Could someone answer this for me please You estimate a simple linear regression model using a sample of 25 observations and obtain the following results (estimated standard errors in parentheses below coefficient estimates): y = 97.25 + 19.74* x (3.86) (3.42) What are the endpoints of the interval estimator for B2 with a 99% interval estimate?
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- In regression model: Yi = B1+ B2Xi+ Ei, where Ei is a random variable independent of Xi, with mean and variance constant and different from zero. Ei ~(media, varianza) Obtain the OLS estimator of ẞ2. Show that the properties of finite samples hold.The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2). The estimated regression equation was ŷ = 83.7 + 2.23x1 + 1.60x2. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.445. (a)Compute and interpret R2 and Ra2.(Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??) . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??).The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGEi=β0+β1EDUCi+εi where WAGEi is the hourly wage of person i (i.e., any specific person) and EDUCiEDUCi is the number of years of education for that same person. The residual εiεi encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAGEi=−12.3+4.4 EDUCi If the standard error of the estimate of β1 is 1.29, then the true value of β1 lies between (2.465, 3.11, 3.755, 1.82) and (5.69, 6.98, 5.045) . As the number of observations in a data set grows, you would expect this range to (INCREASE OR DECREASE) in size.
- Past class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.Suppose you have the following data: X Y 3 9 7 45 5 25 2 8 Using OLS methods: estimate the following regression model: Sq root of Yi = Beta0 + Beta1(Xi) + error(10+05)The following data were collected on the height (inches) and weight (pounds) of women swimmers.Height6870646566 Weight132110106115128 a. Develop the estimated regression equation by computing the values of b0 and b1.
- A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________No written by hand solution Consider the following data: x⎯⎯x¯ = 20, sx = 2, y⎯⎯y¯ = −5, sy = 4, and b1 = 0.40. Which of the following is the sample regression equation? Multiple Choice yˆ = −13 − 0.40x yˆ= −13 + 0.40x yˆ = 3 − 0.40x yˆ = 3 + 0.40xThis exercise refers to the drunk driving panel data regression, summarizedin Regression analysis of Drunk Driving (see attachment)a. New Jersey has a population of 8.85 million people. Suppose that NewJersey increased the tax on a case of beer by $2 (in 1988 dollars). Use theresults in column (5) to predict the number of lives that would be savedover the next year. Construct a 99% confidence interval for your answer. b. The drinking age in New Jersey is 21. Suppose that New Jersey loweredits drinking age to 19. Use the results in column (5) to predict the changein the number of traffic fatalities in the next year. Construct a 95% confidence interval for your answer.c. Suppose that real income per capita in New Jersey increases by 3% inthe next year. Use the results in column (6) to predict the change in thenumber of traffic fatalities in the next year. Construct a 95% confidenceinterval for your answer.d. How should standard errors be clustered in the regressions in columns(2) through…
- When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.Given the following regression output, Predictor Coefficient SE Coefficient t p-value Constant 84.998 1.863 45.62 0.000 x1 2.391 1.200 1.99 0.051 x2 -0.409 0.172 -2.38 0.021 Analysis of Variance Source DF SS MS F p-value Regression 2 77.907 38.954 4.138 0.021 Residual Error 62 583.693 9.414 Total 64 661.600 answer the following questions: d-1. State the decision rule for 0.05 significance level: H0: β1 = β2 = 0; H1: Not all β's are 0. (Round your answer to 2 decimal places.) d-2. Compute the value of the F statistic. (Round your answer to 2 decimal places.) d-3. What is the conclusion? Use the 0.05 significance level.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.