Consider the fitted values from a simple linear regression model with intercept: yˆ = 7 + 4x. Assume that the total number of observations is n = 20. In addition, the explained sum of squares is SSE = 10 and the residual sum of squares is SSR = 30. Under the classical Gauss-Markov assumptions, a) What is the value of the R2 ?

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 certain

Show calculations or explanation for each question. a)  Which of the following techniques is used to predict the value of one variable on thebasis of other variables?a. Correlation analysisb. Coefficient of correlationc. Covarianced. Regression analysis b) In the least squares regression line, y^=3-2x the predicted value of y equals:a. 1.0 when x = −1.0b. 2.0 when x = 1.0c. 2.0 when x = −1.0d. 1.0 when x = 1.0 c) In the simple linear regression model, the y-intercept represents the:a. change in y per unit change in x.b. change in x per unit change in y.c. value of y when x = 0.d. value of x when y = 0.

A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The least squares equation was found Y; = -13258.594 + 2.422X;, where X; is the program per-year tuition and Y; is the predicted mean starting salary. To perform a residual analysis for these data, the following results are obtained. of regression have been seriously violated. Residual index plot QQ Plot of Residuals Residuals Residuals 20000- 20000 0. -20000 -20000 a) To evaluate whether the assumption of linearity has been violated, which of the following graph shou be examined? A. Predicted Values vs. Residuals B. Residual index plot C. QQ plot of residuals D. Residuals vs. Progrm Per-Year Tuition ($) b) To evaluate whether the assumption of normality has been violated, which of the following graph…

)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.

This data table contians the listed prices and weights of the diamonds in 48 rings offered for sale in The Singapore Times. The prices are in Singapore dollars, with the weights in crats. Estimate the linear regression using weight as the explanatory variable and price as the response variable using the following two methods: a) Create a scatterplot with trendline (be sure to show the equation and R-square on the chart.) b) Use the Data Analysis Toolpak to do the work for you.

A year-long fitness center study sought to determine if there is a relationship between the amount of muscle mass gained y(kilograms) and the weekly time spent working out under the guidance of a trainer x(minutes). The resulting least-squares regression line for the study is y=2.04 + 0.12x A) predictions using this equation will be fairly good since about 95% of the variation in muscle mass can be explained by the linear relationship with time spent working out. B)Predictions using this equation will be faily good since about 90.25% of the variation in muscle mass can be explained by the linear relationship with time spent working out C)Predictions using this equation will be fairly poor since only about 95% of the variation in muscle mass can be explained by the linear relationship with time spent working out D) Predictions using this equation will be fairly poor since only about 90.25% of the variation in muscle mass can be explained by the linear relationship with time spent…

We are researching the charity donation behaviour of Australians. We have the following model: (E1) Donate = β0 + β1Income +β2Avg_Gift + β3Edu + u Where: Donate is a dummy variable equal to 1 if the individual makes a donation in response to a social media campaign by our charity organisation, and 0 otherwise Income is the annual household income Avg_Gift is the average value of past donations made by the individual to our charity Educ is the individual's level of education (in years) Our data is a random sample of the population and we have 1,614 observations. You should assume E [u | Income, Avg_Gift, Educ] = 0. Using the information above, please answer the following 3 questions. [i] Referring to Model (E1) above, interpret the coefficient β1.  [ii] You know that this model will suffer from heteroskedasticity. Why is this the case? Explain your reasoning.  [iii] In your own words, what is heteroskedasticity?