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( TRUE OR FALSE help me find the true or false questions )
1. In economic statistics and Econometrics, we do the same thing.( )
2. As same in regression analysis, variables in relation analysis are all random variables.( )
3. Known as residual, "i is an estimate of u , the random disturbance term.( )
4. The slope coefficient of the log-log model measures the elasticity of Y with respect to X.( )
5. In regression of standardized variables, the intercept term is always zero.( )
6. The underlying theory may suggest a particular functional form.( )
7. The disturbance term u is assumed to follow
8. White test is used to check if there exists multicollinearity in the disturbance term of a regression function.( )
9. Dummy variable can be used to test the stability of a regression model just as the function of the Chow Test.( )
10. Where there is autocorrelation in the u , the OLS estimators are not BLUE estimators any more.( )
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- 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 multicollinearity8. Which of the following best describes the linear probability model? The model is the application of the linear multiple regression model to a binary dependent variable The model is an example of probit estimation The model is another form of logit estimation The model is the application of the multiple regression model with a binary variable as at least one of the regressors OO1. An analyst from your firm used a linear demand specification to estimate the demand for its product and sent you a hard copy of the results: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Price of X Income 0.38 0.14 0.13 20.77 150 df 2 147 149 SS 58.87 -1.64 1.11 10398.87 63408.62 73807.49 Coefficients Standard Error 15.33 0.85 0.24 MS 5199.43 431.35 t Stat 3.84 -1.93 4.63 F 12.05 P-value 0.00 0.06 0.00 Significance F 0 Lower 95% 28.59 -3.31 0.63 Upper 95% b. Which regression coefficients are statistically significant at the 5 percent level? a. Based on these estimates, write an equation that summarizes the demand for the firm's product. 89.15 0.04 1.56 C. When price is $10, what is the income elasticity for this product for an income level of 35?
- Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?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.1. R-squaredSuppose regression of y on an intercept and x with 50 observations yields total sum of squares 100 andexplained sum of squares 36.(a) What is ?^2?(b) What is the correlation coefficient between y and x?(c) What is the standard error of the residual?
- 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…14. The regression R² is a measure of: a. whether or not X causes Y. b. the goodness of fit of your regression line. c. whether or not ESS> TSS. d. the square root of the correlation coefficient. 15. The OLS residuals, û, are defined as follows: a. Ŷ-Bo-B₁X; R b. Y – Bo – PıX c. (Y-Y)² d. Y₁ - Ŷ, 16. There exist a relationship test scores and the student-teacher ratio can be modeled as a linear function with an intercept of 798.9 and a slope of -3.28. A increase in the student- teacher ratio by 2 will: a. Reduces test scores by 3.28 on average b. Results in a test score of 798.9 c. Reduces test scores by 6.56 on average d. Reduces test scores by 6.56 for every school district1. You are running a probability regression. If your data contains many outliers, you’d be better off using (A) A linear probability model. (B) Probit. (C) Logit. (D) Either probit or logit would work fine. (E) Any of the above regression models would work fine.
- Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. A. True B. False 10. 11. What is/are the objective(s) of regression analysis? A. To estimate the expected value of the dependent variable only B. To test hypotheses about the nature of the dependence between the variables only C. To predit the expected value of the dependent variable, given the value(s) of the dependent variables beyond the sample range only D. All of the above What is the population regression line or PRL? A. A line derived from the sample collected in a population B. A line that passes through the conditional means of the independent variable (X) C. Gives the average value of the dependent variable corresponding to each value of the independent variable D. Gives the average value of the independent variable corresponding to each value of the dependent variable 12.potter makes and sells ceramic bowls. It is observed that when the price is $32, only 9 bowls are sold in a week; but when the price decreases to $10, weekly sales rise to 20. Assuming that demand can be modelled by a linear function, (a) obtain a formula for Pin terms of Q (b) Calculate for the slope of the curve if the relationship between p and Q is linear. (c) comment on the likely reliability of the modelGiven the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…