Based on the following data, estimate the slope of the equation using OLS (3 significant digits in final answer): K=ẞ1+ B2J+E Observation 1 2 3 2 4 9 K 16 15 14
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- 25) Consider the estimated equation from your textbook = 698.9 - 2.28STR, R2 = 0.051, SER = 18.6, Corresponding standard errors of coefficients are as such: SE (698.9) = 10.4 SE (-2.28) = 0.52 The absolute value of t-statistic for the slope is approximately: A. 67.20 B. 1.76 C. -4.38 D. 0.52Provide a trend analysis for income taxes ratios. What does it mean when a company's income taxes ratios increase over the years?In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..
- A regression of average weekly earnings (AWE, measured in dollars) on age(measured in years) using a random sample of college-educated full-timeworkers aged 25–65 yields the following: AWE = 696.7 + 9.6 X Age, R2 = 0.023, SER = 624.1.a. Explain what the coefficient values 696.7 and 9.6 mean.b. The standard error of the regression (SER) is 624.1. What are the unitsof measurement for the SER? (Dollars? Years? Or is SER unit-free?)c. The regression R2 is 0.023. What are the units of measurement for theR2? (Dollars? Years? Or is R2 unit-free?)d. What does the regression predict will be the earnings for a 25-year-oldworker? For a 45-year-old worker?e. Will the regression give reliable predictions for a 99-year-old worker?Why or why not?f. Given what you know about the distribution of earnings, do youthink it is plausible that the distribution of errors in the regressionis normal? (Hint: Do you think that the distribution is symmetric orskewed? What is the smallest value of earnings,…DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 10. Write the resulting regression equation. Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.The following data gives the experience of the machine operators and their performance ratings as given by the number of good parts turned out per 100 pieces.Experience(X) 16 12 18 4 3 10 5 12Performance Ratings (Y) 88 87 89 68 78 80 75 83Obtain the regression line of performance ratings on experience and estimate the probable performance if the operator has 7 years of experience.
- Could I have help deriving these equationsIn exercise 1, the following estimated regression equation based on 10 observations was presented. y^=29.1270+.5906x1+.4980x2Here SST=6724.125, SSR=6216.375, sb1=.0813, and sb2=.0567. a) Compute MSR and MSE. b) Compute F and perform the appropriate F test. Use α=.05. c) Perform a t test for the significance of β1. Use α=.05. d) Perform a t test for the significance of β2. Use α=.05.Econometrics: Given the following regressions: yt = 1.2yt-1 + Et and xt = 1.3xt-1 - 0.4xt-2 + Ct 1) Find he homogeneous equation and assess the implications of the roots.
- Create log(u^2 ) by first squaring the OLS residuals and then taking the natural log.DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 6. If tax revenue per capita (M) increases by 10, what will happen to the estimated quantity of cement demanded? Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.a simple linear regression equation shows the relationship between-