NAME: ___________________________________________ REVIEW WORKSHEET ON CHAPTER 4 NAME: ___________________________________________ XXXXXXXXXXWORKSHEET # 3 Due ​​​​​__________________________ Fifteen...

I have attached the HW file


NAME: ___________________________________________ REVIEW WORKSHEET ON CHAPTER 4 NAME: ___________________________________________ WORKSHEET # 3 Due ​​​​​__________________________ Fifteen females of varying ages were asked to keep a record of the number of phone calls made to family/friends during the course of a week. The results are given below: age 13 20 52 38 29 64 16 60 58 40 24 89 35 18 44 calls 55 25 15 28 26 8 49 10 13 21 40 4 32 45 22 ANSWER THE FOLLOWING QUESTIONS FOR THE PROBLEM ABOVE. USE THE COMPUTER PRINTOUT PROVIDED TO CHECK YOUR ANSWERS. 1. Which of the following is true about the two variables? a. The age of the woman is the explanatory variable and the number of calls is the response variable. b. The number of calls is the explanatory variable and the age of the woman is the response variable. c. It is not possible to tell which variable is explanatory and which is response. --------------------------------------------------------------------------------------------------------------------------------------- 2. How many pieces of data are there in the data set? a. 15 b. 30 --------------------------------------------------------------------------------------------------------------------------------------- 3. Draw a scatter plot. Be sure to label both axes. _________________________________________________________________________ 4. The relationship between the two variables is: a. positive b. negative c. cannot be determined 5. This means that: a. younger women make fewer phone calls per week b. older women make fewer phone calls per week c. older women make more phone calls per week 6. Find the values for Sxx, Syy, and Sxy. Show all work. x y x2 y2 xy work for Sxx work for Syy work for Sxy --------------------------------------------------------------------------------------------------------------------------------------- Give the three values here: Sxx = __________________ Syy = __________________ Sxy = __________________ 7. How strong is the linear relationship between the age of the female and the number of calls per week? To answer this question, which variable should be calculated? a. slope b. y-intercept c. correlation coefficient d. coefficient of determination Calculate this value. Show all work. Interpret the meaning of this value in terms of the problem. 8. Find the least squares regression line. Use the formulas and show the work. Compare with the printout. slope y-intercept equation 9. Interpret the slope and y-intercept of the line in terms of the problem. slope y-intercept 10. How well does the regression line found in #8 fit the data? To answer this question, which variable should be calculated? a. slope b. y-intercept c. correlation coefficient d. coefficient of determination Calculate this value. Show all work. Interpret the meaning of this value in terms of the problem. (There are 2 parts to this interpretation.) 11. Calculate the residual by hand for a 38 year old female. Show all work, label the answer, and check your results with the printout. 12. Estimate the number of calls made per week by a female who is: (Show all work; label answers.) a. 42 years old b. 8 years old 13. Can it be concluded from these calculations that the age of a female has an effect on the number of calls made per week? Explain thoroughly. ROW age calls predcall resid 1 13 55 43.7776 11.2224 2 20 25 39.2204 -14.2204 3 52 15 18.3877 -3.3877 4 38 28 27.5020 0.4980 5 29 26 33.3612 -7.3612 6 64 8 10.5755 -2.5755 7 16 49 41.8245 7.1755 8 60 10 13.1796 -3.1796 9 58 13 14.4816 -1.4816 10 40 21 26.2000 -5.2000 11 24 40 36.6164 3.3836 12 89 4 -5.7001 9.7001 13 35 32 29.4551 2.5449 14 18 45 40.5225 4.4775 15 44 22 23.5959 -1.5959 The regression equation is calls = 52.2 – 0.651 age Predictor Coef Stdev t-ratio p Constant 52.241 3.892 13.42 0.000 age -0.65102 0.08637 -7.54 0.000 s = 6.940 R-sq = 81.4% R-sq (adj) = 79.9% Analysis of Variance SOURCE DF SS MS F p Regression 1 2736.2 2736.2 56.81 0.000 Error 13 626.2 48.2 Total 14 3362.4