Take Home Final

Due Apr 30

Here is the take home final: FinalTakeHomeExam_Apr24-2014

Do NOT post answers to your blog.

Email your answers to me no later than 5pm on Wed, April 30th.

Review Exam Topics

Topics for the Review Exam on Thu Apr 24

Regression Analysis

• Correlation – graphical interpretation with scatter plots
• Pearson correlation coefficient
• Bivariate and Multiple regression
• Determine equation of regression line (bivariate only)
• Coefficient of determination (r-squared)
• Significance testing (t-test, F-test)
• Interpretation of regression output
• Quadratic models
• Indicator (dummy) variables
• Multicollinearity
• Trend Analysis – linear and quadratic
• Index Numbers

Introductory (BUSAD 265) Topics

• Measures of Central Tendency
• Measures of Variability
• Data Graphics
• Normal Distribution
• Probability under the Normal Curve
• Estimation
• Confidence Intervals
• Hypothesis Testing

Review Exam

Thu Apr 24

There will be a comprehensive “Review Exam” on the last day of class. No new material will be covered but all material this term is fair game. The purpose is to have you review the highlights of what we’ve covered during the term to refresh your memory. I will present a list of review topics during class Tuesday.

This review of material will go hand-in-hand with your final assignment because you need to review various techniques to assess what may be appropriate for your data analysis.

Note this is not the Final exam. There will be an optional take-home final (or an advanced project option) for those who need to make up a missed exam or want an opportunity to improve their grade.

Final Assignment

Due Fri Apr 25

If you missed class Tuesday I described a final assignment. I will clarify here.

1. Select a topic of interest. This could be sports, politics, economics, business, stock market, current events, video games…anything that interests YOU. This should be related to a passion, hobby or something that you enjoy pursuing in your own time. One caveat is that the topic will need to offer some type of data for analysis.

2. Collect data that can be used for analysis. Search the web for relevant data. It won’t be perfect so don’t bother looking for the perfect data. Just find any data and we will do our best. More data is typically better but if there’s a mountain of data and you don’t know where to start I can help you take a random sample or otherwise limit the universe for analysis.

3. Analyze the data using appropriate methods learned in class this term. You could produce a simple descriptive analysis or you could conduct a hypothesis test, produce a confidence intervals or build a regression model. The key is to do something appropriate to gain a better understanding of the data or to make an argument about what the data is telling you. If you do the bare minimum your grade will be the bare minimum as well.

4. Write a blog post describing your analysis. Provide details regarding the data utilized and the analysis completed. If you considered other techniques but decided not to use them for some reason please explain your thought process. Use graphs, equations, tables, computer output etc as appropriate to present your analysis. What did you learn about your topic and the data collected?

For Tuesday please be prepared to tell me about your topic and to ask any questions.

Step #4 must be completed by 5pm Friday, April 25th. 

Trend Analysis, Seasonality and Index Numbers

Due Tue Apr 15

Textbook Reading

• Ch 15 p 618-623 (Trend Analysis)
• Ch 15 p 625-629 (Seasonality)
• Ch 15 p 637-642 (Index Numbers)

Complete the following using this dataset: CornGas

• Calculate Quarterly Seasonal Indices for Gasoline prices
• Create a Corn Price Index
• Develop a Quadratic Regression Model to forecast Gasoline prices
• Post results to your blog

Be prepared to answer questions about trend analysis, seasonality and index numbers.

Data for Multicollinearity Assignment

Here’s the data for the multicollinearity assignment:  black_7e_excel_database

Multicollinearity Assignment

Due Thu Apr 10

• Textbook Reading: Ch 14 p 582-584
• Write a Blog Post: How does multicollinearity apply to the Pueblo Real Estate modeling project?
• Complete Problem #3 on page 598 using this Excel data: black_7e_excel_database

Exam #10 Review and Results

Here are the results from Exam #10. We will review in class.

Review Topics

• Reading t-stats and p-values to assess significance of independent variables
• Reading F-stat to assess overall model significance
• Model accuracy – focus on R-Squared

Descriptive Statistics

• n=28
• μ=89.00
• σ=12.04

Stem and Leaf Plot

Individual Scores

Course Evaluations

Spring 2014 Course Evaluations

• Please use the Course Evaluation service in PAWS to complete an evaluation.
• Please provide feedback so I can improve my teaching.
• Responses are completely anonymous; I will only see summarized results after grades are posted.
• Evaluations may be submitted anytime between today (Apr 7th) and May 4th

Indicator (Dummy) Variables

Due Tue Apr 8

• Textbook Reading Ch 14 p 566-569
• Download this file: PuebloRealEstate_DummyVars
• Add one or more “Dummy” variables to your Pueblo Real Estate model
• Be prepared to answer questions about Indicator variables