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Regression

Economics

(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

Economics and Management

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

Economics and Management

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.

Regression Equation and Elasticity

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

Labor economics

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

2 questions

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

Multicollinearity in regression model

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

Labor economics

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

determine the effect of education on earnings

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 steps are shown.

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

Multiple Choice Economics

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

Regression

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?

ECONOMICS AND MANAGEMENT

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

Regression (OLS) and beta values

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

Managerial Economics

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

Economics and Management

(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

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?

Laura wanted to build a multiple regression model based on advertising expenditures and coffee times price index. Based on the selection of all normal values she obtained the following:

Laura wanted to build a multiple regression model based on advertising expenditures and coffee times price index. Based on the selection of all normal values she obtained the following: 1) Multiple R = 0.738 2) R-square = 0.546 By using lagged values she came up with the following: 3) Multiple R = 0.755 4) R-square = 0.57

Estimation of Demand

In a study of the demand for life insurance, Executive Insurers, Inc. is examining the factors that affect the amount of life insurance held by executives. The following data on the amount of insurance and annual incomes of a random sample of 12 executives were collected. Observation Amount of Life Insurance Annual Income

Cellon case help

Cellon, a manufacturer of a home insulation, wants to develop guidelines for builders and consumers regarding the effects (1) of the thickness of the insulation in the attic of a home and (2) of the outdoor temperature on natural gas consumption. In the laboratory they varied the insulation thickness and temperature. Please

Omitted bias + f-stat

The following regression model represents the demand for peanut butter: log qt = B1 + B2 logpt + B3log rt + B4logmt + ut where qt is the quantity of peanut butter consumed at time t; pt is the price of peanut butter; rt is the price of jelly; and mt is income per capita. Suppose an analyst estimates the following model:

Hypothesis testing

1) One member of the management board claims that for every $1000 increase in income, the amount of life insurance held will go up by $5000. Choose an alternative hypothesis and explain your choice. Does your estimated relationship support this claim? Use a 5 percent significance level. 2) Test the hypothesis that as income

Regression Analysis

A life insurance company wishes to examine the relationship between the amount of life insurance held by a family and family income. From a random sample of 20 households, the company collected the data in the file insur.xls. The data are in thousands of dollars. (a) Estimate a linear relationship between life insurance (Y) a

Engineering Economics: Regression, Learning Curve

7. Identify the formula for the straight line that describes the relationship between the two variables X and Y from the data below. X 4 5 6 7 8 Y 21 24 25 28 8. You have the following historical costs for an item for the last five years. You believe there is a trend that will continue into

9950-econ

Please assist me with the attached. There are two documents posted. The .xls has the data and the regression - see tabs at bottom left. The .doc has the actual problem. The question and background is in black, my answers so far are in blue and the questions I need answered are highlighted in yellow ? they are 6,7,8,9. Dema

Using OLS Estimation for prediction

We have 3 variables X Y Z X = F (Y, Z) Data is 2000-2005. I use OLS to Estimate the model and get a standard result.. for arguments sake I'll say it is X=3.00 -5.00Y +250Z I want to be able to predict the value of X with what I expect the values of both Y and Z to be in the year 2006. I expect Y to be 50 in 2