# ECONOMICS

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What is the point price elasticity of demand when P=\$83?

Select one:

a. 0.018

b. -1.247

c. -1.351

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Question 9

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To maximize total revenue, what would you recommend if the company was currently charging P=\$83? If it was charging P=\$70?

Select one:

a. Price should be raised above both \$70 and \$83.

b. Raise the price if it is currently \$83; lower the price if it is currently \$70.

c. Lower the price if it is currently \$83; raise the price if it is currently \$70.

d. Price should be lower than both \$83 and \$70.

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Question 10

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Use your algebraically-derived direct demand function to determine an equation for TR and MR as functions of Q. What is total revenue when P=\$83 and when P = \$70?

Select one:

a. At P = \$83, TR = \$22,150; at P = \$70, TR = \$22,338.

b. At P = \$83, TR = \$45,676; at P = \$70, TR = \$50,122.

c. At P = \$83, TR = \$8,459; at P = \$70, TR = -\$3,442.

d. At P = \$83, TR = \$12,458; at P = \$70, TR = \$35,790.

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Question 11

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What is the total-revenue maximizing price and quantity, and how much revenue is earned there?

Select one:

a. P* = \$90, Q* = 246, TR* = \$21,698

b. P* = \$74.78, Q* = 299.82, TR* = \$22,421

c. P* = \$70, Q* = 318, TR* = \$22,338

d. P* = \$83, Q* = 266.87, TR* = \$22,150

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Illustration 7.3 (p. 262-3) describes time-series forecasting of new home sales, but you can see that the data is old. Visit the website indicated, click on the Historical Data tab, and download the first table “Houses Sold” (Excel file is sold_cust.xls). Look at the monthly data on the “Reg Sold” tab.

Only keep the dates beginning in January 2008, so delete the earlier observations. Keep only the US data, both the seasonally unadjusted monthly (column B) and the seasonally adjusted annual (column G). Make a new column of seasonally adjusted monthly by dividing the annual data by 12. Make a column called “t” similar to the book’s column 4 on page 263 (t will go from 1 to 105 through Sept. 2016); make a t2 column too (since, if you look at the data, you can see sales dropping until about mid-2011 then rising again; hence the quadratic). Also make a column “D” that is a dummy variable equal to one during the spring and summer months, similar to the book’s column 5.

Question 12

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Do you think making a seasonal adjustment will be useful, given what you observe at this point?

Select one:

a. No since, even though the unadjusted is more volatile than the adjusted, it is expected to be and thus making the adjustment will not improve the analysis.

b. Yes, since the seasonally unadjusted data traces a smoother path (graphically speaking) than the seasonally adjusted data.

c. No, since there is no discernible difference between the two data series, as far as is evident in the graph.

d. Yes since, even though they follow the same general trend, the seasonally unadjusted data is predictably more volatile than the seasonally adjusted data.

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Run four regressions:

1. seasonally unadjusted monthly as the dependent, and t and t2 as the independents,
2. seasonally unadjusted monthly as the dependent, and t, t2, and D as the independents,
3. seasonally adjusted monthly as the dependent, and t and t2 as the independents, and
4. seasonally adjusted monthly as the dependent, and t, t2, and D as the independents.

In interpreting your p-values, remember that, say, 1.0E-08 is 1.0 * 10^-8, which is 0.00000001

Question 13

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In comparing the regression results between model 1 and 2 (the unadjusted sales), it is notable that including the extra variable D in model 2

Select one:

a. increases the R2 as expected but reduces the adjusted R2, suggesting that D does not contribute to the explanatory power of the model.

b. makes the t and t2 variables statistically insignificant in model 2, whereas they were significant in model 1.

c. dramatically improves the explanatory power of the model.

d. increases the R2, but it is insignificant and has an unexpected sign.

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Question 14

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In comparing the regression results between models 2 and 3, it is notable that

Select one:

a. including the D variable in model 2 results in a much larger adjusted R2, suggesting that the inclusion of the dummy variable is necessary to boost predictive power.

b. dropping the D variable in model 3 pulls the R2 down, which is unexpected since D in model 2 is statistically insignificant.

c. the D variable in model 2 does a decent job of capturing the seasonal effect, since the results between the two models are not hugely different and D has the expected sign and is statistically significant.

d. the coefficient estimates for t and t2 change dramatically, even though the models are very comparable (unadjusted with a seasonal dummy is pretty close to seasonally adjusted).

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