Similarity Between Two Variables In A Regression

Similarity Between Two Variables In A Regression

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Similarity Between Two Variables In A Regression

Project 3 ECO 3431, Fall 2021.

State Mandated Academic Learning Compact (SMALC) questions, as required by the State University Board of Governors.

Part I (5%)

1. The coefficient of determination indicates the :

a degree of statistical significance of the independent variables in a regression.

b degree of similarity between two variables in a regression.

c proportion of variation in the dependent variable attributable to variation in the independent

variable, or variables, in a regression.

d proportion of variation between two or more independent variables in a regression

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2. Suppose our regression results are: P = 40 + 1.38 Sqft + e, where P = price, in dollars, and Sqft is size, in square feet, of single-family houses. According to our model, as size increases by 1 square foot, price increases by…

a $1.38

b $13.80

c .138 thousand dollars

d a and c

3. Suppose the estimated coefficient, b1, is 12.5, the standard error is .98, and you are testing, at the 5% level of significance, that the hypothesis that β1 = 0. You calculate a t-ratio of:

a .08

b .625

c 12.76

d 12.25

4. Suppose our estimated regression equation is ln(price) = a + .75 ln (quantity) + e. From this equation we can calculate price elasticity of demand as:

a .25

b -.287 (in other words, the log of .75)

c .75

d We don’t have enough information to calculate price elasticity from the equation.

5. The coefficient of correlation indicates the:

a degree of dispersion between two variables.

b degree of central tendency between two variables.

c degree of linear association between two variables

d degree to which one variables determines the behavior of another variable.

Project 3

ECO 3431 Fall 2021

Part II (95%)

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Retrieve and use the data set from Gujarati (Chapter 13, Table 13_6), posted in the ‘Projects’ module on the class site :

The data set has 4 columns: date, close (closing price of IBM stock), time period, and lnclose (natural log of closing price of IBM stock), January 2000 to August 2002.

Problem 1: Using variables from Gujarati’s IBM data set:

1a. Plot a time series graph of the closing stock price.

1b. Plot the autocorrelation and partial correlation functions of the closing stock price, and save them as PDFs.

1c. Examine the ACF and PACF of the closing stock price. Briefly explain what they suggest about the statistical process generating the IBM stock price.

Problem 2:

2a. Plot a time series graph of the 1st difference of the closing stock price.

2b. Plot the ACFs and PACFs of the 1st difference of the closing stock price, and save them as PDFs.

2c. Examine the ACF and PACF of the 1st difference of the stock price. Briefly explain what they suggest about the statistical process generating the IBM stock price.

3. Identify the probable time series formulation of the stock price. What in the information you have examined leads you to your conclusion?

Submit your answers for problems 1c, 2c, and 3 along with pdfs of the ACFs and PACFs.

Similarity Between Two Variables In A Regression

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