Table 8.1: Selected Excel output from a simple linear regression of levels of stress and average hoursof cycling a week; n=80ANOVASignificancedfMSFFRegression1 330.0887 330.0887 439.59268.61E-34Residual78 58.56994 0.750897Total79 388.6586StandardCoefficientsErrort StatP-valueInterceptHours of cycling8.174779154 0.201398 40.59015<0.0001(week)-0.151563181 0.007229 -20.9665<0.0001By looking at the output in Table 8.1, write down the equation of the fitted regression model, andexplain the meaning of the regression coefficients in context. Comment on the statistical significanceof the regression coefficients. State the alternative name for R?. Calculate the R? for the regression fit shown in Table 8.1 (ofQuestion 8). Round your answer to 2dp. Show your calculations and any formulas used. Fullyinterpret your answer in context and hence comment on the goodness of fit of this regression model.

Answered: Image /qna-images/answer/2bbae6c6-2992-4980-b36b-8e19c2a79c9f.jpg

Answered: Table 8.1: Selected Excel output from a…

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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9:33 AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-. Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Coffee sales, y (in dollars) Fahrenheit) 68.3 1787.6 38.9 2311.0 72.9 1684.0 2200 58.8 1979.5 63.4 1816.4 2000 75.8 1997.6 46.9 2131.7 71.0 1973.9 100 38.7 2018.2 1400 53.9 1597.5 45.9 1996.1 75.4 1501.0 50.9 2266.3 81.7 1574.5 Figure 1 45.5 1798.9 Send data to Excel After deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers…
Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 Correlation between Biking and Heart. Disease is: -0.94616452. Correlation between Smoking and Heart. Disease is: 0.32421. 1) State r 2 (i.e., the coefficient of determination) for “Biking” and “Heart.Disease” and explain what this value means in context of the data set.
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70
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
Please help interpret this SPSS output of a multiple regression analysis result? DV- To what extent the product safety incident have changed your perceptions of the involved brand(s) IV'S- Trustworthy , Reliability , I don’t know how good the company’s products will be before I buy it. , Preventability
The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 15.1540376815.15403768 15.1540376815.15403768 16.0586340516.05863405 0.0102486910.010248691 Residual 55 4.7183457924.718345792 0.943669160.94366916 Total 66 19.8723834719.87238347 Step 2 of 3 : What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places
The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…
6. A small company can employ 3 workers. Each worker independently stays on the job for an exponentially distributed time with a mean of one year and then quits. When a worker quits, it takes the company an exponentially distributed time with a mean of 1/10 of a year to hire a replacement for that worker (independently of how long it takes to replace other workers). If the company takes in $1000 per day in revenue when it has 3 workers, $800 per day when it has 2 workers, and $500 per day when it has 0 or 1 workers, what is the company's long-run average daily revenue?
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error Intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is…
Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model: SALES=a+BAGE+YMIL+SENG Where SALES= amount that a vehicle is sold for($000's), AGE = age of the vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained:
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. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)

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