Question 6Consider the two regression models specified below, where salary measures an employee's salary,priorexp measures the employee's years of prior experience, yrrank captures the years in thecurrent rank, and admin indicates having experience in an administrative position.Model 1: salary = 8₁ +8₂priorexp+eModel 2: salary = 3₁+ B₂priorexp+ Bayrrank + Badmin + eAnalyze the results of the two models below.Model 1:SourceModelResidualTotalsalarypriorexp_consModel 2:SourceModelResidualTotalsalarypriorexpyrrankadmin_consSS2.3573e+091.3938e+111.4174e+11SSdfCoef. Std. Err.4.9146e+109.2594e+10376.3852 107.637346709.43 602.74252.3573e+091723 192783542724dfCoef. Std. Err..451.8466 91.558761099.184 60.4110810342.55 2607.25438060.14 682.3417c. The F-statistic equalsMS195773163tMStNumber of obsF(1, 723)Prob> FR-squaredAdj R-squaredRoot MSEP>|t|Number of obs RF(3, 721)Prob > FR-squaredAdj R-squaredRoot MSEP>|t|=a. In the F-test, the number of restrictions J equals[95% Conf. Interval]725Consider an F-test on the joint power of yrrank and admin. Fill in the blanks below.[95% Conf. Interval]725b. In the F-test, the degrees of freedom nk for the unrestricted model equals. (Keep 1 decimal place).d. (T/F) yrrank and admin are jointly significant factors for salary.

We have to consider the model containing both the independent variables 'yrrank' and ' admin'. So,…

Answered: Consider the two regression models…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, bo + b₁x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. a Answer How to enter your answer (opens in new window) Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. 9 S e $ A A Hours Unsupervised Overall Grades V 96 5 t 8 b Oll 0 0.5 1 3.5 4 5 5.5 95 92 85 83 81 73 63 h → U İ 8 i k N 9 O alt I * Р ctri Tables Copy Data Keypad Keyboard Shortcuts Previous step answers…
A county real estate appraiser wants to develop a statistical model to predict the appraised value of 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, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y=74.80+ 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. 1000 For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…
During the 1950's and 1960's the average weight of vehicles sold in the U.S. was well over 4,000 pounds. There was a dip in the average weights in the 1970's and 1980's, due possibly to both higher demand for better gas mileage and a world-wide shortage of crude oil. Then in the 1990's and early 2000's the average weight of vehicles had a steady increase. A regression analysis was completed on the average weight of the 10 most commonly sold vehicles in the U.S. from the years 2012 through the year 2020 and yielded the following results, where the independent variable is the year and the predicted variable is the average weight of the 10 most popular vehicles. Correlation of "Average Weight" and "Year" = r = 0.9283 The regression equation is "Average Weight" = –124,960.73 + 63.82(Year) Predict the Average Weight to the nearest pound for the 2022 Year. Group of answer choices A. 4057 pounds B. 4015 pounds C. This value of Year is beyond the scope of the…
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 2 3 4 5 6 Overall Grades 94 86 79 71 62 Table Step 1 of 6 : Find the estimated slope, y intercept and correlation coefficient Round your answer to three decimal places. Answer
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Training Dept. of Nimrod Inc wants to develop a regression-based compensation model (compensation in $ per year, Comp) for its mid-level managers to encourage performance, loyalty, and continuing education based on three variables. ▪ Business unit-profitability (Profit per year in $). ▪ Working experiences in Nimrod Inc (Years). ▪ Whether or not a manager has a graduate degree (Grads). If a manager has a graduate degree equals 1, 0 otherwise. Table Attached Question: Which explanatory variables and interaction terms are significant and not significant at alpha = 5%? Explain your answer briefly.
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The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 2 3 5 6 Overall Grades 90 89 87 77 61 Table Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places.

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