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The table below is the quarterly data of the Shiller Real Home Price Index. To remove the seasonality a centered 4-point moving average is used.

Year	Real Home Price Index
1.1	106.5058955
1.2	109.3296707
1.3	101.2225795
1.4	100.0466076
2.1	105.8948393
2.2	103.8986687
2.3	103.9743275
2.4	114.7133093
3.1	114.199126
3.2	115.4621261
3.3	115.3166155

What is the value of ?

Write the answer as following:

XXX.XX

The table below is the quarterly data of the Shiller Real Home Price Index. To remove the seasonality a centered 4-point moving average is used.

Year	Real Home Price Index
1.1	106.5058955
1.2	109.3296707
1.3	101.2225795
1.4	100.0466076
2.1	105.8948393
2.2	103.8986687
2.3	103.9743275
2.4	114.7133093
3.1	114.199126
3.2	115.4621261
3.3	115.3166155

What is the value of ?

Write the answer as following:

XXX.XX

A time series is a:

set of measurements, ordered over time, on a particular quantity of interest.

model that attempts to analyze the relationship between a dependent variable and one or more independent variables.

model that attempts to forecast the future value of a variable.

set of measurements on a variable collected at the same time or approximately the same period of time.

In a multiple regression model, the following statistics are given: SSE = 100, R2 = 0.995, K = 5, and n = 15. Determine the multiple coefficient of determination adjusted for degrees of freedom.

Write the answer as following:

X.XXX

In examining the determinants of income, data were collected regarding the characteristics of 45 adults, and the regression Y = β0 + β1X1 + β2X2 + β3X3 +ε was used, where Y is the annual income (in thousands of dollars), X1 is the person's age, X2 is his/her years of education, and X3 is a dummy variable = 1 if the adult is female.

If you get

= 26.3 + 1.38x1 + 2.98x2 - 0.76x3 + 0.34(x2 ∙ x3) when you run the regression, how would you interpret the coefficient on his/her years of education?

For each additional year of education, women approximately earn $340 less than men.

For each additional year of education, women approximately earn $1,100 less than men.

For each additional year of education, women on average earn $420 less than men.

For each additional year of education, women on average earn an additional $3,320.

A multiple regression analysis involving three independent variables and 25 data points results in a value of 0.769 for the unadjusted multiple coefficient of determination. The adjusted multiple coefficient of determination is:

0.736

0.591

0.877

0.385

A loan officer is interested in examining the determinants of the total dollar value of residential loans made during a month. She used Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε to model the relationship, where Y is the total dollar value of residential loans in a month (in millions of dollars), X1 is the number of loans, X2 is the interest rate, X3 is the dollar value of expenditures of the bank on advertising (in thousands of dollars), and X4 is a dummy variable equal to 1 if the observation is either June, July, or August.

Suppose that she obtained

= 3.8 + 0.23x1 - 1.31x2 + 0.032x3 + 1.05x4 by using data from the past 24 months. How would we interpret the coefficient on x4?

On an average, we would expect an additional 1.05 loans per month during June, July, and August.

There is no difference in the total residential loan amount between the months of June and September.

On an average, we would expect an additional 1.05% increase in loans during June, July, and August.

On an average, we would expect an additional $1.05 million in loans during June, July, and August.

Suppose that she obtained

= 3.8 + 0.23x1 - 1.31x2 + 0.032x3 + 1.05x4 - 0.22x3x4 by using data from the past 24 months. How would we interpret the coefficient of x3x4?

Advertising is more effective during June, July, and August.

Advertising is less effective in June, July, and August.

On an average, we would expect $220,000 more in loans during the months of June, July, and August.

On an average, we would expect $220,000 less in loans during the months of June, July, and August.

variable = 1 and is used if the adult is female. You run the regression and obtain the equation

In examining the determinants of income, data were collected regarding the characteristics of 45 adults, and the regression lnY = β0 + β1 lnX1 + β2 lnX2 + β3X3 + ε was used, where Y is the annual income (in thousands of dollars), X1 is the adult's age, X2 is his/her years of education, and X3 is a dummy

= 6.3 + 0.91 lnx1 + 1.3 ln x2 - 0.05x3.

How would you interpret the coefficient on years of education?

For every one percent increase in his/her years of education, we would expect the income to increase approximately by $1,310, assuming that all the other independent variables in the model are held constant.

For every one percent increase in his/her years of education, we would expect the income to increase on average by 1.3 percent, assuming that all the other independent variables in the model are held constant.

For every addition in his/her years of education, we would expect the income to increase on average by $1,310, assuming that all the other independent variables in the model are held constant.

For every 1.3 percent increase in his/her years of education, we would expect the income to increase approximately by $1,000, assuming that all the other independent variables in the model are held constant.