A regression model relating a, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output. Wherentotal = 32,ANOVAdfMSFSignificance FRegression6,665.7ResidualTotal8,845.1CoefficientsStandard Errort StatP-valueIntercept83.010.915Number of42.05.864Salespersonsa. Write the estimated regression equation (to whole number).b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals)F-valuep-value isc. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals)Select your answer, weSelect your answer - v Họt Statp-value isSelect your answer -, weSelect your answer - v Ho: B, =d. Predict the annual sales at the Memphis branch office. This branch employs 11 salespersons.$thousand (to whole number) a. Write the estimated regression equation (to whole number).a. Write the estimated regression equation (to whole number).ŷ =ŷ =b. Compute the F statistic and test the significance of the relationship at a 0.05 b. Compute the F statistic and test the significance of the relationship at a 0.0F-valueF-valuep-value is- Select your answer-p-value is - select your answer -J, we - Select your answer-|Ho, we - Select your answer -significance of the relationship at a 0.05 le c. Compute the t statistic and test the sign reject|Ho- Select your answer- Select your answerc. Comput less than 0.01pat a 0.05do not rejectbetween 0.01 and 0.025between 0.025 and 0.05between 0.05 and 0.10t Statt Statp-value is 9reater than 0.1Owe - Select your answer - v Ho: B1 = p-value is - Select your answer-Select your answer - v Ho: B,d. Predict the annual sales at the Memphis branch office. This branch employs 11 d. Predict the annual sales at the Memphis branch office. This branch employsthousand (to whole number)thousand (to whole number)a. Write the estimated regression equation (to whole number).a. Write the estimated regression equation (to whole number).ŷ =b. Compute the F statistic and test the significance of the relationship at a 0.05 level of b. Compute the F statistic and test the significance of the relationship at a 0.05 levF-valueF-valuep-value is- Select your answer-we - Select your answer-| Hov, we - Select your answer-| Hop-value is - Select your answer-c. Compute thet statistic and test the significance of the relationship at a 0.05 level ofs c. Compute the t statistic and test the significance of the relationship at a 0.05 levet Statt Statp-value is- Select your answer- Select your answer-we - Select your answer - v Họ: B, =p-value is - Select your answer-v Ho : B1 =we-Select your answer-- Select your answerd. Predict less than 0.01nphis branch office. This branch employs 1l salesp d. Predict the annual sales at the Memphis rejectemploys 11 sabetween 0.01 and 0.025between 0.025 and 0.05between 0.05 and 0.10do not rejectber)thousand (to whole number)greater than 0.10

The ANOVA table for simple linear regression model is given by, Sources of variation df SS MS =…

Answered: A regression model relating a, number…

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 regression equation for predicting a woman's muscle mass (a quantitative metric, Y) from age (in years, X) is given as ý = 156.35 - 1.19. The ANOVA table for the linear regression of Y vs X is given below. Source Df SS MS p-value Age 11627.5 11627,5 174.06 <,0001 Error 58 3874.4 66.8 Total 59 15501.9 Fill in the blanks below that demonstrate how to construct an interval estimate for the muscle mass of an individual 60-year-old woman. Use a confidence level of 95%; note that the multiplier is t = 2.002 and the standard error of the mean response is s(i,) = 1.055. 95% Interval: ( Select) + ( Select ) [ Select]
A normal quantile plot of the residuals from this regression model should look like 1. Randomly scattered noise (watermelon seeds) 2. A normal distribution 3. A parabola 4. A uniform distribution 5. A line 6. Symmetric
A study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
A regression model relating x, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output. Where ntotal = 40. ANOVA df SS MS F Significance F Regression 6,759.5 Residual Total 9,031.4 Coefficients Standard Error t Stat P-value Intercept 75.0 10.408 Number of 40.0 5.323 Salespersons a. Write the estimated regression equation (to whole number). ŷ = + b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) F-value p-value is - Select your answer - we - Select your answer - v Ho c. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) t Stat p-value is - Select your answer - we - Select your answer - v Ho: B, d. Predict the annual sales at the Memphis branch office. This branch employs 10 salespersons. $ thousand (to whole number) %24
Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 2) The slope is 2.521 and therefore differs from 0. 3) The slope significantly differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) The slope is equal to 0.
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the neorest fire station. This information will be used in setting rates for insurance coverage. For o somple of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire domage, in thousands of dollars (y). ANOVA table Source Regression Residual SS df MS F 1,870.5782 1,870.5782 41.39 1,265.4934 3,136.0716 28 45.1962 Total 29 Regression output Standard Variables Coefficients Error t(df-28) Intercept Distance-X 13.76815 3.106 2.914 3.77es e. 5861 6.43 Click here for the Excel Data File a-1. Determine the regression equation. (Round your answers to 3 decimal places.) y3D X. a-2 Is there a direct or indirect relotionship between the distance from the fire station and the amount of fire damage? The relationship between distance and damage is b. How much domage would…
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Which of the following tools is not appropriate for studying the relationship between two numerica variables? Correlation coefficient Scatter plot Historam Regression
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An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.

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