The demand for a commodity is given by Q = Bo + B₁P+u, where Qdenotes quantity, P denotes price, and u denotes factors other thanprice that determine demand. Supply for the commodity is given byQ You, where v denotes factors other than price that determinesupply. Suppose that u and v both have a mean of zero, have varianceso and o2, and are mutually uncorrelated.Solve the two simultaneous equations to show how Q and P depend onu and v.

Answered: Image /qna-images/answer/a8c1d42f-9510-49a7-85cf-8b177df23db0.jpg

Answered: The demand for a commodity is given by…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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7. In regression analysis, if the independent variable is measured in pounds, the dependent variable must also be in pounds b. a. must be in some unit of weight can be any units d. с. cannot be in pounds
A sociologist is interested in determining if those shopping at the mall tend to spend more money if they are with friends versus if they are shopping alone. She interviews 8 shoppers who regularly shop at the mall, both alone and with friends, and inquires about the following two variables: X1-amount spent when they last shopped alone and X2 amount spent when they last shopped with their friends. Below is the data she collected: Shopper X1 X2 1 35 72 10 15 3 80 100 55 90 35 55 23 58 25 25 138 210 (a) What type of test do we need to conduct and what assumptions are necessary? (b) Assuming our assumptions in (a) are valid, by hand, conduct an appropriate test of significance at 2% level of significance to determine if shoppers spend more money when they are with friends than when they are alone using a rejection region approach.
The equation of the line containing the points (−2,−4) and (2,5) is y=2.25x+0.5. Compute the sum of the squared residuals of the given data set for this line. The sum of the squared residuals for the line containing the points (−2,−4) and (2,5) is
In a study a group of 50 adults were asked how comfortable they felt around 5 types of animals: dogs, cats, cows, horses and sheep. For each type of animal, each respondent gave a number between 0 and 100 to indicate their comfort level (0 being 'hate being around' to 100 being 'love being around'). The data are given in Table 10, along with the loadings of the first two principal components. Variable (b) (c) X₁ X₂ X3 X4 X5 Animal Mean Dogs 74.5 Cats 76.2 Cows 45.2 Horses 51.2 Sheep 48.3 Table 10 Standard Deviation Component 1 Loadings 8.1 7.5 7.1 8.6 6.9 0.724 -0.684 0.080 -0.040 -0.017 Component 2 0.387 0.425 0.464 0.449 0.503 (a) The data were not standardized prior to being analyzed using principal components. Give 2 reasons why the decision not to standardize is reasonable for these data. Interpret each of the 2 components. Justify your answer. Suppose a decision was taken to standardize the data after all. Calculate the values of the standardized variables for a person whose…
The data in the table represent the weights of various domestic cars and their miles per gallon in the city for the 2008 model year. For these data, the least-squares regression line is y = - 0.006x + 43.875. A twelfth car weighs 3,425 pounds and gets 13 miles per gallon. (a) Compute the coefficient of determination of the expanded data set. What effect does the addition of the twelfth car to the data set have on R2? (b) Is the point corresponding to the twelfth car influential? Is it an outlier? Data Table Click the icon to view the data table. Weight |(pounds), x Miles per Gallon, y Car 1 3,770 20 Car 2 3,980 19 Car 3 3,530 19 Car 4 3,175 22 Car 5 2,580 27 Car 6 3,729 20 Car 7 2,607 26 Car 8 3,776 19 Car 9 3,311 22 Car 10 2,999 27 Car 11 2,755 27
35. The regression analysis below relates US annual energy consumption in trillions of BTUs to the independent variables "US Gross Domestic Product (GDP) in trillions of dollars" and "average gas mileage of all passenger cars in miles per gallon (mpg)." Which of the two independent variables is significant at the 0.01 level? GDP only Average car gas mileage only Both independent variables Neither independent variable mergy Consumption, GDP, and Gas Mileage ource US Energy Consumption (in trillion BTUs) vs. GDP (in $trillions) and Average Car Gas Mileage (in mpg) Regression Statistics Multiple R R² Adjusted R Standard Error Observations Intercept GDP (Strillions) Avg. car gas mileage (mpg) 0.9709 0.9426 0.9359 1,943 20 F test Results F value Signif. F 139.64 Coefficients Std Error t Stat 63,672. 9,749 3,853 696 -70.50 697 6.53 5.53 -0.10 0.0000 P-value 0.0000 0.0000 0.9206
Two refreshment stands kept track of the number of cases of soda they sold weekly during the summer time, as shown on the dot plots below. Stand A Stand B 10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19 Number of Cases Sold Number of Cases Sold What is the difference between the modes of the number of cases of soda sold? OA. 11 ов. 2 ос. 5 OD. 16
We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictor variable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from Climatology Report No. 77-3 of the Department of Atmospheric Science, Colorado State University, showed the following relationship between elevation (in thousands of feet) and average number of frost-free days per year in Colorado locations. A Minitab printout provides the following information. Predictor Constant Elevation Coef 315.00 -29.166 SE Coef 28.31 3.511 I 11.24 -8.79 P 0.002 0.003 S = 11.8603 R-Sq = 96.44 Notice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation ŷ = a + bx. (c) The printout gives the value of the…
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose that the variable for consumption is measured with error, so conss = cons + e, where conss is the mismeaured variable, cons is the true variable, e is random, i.e., e is independent of all the regressors. What would we expect and why? A) OLS estimators for the coefficients will all be biased B) OLS estimators for the coefficients will all be unbiased C) ALL the standard errors will be bigger than they would be without the measurement error D) both B and C
One of the lines in the following scatter plot is the regression line fitted to the data. Which of the statements are correct?
A random sample of 136 adults were asked to report the number of hours per week the spent on a computer and their number of years of education. The linear model equation below describes the relationship between the mean computers hours and years of education. computers == 9.12 ++ 0.8 ×× education Based on this linear model, which of the following statements is correct? a) An adult who has no years of education is expected to spend 9.12 hours per week on a computer. b) An adult who has 1 more year of education than another is expected to spend 9.12 more hours per week on a computer. c) An adult who spends 1 more hour per week on a computer than another is expected to have had 9.12 more years of education. d) An adult who spend zero hours per week on a computer is expected to have 9.12 years of education.

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