Lab 9 - max_ pul s e = 210. 07 - 0. 7613 age N 31 Rs q 0....

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Unformatted text preview: max_ pul s e = 210. 07 - 0. 7613 age N 31 Rs q 0. 1874 Adj Rs q 0. 1594 RMSE 8. 4021 155 160 165 170 175 180 185 190 195 age 37. 5 40. 0 42. 5 45. 0 47. 5 50. 0 52. 5 55. 0 57. 5 Azfar Khandoker Dr. Knapp Stat 350 4:30 Lab #9 1. a. = .- . y 210 06928 0 76126x b. c. 18.74% d. = . r 0 4329 e. estimate : -0.76126 (-1.26140,-0.26112) f. Yes, there is a significant linear relationship between the maximum running pulse and age. F = 6.69 df 1 = 1 , df 2 = 29 p-value = 0.0150 g. = . y 168 961 h. residual = . 1 0390 i. (164.8899,173.0321) j. (154.1158,183.8063) 2. a. i. = . + . y 38 51927 0 13894x hei ght = 38. 519 +0. 1389 age N 237 Rs q 0. 4210 Adj Rs q 0. 4186 RMSE 3. 0085 50 55 60 65 70 75 age 130 140 150 160 170 180 190 200 210 220 230 240 250 ii. iii. (0.11134,0.16654) iv. 42.10% v. Yes, there is a statistically significant linear relationship between age and height. F = 170.88 df 1 = 1 , df 2 = 235 p-value = less than 0.0001 b. i. Model #: 1 2 3 Units of Height (y): Inches Inches Cm Units of Age (x): Months Years Months Estimate of y-intercept (a): 38.51927 38.51927 97.83895 Estimate of slope (b): 0.13894 1.66723 0.35290 Coefficient of Determination (R 2 ) 0.4210 0.4210 0.4210 Test statistic (t) for H o : = 0 13.07 13.07 13.07 ii. Multiplying a scalar to the explanatory variable does not change the y-intercept of the regression equation. However, multiplying the response by a scalar does change the y-intercept. Multiplying a scalar to either of the variables changes the slope. To the contrary, properties of the variables relationship, i.e. R 2 and the test statistic for the true population slope, are unchanged regardless of the units that are involved. c. i. hei ght = 38. 519 +0. 1389 age N 237 Rs q 0. 4210 Adj Rs q 0. 4186 RMSE 3. 0085- 10. 0- 7. 5- 5. 0- 2. 5 0. 0 2. 5 5. 0 7. 5 10. 0 age 130 140 150 160 170 180 190 200 210 220 230 240 250- 3- 2- 1 1 2 3- 10. 0- 7. 5- 5. 0- 2. 5 2. 5 5. 0 7. 5 10. 0 R e s i d u a l Nor mal Quant i l es The residuals seem to have random scatter. From this plot, it is safe to assume that the relationship between age and height is linear with constant variance. ii. Since the residuals follow a constant linear pattern, it is safe to assume that the residuals are normally distributed. APPENDIX (CODE/OUTPUT): data q1; input age weight oxy_intake run_time rest_pulse run_pulse max_pulse; cards ; 44 89.47 44.609 11.37 62 178 182 40 75.07 45.313 10.07 62 185 185 44 85.84 54.297 8.65 45 156 168 42 68.15 59.571 8.17 40 166 172 38 89.02 49.874 9.22 55 178 180 47 77.45 44.811 11.63 58 176 176 40 75.98 45.681 11.95 70 176 180 43 81.19 49.091 10.85 64 162 170 44 81.42 39.442 13.08 63 174 176 38 81.87 60.055 8.63 48 170 186 44 73.03 50.541 10.13 45 168 168 45 87.66 37.388 14.03 56 186 192 45 66.45 44.754 11.12 51 176 176 47 79.15 47.273 10.6 47 162 164 54 83.12 51.855 10.33 50 166 170 49 81.42 49.156 8.95 44 180 185 51 69.63 40.836 10.95 57 168 172 51 77.91 46.672 10 48 162 168 48 91.63 46.774 10.25 48 162 164 49 73.37 50.38873....
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Lab 9 - max_ pul s e = 210. 07 - 0. 7613 age N 31 Rs q 0....

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