Assignment 6

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.

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As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…

18. A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: D. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?

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…

(2)What would the consequence be for a regression model if theerrors were not homoscedastic?

If a regression equation contains an irrelevant variable, the parameter estimates will be Select one: a. Consistent and unbiased but inefficient  b. Consistent and asymptotically efficient but biased c. Consistent, unbiased and efficient. d. Inconsistent

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2. Which of the following types of regressions will always have a binary outcome variable? (A) Probit (B) Difference-in-differences (C) Regression discontinuity (D) (A) and (B) will both have binary outcome variables

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

1. Consider a linear regression model y = XB + € with E(e) = 0. The bias of the ridge estimator of 3 obtained by minimizing Q(B) = (y — Xß)¹ (y — Xß) + r(BTB), for some r > 0, is ——(X²X + r1)-¹8 1 (X¹X +rI)-¹3 r -r(XTX+rI) ¹8 r(X¹X+r1) ¹3

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

q9-  Which property of linear regression is related with the size effects of individual units in a cross-section data? Select one: a. Heteroskedasticity b. Endogeneity c. Autocorrelation d. Non-normality Clear my choice

Conduct a regression analysis

A linear regression model for the revenue data for a company is R=27.1t+203 where R is total annual revenue and t is time since 1/31/02 in years. 12 months 12 months 12 months Billions of Dollars Revenue Gross Profit 12 months 12 months ending 1/31/02ending 1/31/03ending 1/31/04 ending 1/31/05 ending 1/31/06 500- 201 49 236 54 255 60 500- 277 65 (A) Draw a scatter plot of the data and a graph of the model on the same axes. OA. B. O.C. KICB Q 2 316 72 500- oo D. 500- Q G

Economic

What is the functional form of this equation? What are the advantages and limitations of this functional form?  Interpret precisely the coefficients of Px and Py in the regression.

Please no written by hand solution a) Suppose in a regression of weekly salaries on years of schooling for males(m) and females(f), the following results are obtained. Wm = 50Sm and Wf = 40Sf. where Wm (Wf) denotes weekly salary and Sm (Sf) denotes years of schooling for males and females respectively. 50 and 40 are the coefficients on schooling in the male and female regression respectively. On average, men have 12 years of schooling and women have 10 years of schooling. What is the average male-female wage differential? Is this a good estimate of discrimination? Explain why/why not. Using the information in the question, what would you propose as a better estimate of discrimination? State any assumptions that you use and explain your answer.

1. You are interested the causal effect of X on Y, B1. Suppose that X, and X2 are uncorrelated. You estimate B1 by regressing Y onto X1 (so that X2 is not included in the regression). Does this estimator suffer from omitted variable bias due to the exclusion of X2? (a) Yes (b) No (c) Maybe 2. Omitted variable bias violates which of the following assumptions: (a) The conditional distribution of u, given X1i X2i, ...Xki has a mean of zero (b) (Xi, X2i...Y;), i = 1, ., n are independently and identically distributed (c) Heteroskedasticity (d) Perfect multicollinearity

A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.

QUESTION 1 [10 marks] Given the following table, use the matrix method to derive the constant and slope parameters of the sample regression function: Productivity index = f(Daily sleep hours). X and Y stand for the daily sleep hours and productivity index respectively. X (Daily sleep hours) Y (Productivity index) 30 4 35 5 40 6 65 8. 80

If the number of miles for a delivery is 5 & the number of minutes is 30, what is the estimated overhead cost associated with that delivery rounded to two decimals?

True or False For a linear regression model including only an intercept, the OLS estimator of that intercept is equal to the sample mean of the independent variable.

Consider the following data on production volume (x) and total cost (y) for a particular manufacturing operation. Excel File: data14-29.xlsx Production Volume (units) Total Cost ($) 400 4,000 450 5,000 550 5,400 600 5,900 700 6,400 750 7,000 The estimated regression equation is ŷ = 1246.67 +7.6x. Use a = 0.05 to test whether the production volume is significantly related to the total cost. Complete the ANOVA table. Enter all values with nearest whole number, except the F test statistic (to 2 decimals) and the p-value (to 4 decimals). Source of Degrees of Sum Mean Variation Freedom of Squares Square p-value Regression Error Total What is your conclusion? - Select your answer -

2. Consider a two variable regression model, which satisfies all the Gauss Markov assumptions except that the error variance is proportional to X² i.e.E(u?) = o²X? Y₁ = B₁ + B₂X₁ + Ui How would you obtain the best linear unbiased estimates from the above regression.

Please provide me with the correct answer, along with the calculations, and do not use any AI tools

The linear regression equation, Y= a + bX, was estimated. The following computer output was obtained: DEPENDENT VARIABLE: Y OBSERVATIONS: 15 VARIABLE INTERCEPT Multiple Choice O X R-SQUARE 0.6010 PARAMETER ESTIMATE 412.18 0.6358 F-RATIO 19.58 STANDARD ERROR 102.54 0.1765 P-VALUE ON F 0.0001 T-RATIO P-VALUE 0.0015 0.0032 In the regression above, the parameter estimate of b (on the variable X) indicates that 4.02 3.60 X increases by 0.1765 units when Yincreases by one unit. X increases by 0.6358 units when Y increases by one unit. Y increases by 0.1765 units when X increases by one unit. Y increases by 0.6358 units when X increases by one unit. Y increases by 3.60 units when X increases by one unit.

Question: a) Calculate Regression Analysis of the data. b) Calculate Profit of a shop for 45 units sales

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