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- determine the regression line equation plot the line on a graph and summarize the results( reject or do not) is there enough evidence?When running a ols regression, if one of my 3 control variables are insignificant via T-test should I keep them in the regression/how should I interpret them?We have estimated the impact of gross domestic product (GDP), energy consumption (ENERGY) and population (POP) on CO2 emiisions (CO2) in Cyprus. The results are as follows, Dependent Variable: CO2 Method: Least Squares Date: 04/20/17 Time: 09.46 Sample: 1990 2013 Included observations: 24 Variable Coefficient Std. Error t-Statistic Prob. GDP ENERGY POP 2.002813 0.022114 -0.734352 0.203927 6.458672 0.011872 0.328388 0.293686 0,310097 1.862670 -2.236233 0.694371 0.7597 0.0773 0,0369 0.4954 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.825079 Mean dependentyar 0.798841 0.048515 Akaike info criterion 0.047074 Schwarz criterion 40.75460 Hannan-Quinn.criter. 31.44583 Durbin-Wats on stat 0.000000 3.625982 0.108170 -3.062883 -2.866541 -3.010793 1.410912 S.D. dependent yar a Write down the economie function for the above estimation by using the information obtained from above table| b- Write down the economic model for the above…
- ANOVA Sigmficance F 0,046 df SS MS F 130433116.219 130433116.219 4.083 Regression Residual Total 113 3609911959.86s 31946123.539 114 3740345076.087 Cosfficrent Standard Error Stát Pvalne 1535.215 Intercept Age 10725.802 6.987 0,000 69.964 34.625 2.021 0.046 Which of the following statements is the best explanation of the R? Select one O'A3.5% of the accident damage can be explained by the age of the driver. B. 3.5% of the variation in accidernt damage can be eaplained by variation in the age of the drver. CC3.5% of the coefficients r stat and p value can be explained by the age of the dtver. D.3.5% of the total errar can be eiplained by the SSE Scanned with CamScannerA researcher fitted following OLS regression using time series data from 1973 t0 2020 (Bar)BD =-3.7 + 0.08BD lag(t-1), -2.2LnER lag(t), + 42LnEXP lag(t)-33LnRE lag(t), +10 LnPl lag(t), R²=0.99 DW=1.4 RSS=4.5 Where BD is budget deficit as a percentage of GDP, ER, EXP, RE and Pl are Exchange rate, government expenditures, government revenues and per capita income, respectively. Ln shows natural log and "t" stands for time. i :-Interpret above results ii :- Is there any problem of Autocorrelation in above model? How do you knowWhen running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?
- 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.wages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]Imagine that you were told that the t-statistic for the slope coefficient of the regression line Test Score = 698.9-2.28 x STR was 4.38. What are the units of measurement for the t-statistic? OA. Number of students per teacher. O B. Points of the test score. O C. Standard deviations. OD. Test Score STR
- a simple linear regression equation shows the relationship between-Explain what is meant by an error term. What assumptions do we makeabout an error term when estimating an ordinary least squares regression?Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) 5.59 5.55 College (X,) 5.57 Female (X2) -2.69 -2.67 -2.67 0.30 0.30 Age (X3) 0.70 Northeast (X4) 0.61 Midwest (Xs) -0.28 South (X) Intercept 12.94 4.49 3.83 Summary Statistics SER 6.40 6.34 6.33 0.180 0.194 0.198 0.193 ok97 0.180 4100 4100 4100 Using the regression results in column (2): On average, a worker eans $ per hour for each year that he or she ages.