Critically/quantitatively evaluate the following statement (true, false or uncertain): • In a linear model, the Generalized Instrumental Variable (GIV) estimator BGIV is iden- tical to the Ordinary Least Squares (OLS) estimator BOLS.
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A: Q=378-2P2P=378-QP=378-Q2P=189-Q2Now,TR=P×QTR=189-Q2QTR=189-Q22Thus,MR=∂TR∂QMR=189-Q
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A: Q=423-2P2P=423-QP=423-Q2P=211.5-Q2Now,TR=P×QTR=(211.5-Q2)QTR=211.5Q-Q22Thus,MR=∂TR∂QMR=211.5-Q
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- Determine which model is to selected.... Without plagiarismA multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?Explain when fixed effects, random effects or pooled OLS models are usually used.
- 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 $1.00A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
- The forecasting staff for the Prizer Corporation has developed a model to predict sales of its air-cushioned-ride snowmobiles. The model specifies that sales S vary jointly with disposable personal income Y and the population between ages 15 and 40, Z, and inversely with the price of the snowmobiles P. Based on past data, the best estimate of this relationship isS = k (YZ/ P)where k has been estimated (with past data) to equal 100.a. If Y = $11,000, Z = $1,200, and P = $20,000, what value would you predict for S?b. What happens if P is reduced to $17,500?c. How would you go about developing a value for k?d. What are the potential weaknesses of this model?Does the following dataset satisfy GARP?No written by hand solution Consider the following data: x⎯⎯x¯ = 20, sx = 2, y⎯⎯y¯ = −5, sy = 4, and b1 = 0.40. Which of the following is the sample regression equation? Multiple Choice yˆ = −13 − 0.40x yˆ= −13 + 0.40x yˆ = 3 − 0.40x yˆ = 3 + 0.40x
- A 95% confidence interval for the slope of a simple linear regression model is found to be (0.0046, 0.0252). This means that Select one: a. We are 95% confident the value of the predictor variable will increase by between 0.0046 and 0.0252 units. b. We are 95% confident that the average value of the response value lies between 0.0046 and 0.0252 c. We are 95% confident that the average value of response increases by between 0.0046 and 0.0252 units for every unit increase in the predictor. d. At 5% level of significance, there is no statistical evidence of a linear relationship between response and predictorQuestion 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.Identify the following variables as either discrete or continuous: (a) The number of times heads comes up in 100 tosses of a coin (b) The number of accidents occurring in a specified section of a freeway (c) The weight of boxes shipped from an Amazon warehouse (d) The concentration of carbon dioxide in the air of San Diego versus time (e) Optimistic, most likely, and pessimistic estimates of salvage value