NO handwriting please The President of the Farmers Association wants to know how theamount of fertilizer and the amount of water given to plants affect theirgrowth. The results were inputted into MINITABSO as to fit the model.Regression Analysis: Growth versus Water, FertilizerThe regression equation isGrowthB + ¢ Water + aFertilizerPredictorСоefSEСоefTP42.083.1180.4606.083Constant**0.000.3860.654Water0.178Fertilize5.7900.952rS6.159R-Sg77.1%%R-Sq(adj)73.3%Analysis of VarianceSourceDFSSMSFP1532.6 766.30312 455.13 37.928*****Regression01ResidualErrorTotal14 1987.733Write out the regression equationWhat is the sample size used in this investigation?Determine the values of *, ** and *** ****i.ii.iii.

The President of the Farmers Association wants to know how the amount of fertilizer and theamount of water given to plants affect their growth. The results were inputted into MINITAB so as to fit the model. Regression Analysis: Growth versus Water, Fertilizer The regression equation is Growth = ß + ¢ Water + aFertilizer Predictor Coef SE Соef T P 3.1 18 0.4 60 6.0 83 42.08 0.00 Constant *大 0.386 0.65 4 Water 0.178 Fertilize 5.790 0.952 r S = 6.159R-Sq 77.1%% R-Sq (adj) 73.3% Analysis of Variance Source SS 1532.6 766.3 01 12 455.13 37.928 DF MS F P *** Regression ** 03 Residual Error Total 14 1987.7 33 Write out the regression equation What is the sample size used in this investigation? Determine the values of *, i. ii. iii. * ** and *** ****

The President of the Farmers Association wants to know how the amount of fertilizer and theamount of water given to plants affect their growth. The results were inputted into MINITAB so as to fit the model. Regression Analysis: Growth versus Water, Fertilizer The regression equation is Growth = ß + ¢ Water + aFertilizer Predictor Coef SE Соef T P 3.1 18 0.4 60 6.0 83 42.08 0.00 Constant 大 0.386 0.65 4 Water 0.178 Fertilize 5.790 0.952 r S = 6.159R-Sq 77.1%% R-Sq (adj) 73.3% Analysis of Variance Source SS 1532.6 766.3 01 12 455.13 37.928 DF MS F P Regression 03 Residual Error Total 14 1987.7 33 Write out the regression equation What is the sample size used in this investigation? Determine the values of , i. ii. iii. and * ****

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking relate to the risk of strokes (Dataset "Stroke"). Risk is interpreted as the probability (times 100) that a person will have a stroke over the next 10-year period. For the smoker variable, 1 indicates a smoker and 0 indicates a nonsmoker. a. Develop an estimated regression equation that can be used to predict the risk of stroke given the age and blood-pressure level. b. Consider adding two independent variables to the model developed in part (a), one for the interaction between age and blood-pressure level and the other for whether the person is a smoker. Develop an estimated regression equation using these four independent variables. c. At a 0.05 level of significance, test to see whether the addition of the interaction term and the smoker variable contributes significantly to the estimated regression equation developed in part (a). d. Refer to the model developed in part…
A business statistics professor would like to develop a regression model to predict the final exam scores for students based on their current GPAs, the number of hours they studied for the exam, the number of times they were absent during the semester, and their genders. The data for these variables are given in the accompanying table at the bottom of this page. a) Using Excel, construct a regression model using all of the independent variables. Create the dummy variable Gen, which equals 1 for a male and 0 for a female student ( this assignment is arbitrary) complete the regression equation for the model below, where y= Score, x1= GPA, x2= Hours, x3= Absenses, and x4= Gen. y= (__) + (__)x1 + (__)x2 + (__)x3 + (__)x4 b) Test the significance of the overall regression model using a= 0.10. c) interpret the meaning of the regression coefficient for the dummy variable. d) using the p-values, identify which independent variables are significant with a= 0.10. e) construct a regression…
A researcher was investigating variables that might be associated with the academic performance of high school students. The data included the average Math SAS score of all high school seniors in the city that took the exam (labeled as the variable SAT-M), the average number of dollars per pupil spent on education by the city (labeled as the variable $Per Pupil), and the percentage of high school seniors in the city that took the exam (labeled as the variable %Taking). The researcher ran the following multiple linear regression model as SAT-M=Beta0 + Beta1($Per Pupil) + Beta2(%Taking). This model was fit to the data using the method of least-squares, results shown inside of table within photo. If we want to test using ANOVA F-test with hypotheses Ho: Beta1=Beta2=0 versus H1: at least one of the Beta is not 0, what would the value of our F-statistic mean?
The issue of multicollinearity impacted the 'vadity and trustworthiness' of a regression model. demonstrate how this issue can be a problem by using an appropriate hypothetical and mathematical example.
As large U.S. companies flattened their management hierarchies during the 1990s, many of the middle managers who were laid off gave up on “corporate life” and started their own companies or bought franchise operations. This talent migration, along with the fact that most new jobs in the United States were being created by small businesses, cast entrepreneurism into the national spotlight. The business listed in the table were among the fastest growing in the franchise industry. instuction: Propose a regression model that will enable you to compare the mean number of new franchises for the four types of franchises (food, cleaning, accounting/consulting, hospitality) Provide a clear definition for the variables (dependent and independents) being used in the model.
The data point height=175 cm, height =80 cm was misrecorded as height=145 cm, height =80 cm. If we use the simple linear regression and do not correct the mistake, what is the impact of this point on the model?
A local University conducted a survey of over 2,000 MBA alumni to explore the issue of work-life balance. Each participant received a score ranging from 0 to 100, with lower scores indicating a higher imbalance between work and life. A sample of the data is available below. Let x=average number of hours worked per week and y=work-life balance scale score for each MBA alumnus. Investigate the link between these two variables by conducting a complete simple linear regression analysis of the data. Summarize your findings. Hours WLB Score 50 73.22 45 70.79 50 48.78 55 44.15 55 69.89 60 53.15 50 56.04 60 22.53 50 60.73 55 69.89 70 29.17 45 70.79 40 33.38 40 32.13 45 46.17 The least squares regression equation is y=enter your response here+enter your…
A local University conducted a survey of over 2,000 MBA alumni to explore the issue of work-life balance. Each participant received a score ranging from 0 to 100, with lower scores indicating a higher imbalance between work and life. A sample of the data is available below. Let x=average number of hours worked per week and y=work-life balance scale score for each MBA alumnus. Investigate the link between these two variables by conducting a complete simple linear regression analysis of the data. Summarize your findings. Hours WLB Score 50 73.22 45 70.79 50 48.78 55 44.15 55 69.89 60 53.15 50 56.04 60 22.53 50 60.73 55 69.89 70 29.17 45 70.79 40 33.38 40 32.13 45 46.17 Check the usefulness of the hypothesized model. What are the hypotheses to test?…
A local University conducted a survey of over 2,000 MBA alumni to explore the issue of work-life balance. Each participant received a score ranging from 0 to 100, with lower scores indicating a higher imbalance between work and life. A sample of the data is available below. Let x=average number of hours worked per week and y= work-life balance scale score for each MBA alumnus. Investigate the link between these two variables by conducting a complete simple linear regression analysis of the data. Summarize your findings. Hours WLB Score 50 74.09 45 72.45 50 52.93 55 44.33 50 69.15 60 54.79 55 56.26 60 20.44 55 6-.64 50 69.15 70 29.16 45 72.45 40 33.25 40 32.18 45 45.76 a. What is the test statistic for the hypotheses? t=______ b. What is the p-value for the test statistic? p-value=________ c. What is the value for the coeffiecent of determination r^2? r^2=________
When should a regression model not be used to make a prediction?
A fast-food chain decided to carry out an experiment to assess the influence of advertising expenditure on sales. Different relative changes in advertising expenditure, compared to the previous year, were made in eight regions of the country, and resulting changes in sales levels were observed the accompanying table shows the results. Increase in advertising expenditure (%) 0 5 15 20 25 30 35 40 Increase in sales (%) 5 10 18 25 35 50 60 65 Determine the value of regressions coefficients and write down the simple linear regression model.