Suppose that a researcher, using data on class size (CS) and average testscores from 100 third-grade classes, estimates the OLS regression:TestScore = 520.4 – 5.82 × CS, R = 0.08, SER = 11.5.a. A classroom has 22 students. What is the regression's prediction forthat classroom's average test score?b. Last year a classroom had 19 students, and this year it has 23 students.What is the regression's prediction for the change in the classroomaverage test score?c. The sample average class size across the 100 classrooms is 21.4. Whatis the sample average of the test scores across the 100 classrooms?(Hint: Review the formulas for the OLS estimators.)d. What is the sample standard deviation of test scores across the 100classrooms? (Hint: Review the formulas for the R2 and SER.)

Answered: Suppose that a researcher, using data…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. 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. Age 39 43 46 61 64 Bone Density 352 346 321 314 312 Table Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places
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, 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 1 1.5 2.5 4 5.5 6 Overall Grades 98 86 85 83 80 78 67 Table Step 1 of 6: Find the estimated slope, y intercept, correlation cofficient Round your answers to three decimal places.
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspace
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. 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. Age 50 59 60 64 68 Bone Density 331 326 325 320 315 Table Step 3 of 6 : Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆ.
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, 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 1 2 3 4 4.5 5 5.5 Overall Grades 98 95 93 90 89 72 69 Table Copy Data Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places.
Do movies of different types have different rates of return on their budgets? Here's a regression of USGross (SM) on Budget for comedies and action movies with an indicator variable. Complete parts (a) through (d). Dependent variable is: USGross ($M) Coefficient SE(Coeff) - 6.78278 16.95 1.00523 Variable Constant Budget ($M) Comedy 24.0373 0.1613 11.73 t-ratio P-value -0.400 0.6907 6.23 <0.0001 2.05 0.0451 a) Write out the regression model. USGross = + ( Budget + (Comedy R-squared = 32.8% R-squared (adjusted) = 31.0% s = 47.51 55 degrees of freedom
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, 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 1 2 3 4 4.5 5 5.5 Overall Grades 98 95 93 90 89 72 69 Table Copy Data Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places.
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. a. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. b. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234 Therefore, about _________% of the variation in monthly sales is explained by the years of sales experience. (Type an integer or decimal rounded to one decimal place as needed.)
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $6 million. Is the result close to the actual number of viewers, 8.9 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. 98 3.5 3 7 13 12 13 10 2 6.8 6.3 10.2 8.5 4.4 1.8 2.7 What is the regression equation? y=+x (Round to three decimal places as needed.) What is the best predicted number of viewers for a television star with a salary of $6 million? The best predicted number of viewers for a television star with a salary of $6 million is million. (Round to one decimal place as needed.) Is the result close to the actual number of viewers, 8.9 million? O A. The result is very close to the actual number of viewers of 8.9 million. O B. 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 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.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. 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. Age 50 59 60 64 68 Bone Density 331 326 325 320 315 Table Step 1 of 6 : Find the estimated slope, y instercept and correlation coefficient. Round your answers to three decimal places.

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