Order DescriptionIn the SLP assignment you are also asked to estimate a market demand or a cost function (your choice) using the tools of regression analysis and the regression software outlined in attachment with complete assignment instructions.
The first data set (demand for housing) is used to apply the hedonic approach to demand estimation, while the second data set (demand for cigarettes) is used to apply the classical approach. Finally, the third dataset (cost of electricity) uses a well known dataset to estimate the cost of electricity production. In all cases the data is cross-sectional data.
The estimation of demand follows two approaches:
• the classical approach, whereby the quantity demanded of a product is explained by its own price, the prices of related goods (complements and substitutes), income, tastes and preferences, and the size of the population, among others;
• the hedonic approach, whereby the price of an asset (car, house) is explained by the characteristics of the asset itself (i.e., the price of housing depends on the number of bedrooms, the number of bathroom, the view from the house (using a dummy variable: 1 = view, 0 = no view), the square footage of the house, the square footage of the lot, etc).
SLP Assignment Expectations (Use Heading for paragraphs)
In the SLP Assignment, you are expected to:
• Describe the purpose of the paper and provide a conclusion.
• Present information in a professional manner.
• Answer the SLP Assignment question clearly and provide necessary details.
• Write clearly and correctly—that is, no poor sentence structure, no spelling and grammar mistakes, and no run-on sentences.
• Provide citations to support your argument and place references on a separate page. (All the sources that you listed in the references section must be cited in the paper.) Use APA format to provide citations and references [http://owl.english.purdue.edu/owl/resource/560/01/].
• Type and double-space the paper.
• Whenever appropriate, please use Excel to show supporting computations in an appendix, present economic information in tables, and use the data to answer follow-up questions.
* Note: See attachment with complete instructions on assignment. Paper should include two files: (1) An Excel file with analysis; and (2) A Word document.
Module 4 – SLP
Market Outcomes
Links to Estimation Techniques

Matt Kermode, Explanation of Regression Results, Available at https://www.youtube.com/watch?v=c5blVUkkjTM
Jason Delaney, Introduction to Multiple Regression, Available at https://www.youtube.com/watch?v=c5blVUkkjTM
Session Long Project
PART 1
In 2006 the CEO of Bear Sterns, James Caynes, received a compensation package of $34 million. The following year Bear Sterns cost $2.7 billion to the taxpayers. In 2006, the CEO of Lehman Brothers received a compensation package of $27 million. On September 15, 2008, Lehman Brothers filed for bankruptcy. The collapse of Lehman Brothers is seen by many as the key event that sparked the Global Financial Crisis. In 2006, the CEO of Citigroup, Charles Prince, received a compensation package of $25 million. Since then the stock price has fallen from $50 a share to $3.5 a share. The CEO of Countrywide Financial, Angelo Mozilo, did even better. His compensation package was $43 million. Angelo Mozilo and two other top executives were charged by the Security and Exchange Commission (SEC) with fraud. According to the SEC, from 2005 through 2007, Countrywide Financial engaged in an unprecedented expansion of its underwriting guidelines and was writing riskier and riskier loans, which these senior executives were warned might ultimately curtail the company’s ability to sell them. Countrywide Financial was the third biggest originator of subprime mortgages and the nation’s leader in subprime mortgage- backed securities. The tragedy is that these individuals did not make decisions that were in their companies’ best interest. Why? What went wrong? What caused the relation between the CEO and the stockholders to go so badly awry? Discuss.
PART 2
An important component of this course is experience with analyzing economic data at the managerial level. The computer is a perfect tool for manipulating data and performing statistical analyses. While the focus of BUS 530 is not on learning statistics, this course will utilize and improve your computer skills with a computer assignment designed to illustrate the interconnections between data, information and managerial decisions.
The primary software will be Microsoft Excel and the Excel statistical add-in: Data Analysis. Microsoft Excel 2010 (and previous versions) provides a set of data analysis tools called Analysis ToolPak which you can use to save steps when you develop complex statistical analyses. You provide the data and parameters for each analysis; the tool uses the appropriate statistical macro functions and then displays the results in an output table. The Analysis ToolPak is a Microsoft Office Excel add-in program that is available when you install Microsoft Office or Excel. To use the Analysis ToolPak in Excel, however, you need to load it first. Click the Microsoft Office Button, and then click Excel Options. Click Add-Ins, and then in the Manage box, select Excel Add-ins. Click Go. In the Add-Ins available box, select the Analysis ToolPak check box, and then click OK. (If Analysis ToolPak is not listed in the Add-Ins available box, click Browse to locate it.) If you get prompted that the Analysis ToolPak is not currently installed on your computer, click Yes to install it. After you load the Analysis ToolPak, the Data Analysis command is available in the Analysis group on the Data tab.
In the Module 4 SLP assignment you are also asked to estimate a market demand or a cost function (your choice) using the tools of regression analysis and the regression software outlined above.
The first data set (demand for housing) is used to apply the hedonic approach to demand estimation, while the second data set (demand for cigarettes) is used to apply the classical approach. Finally, the third dataset (cost of electricity) uses a well known dataset to estimate the cost of electricity production. In all cases the data is cross-sectional data.
The estimation of demand follows two approaches:
• the classical approach, whereby the quantity demanded of a product is explained by its own price, the prices of related goods (complements and substitutes), income, tastes and preferences, and the size of the population, among others;
• the hedonic approach, whereby the price of an asset (car, house) is explained by the characteristics of the asset itself (i.e., the price of housing depends on the number of bedrooms, the number of bathroom, the view from the house (using a dummy variable: 1 = view, 0 = no view), the square footage of the house, the square footage of the lot, etc).
PART 2: Assignment
You are given the data on housing. The data are collected from the real estate pages of the Boston Globe during 1990. These are homes that sold in the Boston, MA area. The source of the data is Wooldridge (2009) Introductory Econometrics: A Modern Approach, 4th Edition, Cengage
VARIABLES
1. price price, in dollars
2. assess assessed value, in dollars
3. bdrms number of bedrooms
4. lotsize size of lot, square feet
5. sqrft size of house, square feet
Cut and paste in Excel the data set. Then, in Excel, obtain the logarithmic transformation of the following variables using the Excel function =LOG( . )
6. lprice log(price) : dependent variable
7. lassess log(assess) : independent variable
8. llotsize log(lotsize) : independent variable
9. lsqrft log(sqrft) : independent variable
DATASET 1
OBSERVATIONS
PRICE SQRFT ASSESS BDRMS LOTSIZE
300 2438 349.1 4 6126
370 2076 351.5 3 9903
191 1374 217.7 3 5200
195 1448 231.8 3 4600
373 2514 319.1 4 6095
466 2754 414.5 5 8566
332 2067 367.8 3 9000
315 1731 300.2 3 6210
206 1767 236.1 3 6000
240 1890 256.3 3 2892
285 2336 314 4 6000
300 2634 416.5 5 7047
405 3375 434 3 12237
212 1899 279.3 3 6460
265 2312 287.5 3 6519
227 1760 232.9 4 3597
240 2000 303.8 4 5922
285 1774 305.6 3 7123
268 1376 266.7 3 5642
310 1835 326 4 8602
266 2048 294.3 3 5494
270 2124 318.8 3 7800
225 1768 294.2 3 6003
150 1732 208 4 5218
247 1440 239.7 3 9425
275 1932 294.1 3 6114
230 1932 267.4 3 6710
343 2106 359.9 3 8577
477 3529 478.1 7 8400
350 2051 355.3 4 9773
230 1573 217.8 4 4806
335 2829 385 4 15086
251 1630 224.3 3 5763
235 1840 251.9 4 6383
361 2066 354.9 4 9000
190 1702 212.5 4 3500
360 2750 452.4 4 10892
575 3880 518.1 5 15634
209 1854 289.4 4 6400
225 1421 268.1 2 8880
246 1662 278.5 3 6314
713 3331 655.4 5 28231
248 1656 273.3 4 7050
230 1171 212.1 3 5305
375 2293 354 5 6637
265 1764 252.1 3 7834
313 2768 324 3 1000
417 3733 475.5 4 8112
253 1536 256.8 3 5850
315 1638 279.2 4 6660
264 1972 313.9 3 6637
255 1478 279.8 2 15267
210 1408 198.7 3 5146
180 1812 221.5 3 6017
250 1722 268.4 3 8410
250 1780 282.3 4 5625
209 1674 230.7 4 5600
258 1850 287 4 6525
289 1925 298.7 3 6060
316 2343 314.6 4 5539
225 1567 291 3 7566
266 1664 286.4 4 5484
310 1386 253.6 6 5348
471 2617 482 5 15834
335 2321 384.3 4 8022
495 2638 543.6 4 11966
279 1915 336.5 4 8460
380 2589 515.1 4 15105
325 2709 437 4 10859
220 1587 263.4 3 6300
215 1694 300.4 3 11554
240 1536 250.7 3 6000
725 3662 708.6 5 31000
230 1736 276.3 3 4054
306 2205 388.6 2 20700
425 1502 252.5 3 5525
318 1696 295.2 4 92681
330 2186 359.5 3 8178
246 1928 276.2 4 5944
225 1294 249.8 3 18838
111 1535 202.4 4 4315
268 1980 254 3 5167
244 2090 306.8 4 7893
295 1837 318.3 3 6056
236 1715 259.4 3 5828
202 1574 258.1 3 6341
219 1185 232 2 6362
242 1774 252 4 4950

Please keep in mind that when you interpret a regression coefficient, you are assuming that all the other variables remain constant.
A Note on ANOVA
The ANOVA table is used to test the null hypothesis that all regression coefficients (excluding the intercept term) are equal to zero against the alternative hypothesis that at least one is different from zero. This test is known as the F test for regression. The F test is computed as follows, under the assumption that the null hypothesis is true:

The F statistics has two sets of degrees of freedom: numerator (attached to the Regression SS) and denominator degrees of freedom (attached to Residual SS).
Excel computes the F statistic for you in the ANOVA table, and computes in the last column the level of significance (p-value). If the level of significance of the test is less than 5%, you will reject at the 5% level the null hypothesis that all regression parameters are zero. On the other hand, if the level of significance is greater than 5%, you will accept (i.e., fail to reject) the null hypothesis that all regression parameters are zero.
SLP Assignment Expectations (Use Heading for paragraphs)
In the Module 4 SLP Assignment, you are expected to:
• Describe the purpose of the paper and provide a conclusion.
• Present information in a professional manner.
• Answer the SLP Assignment question clearly and provide necessary details.
• Write clearly and correctly—that is, no poor sentence structure, no spelling and grammar mistakes, and no run-on sentences.
• Provide citations to support your argument and place references on a separate page. (All the sources that you listed in the references section must be cited in the paper.) Use APA format to provide citations and references [http://owl.english.purdue.edu/owl/resource/560/01/].
• Type and double-space the paper.
• Whenever appropriate, please use Excel to show supporting computations in an appendix, present economic information in tables, and use the data to answer follow-up questions.