Salary vs School and Main Effects Dec 11

Economic

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?

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?

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

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…

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

Please answer fast

Determine the mode choice (personal vehicle or bus system) for the following regression model:              Utility Function: Umode – (8.333 x 10-4)*(Access time in sec) – (6.667 x 10-4)*(Wait Time in sec) – (5.00 x 10-4)*(Riding time in sec) – (1.40)*(Cost, $)   PARAMETER PERSONAL VEHICLE CITY BUS SYSTEM MODE CONSTANT -0.01 -0.07 ACCESS TIME (SECS) 300 600 WAITING TIME (SECS) 0 900 RIDING TIME (SECS) 1,500 6,000 COST (DOLLARS) $1.50 $1.00

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…

Table 1 Descriptive statistics and a summary of zero-order correlations and regression values for the Dark Triad (predictor variables) and chronotype (outcome variable). r ẞ (t) M (SD) Dark Triad -.14* [-.23**] 0.00 (0.80) Narcissism -.04 [-.05] .04 (0.60) 0.34 (0.18) Entitlement/ -.20** [-.36**] -.24 (-3.33**) 0.19 (0.26) exploitativeness Grandiose exhibitionism -.05 [.07] -.05 (-0.69) 0.30 (0.26) Leadership/authority .05 [.07] .18 (2.29*) 0.40 (0.26) Psychopathy -.13* [-.18**] -.09 (-1.05) 142.56 (27.99) Primary -.07 [-.11] .16 (-1.07) 75.16 (16.57) Secondary -.18** [-.26**] -.20 (-2.63**) 67.40 (14.54) Machiavellianism -.14* [-.23**] -.09 (-1.07) 75.16 (16.57) *p < .01, **p < .001, '-' denote variables not included in the regression. Correlations in brackets are the disattenuated values using the reliability for the scales. 1. turn your attention to Table 1 in Jonason et al. (2013). The first column represents the correlation between the chronotype index and dark traits while the…

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

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

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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

I need help with c

The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985   Correlation matrix: X1 X2 X1 1 X2 0.950 1   Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?

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

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.

Conduct a regression analysis

q9-

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The error term in a regression equation is said to exhibit homoskedasticty if ____________. a) it has the same variance for all values of the explanatory variable(s). b) None of the other possible answers correctly finish the sentence. c) it takes the value of one given any values of the explanatory variable(s). d) it has the same value for all values of the explanatory variable(s). e) it has a zero conditional mean.

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.

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.

Thirty data points on Y and X are employed to estimate the parameters in the linear relation Y = a + bX. The computer output from the regression analysis is DEPENDENT VARIABLE: R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 30 0.5300 13.79 0.0009 VARIABLE PARAMETER STANDARD T-RATIO P-VALUE ESTIMATE ERROR INTERCEPT 93.54 46.210 2.02 0.0526 -3.25 0.875 -3.71 0.0009 The percentage of the total variation in Y NOT explained by the regression is percent,

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

This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years State Effects? Time effects? (1) 0.41* (0.056) 1982-88 no no (2) (3) (4) -0.62** -0.76*** -0.42 (0.39) (0.33) (0.38) 0.023 (0.078) -0.014 (0.084) -0.023 -0.075 (0.053) (0.064) 0.034 -0.109*** (0.058) (0.058) no yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables Time effects=0 (5) -0.76** (0.36) 0.041 0.083 (0.111) (0.115) 0.006 0.015 (0.005) (0.011) -0.068* (0.016) 1.66* (0.66) 1982-88 1982-88 1982-88 1982-88 yes yes yes yes yes yes (6) -0.46 (0.39) -0.004 (0.022) 0.043 (0.101) 0.007 (0.005) -0.064*…

Consider the output here from a regression in R. What is 3₂? Coefficients: Estimate (Intercept) 1.708 5.404 -1.478 9.531 X1 X2 X3 Std. Error 0.555 2.792 0.6 2.758