What is the significance of R and R2 in gression model?
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Q: Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have…
A: Given ; Suppose that Y is normal and we have three explanatory unknowns which are also normal.
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A: Note: Hi there! Thank you for posting the question. As your question has more than 3 parts, we have…
Q: Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have…
A: SOLUTION:- Given SSR=85000 SSE=15000
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Q: )Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have…
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Q: Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have…
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What is the significance of R and R2 in gression model?
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- In terms of the model parameters, state the null hypothesis that, after controlling for sales and roe, ros has no effect on CEO salary. State the alternative that better stock market performance increases a CEO’s salary.In which situation cointegration test can be performed? Write down its null Also, refer the given table and interpret the results?Find the least square regression line for set of points {(1,3), (2,4), (3,4), (4,6)}
- Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain5 How does the dynamic CCE estimator solve the estimation problems of the PMG technique?The subsets of {1,2} are Φ, {1}, {2} and {1,2}. So there are four possible potential models when deciding on a regression with a choice of inputs from a dataset containing two potential input variables. Your friend tells you this is nonsense and there are three possible models because the empty set Φ is just an imaginary concept concocted by some mathematician. What should you say to your friend? You should explain that the empty set corresponds to the model y = β + ε, where β is some constant and ε is a residual term. In this model, the output predictions are always equal to the output's sample average. You should explain that the empty set corresponds to the model y = ε, where ε is a residual term. In this model, the output predictions are totally random. You should indeed agree. Your friend is spot on and one cannot have a regression with no input variables. You should partially agree. Your friend is spot on that there are three models and not…
- Consider the regression model y = a + ßx+u, where x is an endogenous variable. a) Write x₁ = x + ê; and show that = x. b) Suppose there are k valid instruments: Z1, Z2, ..., Zk. Let x₁= πo+f1zil + π2Zi2+...+îkzik. Show that the 2SLS estimator is equivalent to an IV estimator using as an instrument: Σ₁₁(&i- Ñ)(vi- V) _ Σ₁(Ri—Ñ)(Yi-Y) Ei=1(xi-x)² 21(®i- Đ)(xi-x) = [Hint: OLS first order conditions imply №₁ ê¡Â¡ = 0 and Σ¼_₁ ê¡= 0.] c) Suppose there is only one instrument z and ✰; = fo+ fizi. Show that the 2SLS estimator is numerically identical to the IV estimator: = Σ₁ (Îi—Ñ)(vi-V) – Σ₁(²₁-7)(vi-V) Σ=1(ât− x)2 Σ₁ (Z₁-7)(xi-x)* n [Hint: Use the result in (b) and plug in â; and ☎ = ñîo+â¦Z.])A county real estate appraiser wants to develop a statistical model to predict the appraised value of 3) houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model: E(u) = Bo + Bix, where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 73 houses in Fast Meadow, the following results were obtained: y = 73.80 + 19.72x What are the properties of the least squares line, y = 73.80 + 19.72x? A) Average error of prediction is 0, and SSE is minimum. B) It will always be a statistically useful predictor of y. C) It is normal, mean 0, constant variance, and independent. D) All 73 of the sample y-values fall on the line.What is the meaning of the value of parameter bb in this situation?
- An engineer is testing a new car model to determine how its fuel efficiency, measured in L/(100 km), is related to its speed, which is measured in km/hour. The engineer calculates the average speed for 30 trials. The average speed is an example of a (statistic or parameter) The engineer would like to find the least squares regression line predicting fuel used (y) from speed (x) for the 30 cars he observed. He collected the data below. Speed 62 65 80 82 85 87 90 96 98 100 Fuel 12 13 14 13 14 14 15 15 16 15 Speed 100 102 104 107 112 114 114 117 121 122 Fuel 16 17 16 17 18 17 18 17 18 19 Speed 124 127 127 130 132 137 138 142 144 150 Fuel 18 19 20 19 21 23 22 23 24 26 The regression line equation is Round each number to four decimal places.Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 21 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.9, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 90000 and the sum of squared errors (SSE) is 10000. From this information, what is the number of degrees of freedom for the t-distribution used to compute critical values for hypothesis tests and confidence intervals for the individual model…
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