Question

a. 93.5 b. 18.3 C. 4.22 d. 27 Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. 25) The matched pairs are given as: xlo18 | 20 | 28 | 36 424 13 20 30 a. 9 = 0.8012 - 3.79 b. 9 = 0.897x -2.79 c. 9=0.950x - 2.79 d. 9 -0.997x - 3.79 Use the following information for problems 26 through 29. R&D engineers for a car manufacturer wanted to test the relationship between a cars speed x (mph) and its gas mileage y (mpg) using regular unleaded gas. The car was driven at different speeds in controlled conditions and the following gos mileage was observed. Speed, x mpgy 30 39 40 38 50 36 60 32 70 27 80 24 90 22 26) What kind of relationship is suggested by a scatter plot for the data above? a. Non-linear b. Parabolcc . Normal d . Linear 27) What is the correlation coefficient indicated by the relationship? 0. 10.75449 b. 0.985349 C. -0.985349 d. -0.3142857 28) What kind of mpg should you expect if they drove at 66 mph? a.-29 b.-31 c. -27 d. -30 29) For every 1 mph increase in speed, what effect would you see in mpg? a. gas mileage should improve slightly, about 1/3 mpg. b. gas mileage should be reduced slightly, about 1/3 mpg. C. gas mileage should remain about the same. d. gas mileage is not dependent on speed driven. Is the data point, P, an outller, an influential point, both, or neither? 301 Pass

Answer 1

Question-25

36.209951 - 26.209951 - Y-Dependent 16.209951 - 6.209951 - -3.790049 1 0 10 40 20 30 X - Independent - Regression Line (ỹ = 0.9X - 3.79) Calculation Summary Sum of X= 98 Sum of Y = 69 Mean X = 19.6 Mean Y = 13.8 Sum of squares (SSx) = 659.2 Sum of products (SP) = 591.6 Regression Equation = û = bx + a b = SPISSx= 591.6/659.2 = 0.89745 a = My - bMy = 13.8 - (0.9*19.6) = -3.79005 û = 0.89745X - 3.79005

26] linear

27] - 0.985349

X Values Y Values 30 Y Values X Values X - MX -30.000 -20.000 - 10.000 0.000 10.000 20.000 30.000 Y - My 7.857 6.857 4.857 0.857 -4.143 -7.143 -9.143 (X - My)2 900.000 400.000 100.000 0.000 100.000 400.000 900.000 (Y - My)2 61.735 47.020 23.592 0.735 17.163 51.020 83.592 (X - Mx)(Y - My) -235.714 - 137.143 -48.571 0.000 -41.429 - 142.857 -274.286 Mx: 60.000 My: 31.143 Sum: 2800.000 Sum: 284.857 Sum: -880.000 Result Details & Calculation Key X Values = 420 Mean = 60 E(X - Mx)2 = 55x = 2800 X: X Values Y: Y Values My: Mean of X Values Mji Mean of Y Values X - MX & Y - My: Deviation scores (X - MX) & (Y - MyH: Deviation Squared (X - MX)(Y - My): Product of Deviation Scores Y Values = 218 Mean = 31.143 ETY - My)2 = SSy = 284.857 X and Y Combined N = 7 E(X - MX/Y - My) = -880 R Calculation r = (X - My)(Y - Mx)/ ((SSx)(SSy)) r=-880 / v((2800)(284.857)) = -0.9853 Meta Numerics (cross-check) r= -0.9853 The value of Ris -0.9853.

Y-Dependent - 50 90 110 10 i X - Independent - Regression Line (ŷ =-0.31X +50) Calculation Summary Sum of X = 420 Sum of Y= 218 Mean X = 60 Mean Y = 31.1429 Sum of squares (SSX) = 2800 Sum of products (SP) = -880 Regression Equation = ý = bx + a b = SPISSx= -880/2800 = -0.31429 a = My-bMx = 31.14 - (-0.31*60) = 50 y = -0.31429X + 50

és 2o. a ý = 0.897 * = 329 A linan nar -0.985349 y = for 50- 0. 314294 n=66 50 - 0.31429 x 66 Ÿ= = 29.25 686. afal 29 I MY . (3) Ansuris ® ✓ @ neither .