification Trees   Data Mining (SPSS PDF)  Data Mining    Case 6 distributed.  Case 5 due.  NASDAQ Forecast 6.

4/16

 

Optional Presentations.  Review for Final.  Case 6 due.  NASDAQ Forecast 7.

4/23

Exam

Written Projects due.  One essay question on the exam is an emailed evaluation of class presentations.

 

 

 

Course Requirements:

Points

Two exams, 100 points each

200

Six case studies, 15 points each

  90

Market Forecasting Project

100

NASDAQ Forecasting Competition

  10

Total points

400

 

Expected grade distribution:  95 to 100%, A; 90 to 94%, A-, 87 to 89%, B+; 82 to 86%, B; 80 to 81%, B-. 

 

The Market Forecasting Project can be done individually or with a group based on a topic of personal interest such as a company sales forecast, real estate analysis, or a market segment forecast.  It should apply one or more of the analysis tools discussed in the class. The project can be delivered as a written report or, optionally, presented to the class.   Six of the case studies involve the use of Excel and SPSS (optionally SAS) in short data analysis projects.  Two of the cases involve development of the Market Forecasting Project, including a focus group report and the design of a survey questionnaire.

 

The NASDAQ Forecasting Competition involves providing a forecast of how the NASDAQ will close on the following Thursday (class day).  Forecasts will be collected during the beginning of class, or can be emailed before the open of the market on the following Thursday.  Five points for participation.  Try to make at least 10 forecasts and give some analysis in class.