Lundquist College of Business

Decision Sciences 330H

Business Statistics
Syllabus

 Spring 2009

 

ADDRESSES |ANNOUNCEMENTS |ASSIGNMENTS |HANDOUTS |LINKS |


University of Oregon

 

 

 

SERGIOK@UOREGON.EDU


Announcements

 

Please make sure to always bring your class packets to class!!!!

By clicking on the links below you will find :

  • files containing data to be analyzed for specific classes
  • assignments - filenames with the suffix:
    • -1 are generally due on the Wednesday of that week prior to class
    • -2 are generally due on the Monday of following week prior to class
  • handouts (go directly to the specific week below)

 

  Old Announcements

 

Tentative Schedule

 

 

Week 1

Review: Probability Distributions

Introduction; Summary Statistics; Tchebychev's Theorem; Probability Distributions (Discrete & Continuous)

Normal Distribution; Sampling Distributions; Central Limit Theorem.

 

 

Week 2

Review: Confidence Intervals & Hypothesis Testing

Confidence Intervals; T - Distribution; Hypothesis Testing; Problems.

 

 

 

Week 3

  Exploiting Relationships Among Variables to Make Decisions

Hypothesis Testing: Testing Differences of Means between Groups; Correlation & Autocorrelation; Regression Analysis: General Linear Additive Model; Transformations; Interpretation of Coefficients; Using Regression to Make Business Decisions.

 

 

Week 4

Regression Analysis

Least Squares; Confidence Intervals; Statistical Significance of Coefficients; Hypothesis Testing; Goodness of Fit - R2; Lags; Generating Forecasts; Construction of Regression Models Using SPSS; Formulation Computer Models.

 

Week 5

 

Regression Analysis: Construction & Interpretation

F-tests; Testing the Utility and Portions of a Regression Model; F-distribution; Dummy/Categorical Variables: Modeling Extraneous Effects; Trends & Seasonality; Problems SPSS .

 

 

Week 6

 

Regression Analysis: Construction & Interpretation

Assumptions Behind the General Linear Model; Linearity; Multicollinearity; Problems.

Midterm 1  

 

Week 7

 

 

Regression Analysis: Model Diagnostics

Regression Pitfalls: Autocorrelation-Consequences; How to Spot It; Generic Remedies; Autocorrelation: Partials; Cochrane-Orcutt; ARMA Processes; Koreisha & Fang; Computer Models. Problems.

 

 

Week 8

 

Regression Analysis: Pitfalls

 

Seasonal AR Processes; Computer Problems.

Heteroscedasticity; Computer Problems

 

 

Week 9

 

Regression Diagnostics and Model Building

Midterm 2

 

 

 

Week 10

 

Outliers; Leverage & Computer Models. Review

Final: Wednesday, Jun 10th @ 15:15

 

 

 

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