to test a GDP series for a unit root
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(c) Suppose that you need to test a GDP series for a unit root. Explain how you will decide the appropriate form of the test to apply, including the formulation of the test regression.
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- For linear regression, the standard error of estimate can be computed as the square root of O SS residual/(n-1). O SS residual/(n − 2). O SS regression/(n-1). SS Pregression/(n - 2).Say you have a regression where you are regressing wages on several variables, one being Years of Education (X1). In this regression, you estimate βˆx1 = 1.5 and it is statistically significant. Suppose you then find there is information of individuals SAT scores (an approximate measure of underlying ability) and add it to your regression as X2. You now get that βˆx1 = 0.5 and is still statistically significant, and you get βˆx2 = 0.1 and it is also statistically significant. What type of relationship to you suspect there is between Years of Education, SAT scored, and wages? Touch on the correlation of each variable to every other variable and explain the likely causal pathways. If cor(x1, y) ≡ 0.5, and cor(x2, y) ≡ 0.5, explain when regressing y on x1 and x2 gives an R2 of 0.5In regression analysis, the error term is very important. By stating the assumptions about the error term, discuss three reasons for the inclusion of the error term in a regression model
- Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the date and the least-squares regression line. The equation for this line is y=82.84-0.49x. Birthrate, x (number of births per 1000 people) 24.6 50.1 40,6 20.0 50.7 28.1 143 49.0 31.9 14.8 33.6 46.5 Send data to calculator Send data to Excel Female life expectancy, y (in years) 74.3 54.4 66.2 73.6 58.9 72.7 76.1 61.5 62.4 71.4 68.0 57:5 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) Birthrate (number of births per 1000 people)How do I construct a time series plot and determine pattern of data? What formula do I use for regression analysis to find the parameters for the line that minimizes MSE for this time series?What is the symbol for the residual?
- A car dealer wants to estimate the price of a used car based on the age of the car and the mileage. Based on a sample of 20 cars, she determines the sample regression equation that predicts price taxes on the basis of the age (in years) of the number of miles is Price=21,619-1022Age-0.03 Miles (a) If the age of the car was fixed and the mileage was increased by 10,000, how much would the price increase or decrease and by how much? (b) Predict the selling price of a five-year-old car with 65,000 miles. (Round your answers to the nearest whole number.)Based on the results of the regression, what would be the mose likely conclusion?what is the estimated regression equation.?
- What is the regression equationSuppose that a researcher, using data on class size (CS) and average test scores from 102 third-grade classes, estimates the OLS regression TestScore=515.196+ (-5.7618)x CS, R² = 0.06, SER=11.4. A classroom has 23 students. The regression's prediction for that classroom's average test score is (Round your response to two decimal places.) Last year a classroom had 20 students, and this year it has 24 students. The regression's prediction for the change in the classroom average test score is (Round your response to two decimal places.) The sample average class size across the 102 classrooms is 21.19. The sample average of the test scores across the 102 classrooms is (Hint: Review the formulas for the OLS estimators.) (Round your response to two decimal places.)Figure shows three residual plots and a normal probability plot of residuals. For each part, decide whether the graph suggests violation of one or more of the assumptions for regression inferences. Explain your answers.
