# Summary Statistics for AIMCO Business Statistics Project Worksheet

### Description

Explanation & Answer length: 6 Pages.

• Project Length: Roughly 6 pages (including graphs and tables), double spaced, but it can be longer.
• This paper must be written in the format of a research paper (minus the references, unless you want to include some—but it’s not required).
• A maximum of three students can work together—or you can work alone, or in pairs.

Idea: Why do prices of an investment in REIT go up or down? In other words, to obtain insight in the drivers of REIT returns.

1. INTRODUCTION (This section should be a minimum of 1 page):

What is the population we are studying (Apartment Investment & Management Co: https://www.aimco.com/ )? Give a brief introduction on what this REIT is, where it invests, etc. What kind of data is this (time series data or cross sectional data)? What is the size of the sample? What do you hope to show using this data?

2. DESCRIPTIVE STATISTICS (This section should be a minimum of 2 pages):

2.1 Include a list of the variables and their descriptions (see Excel spreadsheet).

2.2 Summarize the return and the other nine variables that you are analyzing.  Make a table with the Means, Medians, Standard Deviations, etc. like the following:

Table 1: Summary Statistics for AIMCO

2.3 Include two graphs:

• Show the price of the share in a graph over time
• Make a histogram for the return of the share; you have to select the borders of the bins (in total 7-10 bins)

Below the graphs write a brief explanation of what each graph shows.

2.4 Include a table with the correlations between all ten variables (including return).

3. INFERENTIAL ANALYSIS (This section should be a minimum of 2 pages):

3.1 Perform multiple linear regressions with all explanatory variables

Return = βo + β1X1 + β2X2 + …. + β10X10 + e                       (1)

In Table 2 below are the results from equation (1).

Present your regression results in a table and briefly explain your findings.

Table 2: Multiple Linear Regression Model 1 Results

*) *Significant at the 10% level, ** significant at the 5% level, *** significant at the 1% level.

3.2 Adjusted regression. Exclude indicators that are individually not significant at the 10% level, and with the remaining indicators you run a second regression.

Return = βo + β..X.. + …. + β..X.. + e                        (2)

In Table 3 below are the results from equation 2.

Present your regression results in a table and briefly explain your findings.

Table 3: Multiple Linear Regression Model 2 Results

CONCLUSION

Conclusion: Which variable(s) explain the return the best?

Teams

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