A sales manager collected the following data on annual sales for new customer accounts and the number of years of experience for a sample of 10 salespersons.Years ofAnnual SalesSalespersonExperience($1000s)118076382483897931096812113141211016119The data on y= annual sales ($1000s) for new customer accounts and 2 = number of years of experience for a sample of 10 salespersons provided the estimatedregression equation ŷ = 68.53+3.31r. For these data 7 = 8.3, (c.I = 206.10, and s = 5.2107.%3Da. Develop the 95% confidence interval for the mean annual sales ($1000s) for all salespersons with eleven years of experience.(2$) (to 2 decimals)b. The company is considering hiring Tom Smart, a salesperson with eleven years of experience. Develop a 95% prediction interval of annual sales ($1000s) for TomyegrsSmart.($24) (to 2 decimals)c. Discuss the differences in your answers to parts (a) and (b).As expected, the prediction interval is much (narrowpredict annual sales for one new salesperson with 11 years of experience than it is to estimate the mean annual sales for all salespersons with 11 years of experience.than the confidence interval. This is due to the fact that it is more easyv to5.

The predicted value is y = 68.53 + 3.31 x 11y = 104.94t-critical value at 0.05 and df = 8 is 1.8596…

The data on y = annual sales ($1000s) for new customer accounts and 2 = number of years of ex regression equation y = 68.53 + 3.31z. For these data 7 = 8.3, (. 7) = 206.10, and s = %3! a. Develop the 95% confidence interval for the mean annual sales ($1000s) for all salespersons wit ) (to 2 decimals) D. The company is considering hiring Tom Smart, a salesperson with eleven years of experience. De Smart. ) (to 2 decimals) c. Discuss the differences in your answers to parts (a) and (b). As expected, the prediction interval is much (narrow than the confidence interval.

The data on y = annual sales ($1000s) for new customer accounts and 2 = number of years of ex regression equation y = 68.53 + 3.31z. For these data 7 = 8.3, (. 7) = 206.10, and s = %3! a. Develop the 95% confidence interval for the mean annual sales ($1000s) for all salespersons wit ) (to 2 decimals) D. The company is considering hiring Tom Smart, a salesperson with eleven years of experience. De Smart. ) (to 2 decimals) c. Discuss the differences in your answers to parts (a) and (b). As expected, the prediction interval is much (narrow than the confidence interval.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

49
what is a confidence coefficinent for a simple linear regression line whoes alpha =0.05 and line is y(hat)=-7.45-10x
The Toyota Camry is one of the best-selling cars in North America. The cost of a previously owned Camry depends upon many factors, including the model year, mileage, and condition. To investigate the relationship between the car's mileage and the sales price for a 2007 model year Camry, the following data show the mileage and sale price for 19 sales (PriceHub website). Click on the datafile logo to reference the data. DATA file 1. 18 Price ($1000s) 16 14 12 10 18 6 If your answer is zero, enter "0". a. Select a scatter diagram with the car mileage on the horizontal axis and the price on the vertical axis. 20 40 6,0 8,0 100 Miles (1000s) 120 223 +♡ ♡ Miles(1000s) 29 36 47 63 77 73 87 92 101 110 28 59 68 68 91 42 65 110 Price ($1000s) 16.2 16.0 13.8 11.5 12.5 12.9 11.2 13.0 11.8 10.8 8.3 12.5 11.1 15.0 12.2 13.0 15.6 12.7 8.3
The following table gives the data for the hours students spent on homework and their grades on the first test. The equation of the regression line for this data is yˆ=43.097+1.15x. This equation is appropriate for making predictions at the 0.01 level of significance. If a student spent 32 hours on their homework, make a prediction for their grade on the first test. Round your prediction to the nearest whole number. Hours Spent on Homework and Test Grades Hours Spent on Homework 30 30 31 42 11 27 34 47 5 29 Grade on Test 83 75 75 96 45 76 97 85 53 75
** Based on the regression results, answer the following questions ** A sample of data is collected (from 1999 and 2000) concerning the compensation of the executives (compensation is measured in 1000’s of $’s) of a number of public companies along with other firm-specific data. The dependent variable is total compensation, CEOANN is a dummy variable =1 for an individual who is a CEO and =0 for individuals who are not CEO’s, EMPL is total employees, MKTVAL is the natural logarithm of the market value of the firm, EPSIN is earnings per share, YEAR is a dummy variable = 1 for the year 2000 and =0 for year 1999, and ASSETS is the natural logarithm of the total assets of the company. Based on the regression results, answer the following questions b) What is the estimated regression equation? c) What percentage of the variation in income in explained by the regressors? d) What is the standard error of the error term in the regression equation?
Suppose you wanted to test whether or not the payoff to an additional year of education was the same for men and women in the STEM majors. How would you set up your regression analysis in this case
If you have a b of 0.56 in a regression equation, what does this mean? For every one-unit increase in x, you get an increase of 0.56 in y r = .31 On average, the variability of real scores around the regression line is 0.56 For every 1 standard deviation increase in x, you get an increase of 0.56 standard deviations in y
This table reports the regression coefficients when the returns of the size-institutionalownership portfolio (columns 1 and 2) returns are regressed on three variables: a constant(column 3), the stock market returns (column 4), and the change of the value weighted discountof the closed end fund industry (column 6). Columns 5 and 7 report the corresponding t-statistics of the coefficient estimates. Note that a t-statistic with an absolute value above 1.96means the coefficient estimate is significantly different from 0 at the 1% level. Column 8reports the R square of the regressions. Column 9 reports the mean institutional ownership ofeach portfolio. The last column reports the F-statistics for a multivariate test of the null hypothesis that the coefficient on ΔVWD in the Low (L) ownership portfolio is equal to theHigh (H) ownership portfolio. Two-tailed p-values are in parentheses. 1. What is the main finding of this Table? 2. What is the explanation for…
An investigation into the relationship between an adolescent mother's age x in years and the birth weight y of her baby in grams yielded the regression equation y= - 1163.45 + 245.15x as well as r = .88369, r2= .78091, SSE = 337212.45, and s= 205.30844 1) What is the predicted birth weight for a baby brn to a 17 year old woman? 2) What is the propotion of the variability in the weights of babies born to adolescent mothers that is accounted for by the mother's age? 3) For every additional year in the mother's age that mean birth weight of the baby? (a) increases by about 245g (b) decreases by about 245g (c) increases by about 1163g (d) increases by about 1163g (e) changes by an amount that cannot be determined from the information given.