MGEC72-Assignment-3

(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?

d/My courses / Faculty Of Economics & Administratiive Sciences / ECON309 / Finals / ECON 309 Fin 13. In the simple linear regression model, the regression slope a. O a. indicates by how many percent Y increases, given a one percent increase in X. ut of O b. represents the elasticity of Y on X. uestion Oc. when multiplied with the explanatory variable will give you the predicted Y. O d. indicates by how many units Y increases, given a one unit increase in X. nage

Just number 3 please thankyou

Topic: Regression    Problem 3:  A researcher is interested in determining whether there is a relationship between grades and hours studied for statistics.   Hours studied(X) Grade on final(Y) 1 20 2 30 4 40 7 60 6 70 7 78 8 83 9 98   1- You are given data for Xi (independent variable) and Yi (dependent variable).      2- Calculate the correlation coefficient, r:             r =           -1 ≤ r ≤ 1     3- Calculate the coefficient of determination: r2 = =                0 ≤ r2 ≤ 1 This is the proportion of the variation in the dependent variable (Yi) explained by the independent variable (Xi)                4- Calculate the regression coefficient b1 (the slope):              b1 = = Note that you have already calculated the numerator and the denominator for parts of r.  Other than a single division operation, no new calculations are required.   5- Calculate the regression coefficient b0 (the Y-intercept, or constant):…

8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OO

Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?

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11. Which of the following statements is not true about multicollinearity? (a) Perfect multicollinearity will prevent you from being able to estimate a linear regression model. (b) Imperfect mulitcollinearity affects the individual t-statistics of the regressors. (c) Multicollinearity is defined as a linear relationship between different independent variables. (d) Imperfect multicollinearity affects model validity of the model. (e) The least squares estimators are unbiased in the presence of imperfect multicollinearity.

5. Among important factors that affecting the price of land lot are size, number of mature trees and distance to the lake. Using data for 60 recently sold land lots are shown below: B 1 SUMMARY OUTPUT 2 3. Regression Statistics 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 0.4924 0.2425 0.2019 40.24 8 Observations 60 9. 10 ANOVA Significance F 5.97 11 df S MS 9676.6 0.0013 12 Regression 13 Residual 3 29,030 90,694 56 1619.5 14 Total 59 119,724 15 16 Coefficients Standard Error t Stat P-value 0.0331 0.2156 17 Intercept 51.39 23.52 2.19 18 Lot size 0.700 0.559 1.25 19 Trees 0.679 0.229 2.96 0.0045 20 Distance -0.378 0.195 -1.94 0.0577 a) Write the regression equation b) What is the standard error of estimate? Interpret its value. c) What is the coefficient of determination? Interpret its value. d) What is the adjusted coefficient of determination? Interpret its value. e) Test the validity of the model. f) Interpret each of the coefficients. g) Test at 5% level of…

Question 2 B ..  Full explain this question and text typing work only     We should answer our question within 2 hours takes more time then we will reduce Rating Dont ignore this line

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d. If the director used these 4 weeks of data to create a linear regression, what does that linear regression formula suggest for this week's forecast of employee appointments? What does the regression analysis suggest in general about employee appointments for Director Very Busy?

Kindly provide a correct answer and a detailed explanation; otherwise, I will have to give multiple downvotes. Please avoid using ChatGPT and refrain from providing handwritten solutions; otherwise, I will definitely give a downvote. Also, be mindful of plagiarism. Answer completely and accurate answer. Rest assured, you will receive an upvote if the answer is accurate.

Question 9 Regression analysis was applied between demand for a product (Y) and the price of the product (X), and the following estimated regression equation was obtained. Y = 120 - 15 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to: O Increase by 120 units Decease by 30 units O Increase by 2Q units Next « Previous Submi No new data to save. Last checked at 9:51pm

1. Suppose that you have following data: Variable Description CEO salary measured in thousands of $ Firm's sale measure in millions ofS Return on equity in percent Salary sales roe *Return on equity is a measure of financial performance calculated by dividing net income by shareholders' equity. Your estimated regression is given by log (salary) = 4.322 + 0.276 log(sale) + 0.0215roe - 0.0008roe?, R = 282, n = 209. (324) (0.033) (0.0129) (0.00026) a) Is the effect of all independent variables statistically equal to 0? b) Interpret the coefficient on log(sale). c) Interpret the effect of roe on log(salary). • Without more information, your interpretation of the effect of roe on log(salary) should include answers to these sub-question. Should the roe be included in this model? il. Comment on relationship between roe and log(salary): is it U-shaped or inverse U-shaped? What is the turning point? How would you interpret this point? Plot log(salary) vs roe. v. ii. iv. Compute predicted value…

Styles 6. The following two regression models are Probit and Logit respectively: Pr(Y=1|X)=(Bo+Bi xXr+Bzx X2) Pr(Y=1|X)=F(Bu + B₁ × Xs + B₂ × X2) (a) what functions do and F represent? (b) How do Probit, and Logit ensure that the predicted probabilities are always between 0 and 1? (c) Sketch a graph of the Y= $(Z) function. (Z on the horizontal axis, Y on the vertical axis) (d) What estimation method is used to estimate the coefficients in a Probit/Logit model? (e) What are the two measures of fit for models with binary dependent variables? Focus 88 B E

A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. False

The table to the right contains price-demand and total cost data for the production of projectors, where p is the wholesale price (in dollars) of a projector for an annual demand of x projectors and C is the total cost (in dollars) of producing x projectors. Answer the following questions (A) - (D). (A) Find a quadratic regression equation for the price-demand data, using x as the independent variable. X 270 360 520 780 The fixed costs are $. (Round to the nearest dollar as needed.) ITTI y = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) Use the linear regression equation found in the previous step to estimate the fixed costs and variable costs per projector. The variable costs are $ per projector. (Round to the nearest dollar as needed.) (C) Find the break even points. The break even points are (Type ordered pairs. Use a comma to separate answers as needed. Round to the nearest integer as…

Demand Estimation for The San Francisco Bread Company Consider the hypothetical example of The San Francisco Bread Company, a San Francisco-based chain of bakery/cafes.  San Francisco Bread Company has initiated an empirical estimation of customer traffic at 30 regional locations to help the firm formulate pricing and promotional plans for the coming year.  Annual operating data for the 30 outlets appear in the attached Table 1. The following regression equation was fit to these data:                     Qi = b0 + b1Pi + b2Pxi + b3Adi + b4Ii + uit. Where:            Q is the number of meals served,                         P is the average price per meal (customer ticket amount, in dollars),                         Px is the average price charged by competitors (in dollars),                         Ad is the local advertising budget for each outlet (in dollars),                         I is the average income per household in each outlet’s service area,                         ui…

QUESTION 17 I am trying to figure out how to measure an athlete's productivity. So, I have run a linear regression of a NBA player's salary (dependent variable) on a player's statistics including average points, assists, rebounds per game, and turnovers per game (the independent variables). The final model is: Salary = 1,000,000 * Points per game + 50,000 * Assists per game + 20,000 * Rebounds per game - 30,000 * Turnovers per game %3! Last year, Lebron James averaged 25 points per game, 8 assists per game, 8 rebounds per game and 4 turnovers per game. What is Lebron's predicted salary?

2.9. Based on price statistics of 26 properties sold (Y USD/m? ) linear correlation coefficients were calculated with potential explanatory variables X,,X2,X3, X4 . Vector R, and matrix R are listed below 0,3 0,6 0,5 1 Ro= R = -0,7 0,6 0,6 1 0,4 0,8 0,8 0,7 1] The critical value of the linear correlation coefficient was assumed to be r* =0, 55. Perform the selection of explanatory variables for the model using the correlation coefficient analysis method.

10- What would be the consequences for the OLS estimator if heteroskedasticity is present in a regression model but ignored?* a. It will be biased b. It will be inconsistent c. It will be inefficient d. All of (a), (b), and (c) will be true

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When the regression line passes through the origin then: O The intercept is zero. O The regression coefficient is zero. O The correlation is zero. O The association is zero. O All of the above.

In a recent closely contested lawsuit, Apex sued Bpex for patent infringement. The jury came back today with its decision. The rate o return on Apex was rA = 3.7%. The rate of return on Bpex was only rg = 3.3%. The market today responded to very encouraging new about the unemployment rate, and M = 3.4%. The historical relationship between returns on these stocks and the market portfolio has been estimated from index model regressions as: Apex: A = 0.3% + 1.5PM Bpex: rg = -0.1% + 0.5rM. Required: a. What is the predicted returns for Apex & Bpex? b. Which company do you think won the lawsuit? Complete this question by entering your answers in the tabs below. Required A Required B What is the predicted returns for Apex & Bpex? Note: Do not round intermediate calculations. Round your answers to 1 decimal place. Apex Врех Predicted Returns % %

Please provide the correct answer along with the calculation. Do not use ChatGPT, otherwise I will give a downvote.

1. An analyst from your firm used a linear demand specification to estimate the demand for its product and sent you a hard copy of the results: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Price of X Income 0.38 0.14 0.13 20.77 150 df 2 147 149 SS 58.87 -1.64 1.11 10398.87 63408.62 73807.49 Coefficients Standard Error 15.33 0.85 0.24 MS 5199.43 431.35 t Stat 3.84 -1.93 4.63 F 12.05 P-value 0.00 0.06 0.00 Significance F 0 Lower 95% 28.59 -3.31 0.63 Upper 95% b. Which regression coefficients are statistically significant at the 5 percent level? a. Based on these estimates, write an equation that summarizes the demand for the firm's product. 89.15 0.04 1.56 C. When price is $10, what is the income elasticity for this product for an income level of 35?

Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. A. True B. False 10. 11. What is/are the objective(s) of regression analysis? A. To estimate the expected value of the dependent variable only B. To test hypotheses about the nature of the dependence between the variables only C. To predit the expected value of the dependent variable, given the value(s) of the dependent variables beyond the sample range only D. All of the above What is the population regression line or PRL? A. A line derived from the sample collected in a population B. A line that passes through the conditional means of the independent variable (X) C. Gives the average value of the dependent variable corresponding to each value of the independent variable D. Gives the average value of the independent variable corresponding to each value of the dependent variable 12.