Question 5: Week 11 Question 3, (7 Marks)Dex Research Limited conducted a research to investigate consumer characteristics that can be used topredict the amount charged by credit card users. The following multiple regression output is based on adata collected by this research company on annual income, household size and annual credit cardcharges for a sample if 50 consumers.Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations0.9086A0.8181398.0910ANOVAdfSignificance FMSFRegression2DEG.1.50876E-18Residual7448393.148Total4942699148.82Coefficients Standard Errort StatP-valueIntercept1304.9048197.65486.60193.28664E-08Income ($1000s)33.13303.96797.68206E-11Household Size356.295933.200910.73153.12342E-14(4 marks)a. Complete the missing entries from A to H in this outputb. Estimate the annual credit card charges for a three-person household with an annual incomeof $40,000.(2 marks)(1 mark)C.Did the estimated regression equation provide a good fit to the data? Explain

Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9086 0.8181 398.0910 ANOVA Significance F 1.50876E-18 df SS MS F Regression 2 D E Residual 7448393.148 Total 49 42699148.82 Coefficients Standard Error 1304.9048 t Stat P-value Intercept Income ($1000s) Household Size 197.6548 6.6019 3.28664E-08 33.1330 3.9679 7.68206E-11 356.2959 33.2009 10.7315 3.12342E-14 (4 marks) b. Estimate the annual credit card charges for a three-person household with an annual income (2 marks) (1 mark) a. Complete the missing entries from A to H in this output of $40,000. c. Did the estimated regression equation provide a good fit to the data? Explain

Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9086 0.8181 398.0910 ANOVA Significance F 1.50876E-18 df SS MS F Regression 2 D E Residual 7448393.148 Total 49 42699148.82 Coefficients Standard Error 1304.9048 t Stat P-value Intercept Income ($1000s) Household Size 197.6548 6.6019 3.28664E-08 33.1330 3.9679 7.68206E-11 356.2959 33.2009 10.7315 3.12342E-14 (4 marks) b. Estimate the annual credit card charges for a three-person household with an annual income (2 marks) (1 mark) a. Complete the missing entries from A to H in this output of $40,000. c. Did the estimated regression equation provide a good fit to the data? Explain

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?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 8 14 24 27 31 32 Temperature 55 37 20 −5 −27 −41 −57 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)
Pls answer subparts: Questions 4,5,6, 7 A multiple regression analysis between yearly income(y in $1,000s), college grade point average(X1) , age of the individuals (X2), and the gender of the individual (X3); zero representing female and one representing male) was performed on a sample of 10 people, and the following results were obtained. Coefficient Standard Error Constant 4.0928 1.4400 X1 10.0230 1.6512 X2 0.1020 0.1225 X3 -4.4811 1.4400 Analysis of Variance Source of Degrees of Sum of Mean Variance Freedom Squares Square F Regression 360.59 Error 23.91 Write the regression equation for the above. Interpret the meaning of the…
QUESTION 21 The following results are from data concerning the amount withdrawn from an ATM machine based on the amount of time spent at the ATM machine (SECONDS) and the gender, FEMALE (dummy variable = 1 for females and = 0 for males) and an interaction term, SECONDS*FEMALE SUMMARY OUTPUT Regression Statistics Multiple R 0.503 R Square Adjusted R Square Standard Error Observations 50 ANOVA df SS MS F Significance F Regression 2728.7 5.19 0.004 Residual 24161.8 525.3 Total 32348 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 132.6…
QUESTION 2 Below is some of the regression output from a simple regression of the gas mileage (expressed in miles per gallon) (MPG) an automobile gets and the size of the car's engine (expressed in cubic liters) SUMMARY OUTPUT (engine size and MP Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 9 ANOVA df SS MS F Significance F Regression 76.9 76.9 26.96 0.001263155 Residual 20.0 2.9 Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 37.7 2.493 15.13 1.324E-06 31.83 43.62 Engine Size…
QUESTION 19 Below is some of the regression output from a simple regression of the number of wins for a major league baseball team and the size amount of money the team is paying its players (expressed in millions of $'s) SUMMARY OUTPUT Wins v. Team Payroll Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 30 ANOVA df SS MS F Regression 1032.6 1032.6 13.7 Residual 2109.7 75.3 Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 67.6 4.02 16.8 3.515E-16 59.4 75.9 Payroll 0.249…
QUESTION 1 Below is some of the regression output from a simple regression of the gas mileage (expressed in miles per gallon) (MPG) an automobile gets and the size of the car's engine (expressed in cubic liters) SUMMARY OUTPUT (engine size and MPG) Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 9 ANOVA df SS MS F Significance F Regression 76.9 76.9 26.96 0.001263155 Residual 20.0 2.9 Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 37.7 2.493 15.13 1.324E-06 31.83 43.62 Engine Size…
QUESTION 17 STATS The following results are from data concerning the amount withdrawn from an ATM machine based on the amount of time spent at the ATM machine (SECONDS) and the gender, FEMALE (dummy variable = 1 for females and = 0 for males) and an interaction term, SECONDS*FEMALE SUMMARY OUTPUT Regression Statistics Multiple R 0.503 R Square Adjusted R Square Standard Error Observations 50 ANOVA df SS MS F Significance F Regression 2728.7 5.19 0.004 Residual 24161.8 525.3 Total 32348 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 133.6…
Which of following is true or the most appropriate about a scatterplot with regression line or ordinary least square (OLS) regression? Question 18 options: No matter how you decide to draw your straight line, in general, all will will fall directly on the line. Any data point that does not fall directly on the line will have a certain amount of distance between the point and the line. If you were to calculate the distance between the data point and the line you have drawn, and then sum the distance, you would have a regression coefficient for the data points OLS is based on an idea that we have seen before: the sum of deviation.
Which of following is true or the most appropriate about a scatterplot with regression line or ordinary least square (OLS) regression? Question 18 options: No matter how you decide to draw your straight line, in general, all will will fall directly on the line. Any data point that does not fall directly on the line will have a certain amount of distance between the point and the line. If you were to calculate the distance between the data point and the line you have drawn, and then sum the distance, you would have a regression coefficient for the data points OLS is based on an idea that we have seen before: the sum of deviation. Previous PageNext Page