
Suppose Alphonso’s town raises the
Why is file
How is his budget constraint affected from all three changes? Explain.

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Chapter 2 Solutions
Principles of Economics 2e
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- Suppose you have sales data of surf board and run a following regression equation sales = Bo+B₁price + B2 season + u Price is measured in dollars, and the variable season takes the value 1 for spring, 2 for summer, 3 for fall, and 4 for winter. Interpret the coefficient for season in words.arrow_forwardModel Residual 6678.74195 203982.577 1 6678.74195 795 256.581858 F(1, 795) Prob > F R-squared 26.03 = 0.0000 - 0.0317 Adj R-squared 0.0305 Total 210661.319 796 264.649898 Root MSE 16.018 wage Coefficient Std. err. t P>|t| [95% conf. interval] bachelors _cons 6.633368 24.0767 1.300169 .6577875 5.10 36.60 0.000 0.000 4.081198 9.185537 22.78549 25.3679 reg wage bachelors educ Source ss df MS Number of obs 797 Model Residual 54084.0835 156577.235 794 2 27042.0417 197.200548 F(2, 794) Prob > F R-squared 137.13 0.0000 0.2567 Adj R-squared 0.2549 Total 210661.319 796 264.649898 Root MSE 14.043 wage Coefficient Std. err. t P>|t| [95% conf. interval] bachelors educ cons -.3159352 1.224789 3.063893 .1976123 -17.99629 2.774185 -0.26 0.797 15.50 0.000 -6.49 0.000 -2.720143 2.088273 2.675989 3.451798 -23.4419 -12.55069 Select the one incorrect answer. O The effect of having a bachelor's degree on wage is overestimated in the simple linear regression. In the multiple linear regression, the effect of…arrow_forwardWhich of the five Types of Arguments do you think best fits your preconceptions of your Argumentative Paper? Why? on addictionarrow_forward
- reg avgprice mpgcity Source SS df MS Number of obs F(1, 91) 93 49.76 Model Residual 3.0345e+09 5.5495e+09 1 3.0345e+09 91 60983890.4 Prob > F 0.0000 R-squared = 0.3535 Total 8.5840e+09 92 93304579.4 Adj R-squared = Root MSE = 0.3464 7809.2 avgprice Coef. Std. Err. t P>|t| [95% Conf. Interval] mpgcity -1021.944 cons 42366.05 144.8745 3339.859 12.68 0.000 35731.83 49000.27 reg avgprice mpgcity weight Source SS df MS Model Residual 3.6663e+09 4.9177e+09 2 1.8332e+09 90 54640959.2 Number of obs F(2, 90) Prob > F R-squared Total 8.5840e+09 92 93304579.4 Adj R-squared Root MSE 93 33.55 0.0000 = 0.4271 0.4144 7392 avgprice Coef. Std. Err. t P>|t| [95% Conf. Interval] mpgcity weight -290.7165 8.262269 255.0389 2.429698 cons 622.5692 12676.12 -1.14 3.40 0.05 0.257 0.001 0.961 -797.3957 215.9627 3.43525 -24560.76 13.08929 25805.9arrow_forwardreg avgprice mpgcity Source SS df MS Number of obs F(1, 91) 93 49.76 Model Residual 3.0345e+09 5.5495e+09 1 3.0345e+09 91 60983890.4 Prob F = 0.0000 R-squared = 0.3535 Total 8.5840e+09 92 93304579.4 Adj R-squared Root MSE = 0.3464 7809.2 avgprice Coef. Std. Err. t P>|t| [95% Conf. Interval] mpgcity _ cons -1021.944 42366.05 144.8745 3339.859 12.68 0.000 35731.83 49000.27 reg avgprice mpgcity weight Source SS df MS Number of obs 93 F(2, 90) 33.55 Model Residual 3.6663e+09 4.9177e+09 2 1.8332e+09 90 54640959.2 Prob > F R-squared = 0.0000 = 0.4271 Total 8.5840e+09 92 93304579.4 Adj R-squared Root MSE = = 0.4144 7392 avgprice Coef. Std. Err. t P>|t| [95% Conf. Interval] mpgcity weight -290.7165 255.0389 cons 8.262269 2.429698 622.5692 12676.12 -1.14 0.257 3.40 0.001 0.05 0.961 -797.3957 215.9627 3.43525 -24560.76 13.08929 25805.9arrow_forwardThe esimated regression equation is as follows rent = 412 0.00013pop (18.17) (0.00015) The numeric value in the parenthesis represents standard error. (For example, Var(1) = 0.00015) Choose all correct answer Significance level is 1% B₁ is not statistically significant B₁ is statistically significant Confidence Interval does include 0 There is no strong evidence to reject the null hypothesis of no effect of population on rent. There is strong evidence to reject the null hypothesis of no effect of population on rent." Confidence Interval does not include 0 010arrow_forward
- The esimated regression equation is as follows rent = 412 0.00013pop (18.17) (0.00015) The numeric value in the parenthesis represents standard error. (For example, V Var(61) = 0.00015) Choose one correct answer 0.05 Pvalue < 0.1 P value <0.01 0.01 Pvalue < 0.05 0.1 < Pvaluearrow_forward4. Effects of a tariff on international trade The following graph shows the domestic demand for and supply of oranges in Honduras. The world price (PW��) of oranges is $535 per ton and is displayed as a horizontal black line. Throughout the question, assume that all countries under consideration are small, that is, the amount demanded by any one country does not affect the world price of oranges and that there are no transportation or transaction costs associated with international trade in oranges. Also, assume that domestic suppliers will satisfy domestic demand as much as possible before any exporting or importing takes place. A graph plots domestic supply and demand for oranges in Honduras with price in dollars per ton on the y-axis ranging from 500 to 850 in increments of 35 and quantity in tons of oranges on the x-axis ranging from 0 to 400 in increments of 40. The graph plots a downward sloping straight line curve labeled domestic demand ranging from (0, 850) to (400, 500),…arrow_forwardSuppose you are working as a manager of an organic grocery store and want to analyze how income affects grocery spending. In the dataset, grocery_spending measures the amount spent on groceries in dollars, and income represents individual income, also measured in dollars. The following regression results present estimates from two different specifications. reg grocery_spending income Source SS df MS Model Residual 20685432.5 29227282.9 1 20685432.5 9,998 2923.31295 Number of obs = F(1, 9998) Prob > F R-squared => 10,000 7076.02 0.0000 Total 49912715.4 9,999 4991.77071 Adj R-squared = Root MSE = 0.4144 54.068 grocery_sp~g Coefficient Std. err. t P>|t| [95% conf. interval] income _cons 0033984 .0000404 84.12 2113.24 1.144063 1847.14 0.000 0.000 .0033192 2110.998 .0034776 2115.483 ⚫ gen Inincome-In (income) .reg grocery_spending Inincome Source SS df MS Number of obs = 10,000 Model Residual 24748934.7 25163780.7 9,998 1 24748934.7 2516.88144 F(1, 9998) Prob > F R-squared = 9833.17 " =…arrow_forward
- Both the level-level and level-log regression models presented above use the same number of observations. What does this suggest about the variables used in each model?arrow_forwardCalculate the R-squared value for the missing section. (Round totwo decimal places. You do not need to show your work.) Based on this answer, which model specification provides a better fit to the data? Why? On the level-level model specification, what is the null hypothesiswe would like to reject? Briefly describe the null hypothesis in words.arrow_forwardYou are working at DoorDash and want to understand how delivery time affects the amount of tips received. Tips are measured in dollars, and delivery time is measured in minutes. Specifically, you run the following regression. Tips = 3 + ẞ₁Time + u 0 1 Choose all the correct answers: (a) ẞ represents the average delivery time when tips are zero from the population data 0 (b) B₁ represents the causal effect of delivery time on tips, assuming the zero conditional mean assumption holds B (c) If E[U] = 0, Tips = ß + ß> 0 (d) The model assumes a linear relationshiparrow_forward
- Economics (MindTap Course List)EconomicsISBN:9781337617383Author:Roger A. ArnoldPublisher:Cengage LearningPrinciples of Economics 2eEconomicsISBN:9781947172364Author:Steven A. Greenlaw; David ShapiroPublisher:OpenStax




