Analysis of Variance Source DF SS MS Regression 1 Residual Error 13 0.2364 Total 14 11.3240 What is the value of SSR (Sums of Squares for Regression)?
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- 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)]Regression Statistics Multiple R 0.971 R-Square A Adjusted R-Square .942 Standard Error 30.462 Observations 51 ANOVA df SS MS F Significance F Regression C 747851.57 373925.79 402.98 9.89E-31 Residual 48 D 927.91 Total 50 792391.11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept E 62.13 26.79 1.60E-30 1539.66 1789.51 Price of Roses −6.68 F −1.41 1.64E-01 −16.16 2.81 Disposable Income (M) 9.73 0.34 G 1.23E-31 9.04 10.42…When running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?
- Explain Distribution of Regression Statistics with Normal Errors?Explain what is meant by an error term. What assumptions do we makeabout an error term when estimating an ordinary least squares regression?Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
- 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 CamScannerdoes the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and collects monthly data for 25 firms. He estimates the modet: Sales- Bo + B1 Advertising +t. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value Intercept 40.10 14.08 2.848 0.0052 Advertising 2.88 1.52 -1.895 0.0608 Which of the following are the competing hypotheses used to test whether the slope coefficient differs from 3? Multiple Choice Ho i bị 3; HAtbi3 Họ ib - 2.88; HAibi 2.88
- Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)Test Design: Suppose I want to test the impact of soccer coaches on soccer teams. How would you test this? Include a few (3 or 4) independent variables to explain the dependent variable. Describe the data and write the regression equation.A 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 know