See attached table and data chart: A. What firm-specific and industry-specific factors might be used to explain differences among giant corporations in the amount of revenue per employee and profit per employee? B. A multiple regression analysis based upon the data contained in the attached table reveals the following (t s
Multiplying an estimating equation by a correcting factor to correct heteroskedasticity may cause extra correlation to enter the model, which raises the R^2. This renders our ultimate regression results meaningless. True or false, or uncertain? Please provide an explanation.
The following equation was estimated for the fall and second semester students: (See attached) Here, trmgpa is term GPA, crsgpa is a weighted average of overall GPA in courses taken, cumgpa is GPA prior to current semester, tothrs is total credit hours prior to the semester, sat is SAT score, hsperc is graduating percentile
Answer this question (question 17) and questions 18 and 19 on the basis of the following regression results, standard errors in parentheses, n = 200) Qd = -500 - 100Pa + 50Pb + .3I + .2A (250) (50) (30) (.1) (.08) R2 = .12 Where Qd = 10,500 quantity demanded of pro
The following data is the quarterly sales of gasoline in the United States (in millions of barrels during part of the 1980s and the 1990s. Estimate the linear trend and use it to forecast sales for the first quarter of 1992. Note: There are two parts to this question. The first answer looks like S = a +b * T where A an B are con
1) What were the alternative methods used by the FTC and the merging firms to determine whether or not an Office Depot and a Staples outlet were in the same geographical market? 2) How did this differentiation in geographical market measurement affect the pricing behavior estimated by the FTC and by the merging firms? What i
Please help interpret the regression results. I regressed LogCost on LogSeatMiles, LogPriceLabor, LogPriceMaterials Log PriceFuel Here are the results: Variable Coefficient Log SeatMiles .8958442 Log PriceLabor .287488 Log PriceMaterials -.0987056 Lo
I need some help interpreting regression results. How do you interpret the effect of immigrant status on wages (when the model is Log wages regressed on immigrant dummy, and an immigrant dummy interaction) Here are the important numbers Immigrant -.095411Coefficient .0134239 SE Immigrant Interaction .0851195 COEF
I have a few different regression results, and need some help interpreting them. On a few, I put some of my answers to the questions in brackets - I'd like to know if I am correct, if not, some assistance would be appreciated. The results are as follows A. PSoda Hat = .956 + .1149882 PrBlack + 1.60 income Where: P
1. Portray the following data on a two variable diagram Academic Year Total Enrollment Enrollment in ECon 1994-95 3000 300 95-96 3100 325 96-97 3200 350 97-98 3300 375 98-99
Using the Durbin-Watson test for first-order serial correlation. Determining whether the results are significant. Detecting impure serial correlation.
How is R-squared calculated, and what information does this give you?
(See attached file for full problem description with proper equations and charts) --- 1) Consider the following model: Yi = Bo + B1Xi +B2D2i + B3D3i + ui Where Y = annual earnings of MBA graduates X = years of service D2 = 1 if Harvard MBA = 0 if otherwise D3 = 1 if Wharton MBA = 0 if otherwise
6. One of the series included among the lagging indicators is A. the change in sensitive material prices B. the index of industrial production C. employees on non-agricultural payrolls D. average duration of unemployment 7. An explanatory forecasting technique in which the analyst mus
1. Which of the following is a test of the statistical signficiance of the entire regression equation? A. t-test B. R2 C. F-test D. Durbin-Watson 2. When the R2 of a regression equation is very high, it indicates that A. all the coefficients are statistically signficant. B.
The following question refers to this regression equation. QD= 15,000 - 10 P + 1500 A+ 4 PX + 2 I Q = Quantity demanded P= Price = 7,000 A = Advertising expense,in thousands = 54 PX = price of competitor's product = 8,000 I = average monthly income = 4,000 Calculate the elasticity for each variable (own price el
4. The following is the regression output for the following equation, which was estimated for a large sample of people: log(wage) = α + β1Schooling + β2Age + β3Female + β4Non-white Variable Coefficient estimate (β-hat) Standard error Schooling (years) .134 .024 Age (years) 0
1. Associated with the name of Simon Kuznets is the idea that the relationship between GDP and inequality is nonlinear. Kuznets hypothesized three phases in economic development. In the first stage of development, incomes and inequality are both low. During the process of modernization and industrialization, income and inequ
2. You are worried about multicollinearity in your regression model. In particular, you are worried that X2 and X3 are collinear. You compute the correlation coefficient: r(X2,X3) = - 0.82. Which of the following statements do you think are true? (Circle more than one answer if you think more than one statement is true. C
According to compensating differential theory: (a) Should a job with health insurance pay more than a job without (holding all else constant)? (b) If population data were analyzed, it would likely show that wages are higher (on average) in the jobs that the theory of compensating differentials would predict to be lower. Why
3. As a natural experiment to determine the effect of education on earnings, a researcher compares the schooling and educational attainment of two groups of people. The first consists of those that lived in a state that devoted a high percentage of its budget to postsecondary education. The second consists of those that lived in
The application of the least-squares procedure to a multiple linear regression equation requires that: a no exact linear relationships can exist among any of the independent variables b the number of observations (n) must exceed the number of b parameters to be estimated (m) c the number of observations (n) mus
Lenny's, a national restaurant chain, conducted a study of the factors affecting demand (sales). The following variables were defined and measured for a random sample of 30 of its restaurants: (NOTE: This question and the 3 that follow it, may require the use of statistical tables.) ---Y = Annual restautant sales ($000) --
The presence of autocorrelation leads to all of the following undesirable consequences in the regression results except: a the least-squares estimates of the regression coefficients will be biased b the t-statistics may yield incorrect conclusions concerning the significance of the individual independent variables
Q = Which regression method is most frequently used for short run cost estimates? Q = What are the problems you might encounter? How can you overcome these problems?
1. If a company faces a price-elastic demand curve, it can increase the revenue by decreasing the price. True False 2. F-test measures the statistical significance of each explanatory variable. True False 3. A lawyer whose annual income used to be $150,000 quit the job and opened a res
2. Before dinner, you run an OLS regression with the data below and commit the estimated beta values to memory. X Y 2 6 4 17 5 16 8 20 2 8 While watching television after dinner, you suffer memory loss. You can't remember what show you just watched or what you ate for dinner. What's worse, you can no long
Need help understanding (need to see) how these problems are worked. (See attached file for full problem description with equations and data table) --- s 1. Suppose the supply function for product X is given by Q x = -50 + 0.5 Px - 5Pz. a. How much of product X is produced when Px = $500 and Pz = $30? b
(See attached file for full problem description) --- 1. In a regression analysis, the variable that is being predicted must have the same units as the variable doing the predicting is the independent variable usually is denoted by X is the dependent variable None of the a
Multicollinearity refers to the existence of correlation among the independent variables in a multiple regression model. Discuss how multicollinearity can impact your regression analysis. How do you indentify it? What do you do in response to identifying a multicollinearity problem?