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Question 8 1 pts Mika examined whether ocean water depth was related to available oxygen levels. She took 8 random sampl

Posted: Thu Apr 28, 2022 7:10 am
by answerhappygod
Question 8 1 Pts Mika Examined Whether Ocean Water Depth Was Related To Available Oxygen Levels She Took 8 Random Sampl 1
Question 8 1 Pts Mika Examined Whether Ocean Water Depth Was Related To Available Oxygen Levels She Took 8 Random Sampl 1 (67.01 KiB) Viewed 31 times
Question 8 1 pts Mika examined whether ocean water depth was related to available oxygen levels. She took 8 random samples and gathered the following data: 20 60 40 depth The regression line was calculated to be y = 65.35 +-0.6x. The residual sum of squares was 2717.4. Use the table below to calculate the Goodness-of-fit F ratio value for this regression model. You -F can assume the mean oxygen level is 46.5. Give your answer to two decimal places. Water Depth Oxygen Levels y. lygy Mean Oxygen level 5 81 46 5 34.5 1190.25 15 44 46 5 -2.5 6.25 22 65 46 5 18.5 342.25 43 62 46.5 15.5 240.25 47 50 46 5 61 16 46.5 20 19 46 5 -27,5 756.25 40 35 46.5 - 11.5 132.25

Question 7 1 pts The regression line between self-esteem and happiness for a sample of students was calculated as y = 2.42 +0.84x Use the table below to help you calculate the standard error of the estimate Oest for this linear regression model. Give your answer to three decimal places. 21 18 happiness 15 12 125 20.0 22 150 175 self_esteem Sun-esteem Happiness Raw Residual Predicted Happiness Raw Residual Squared 16 20 15.86 4.14 17.14 15 15 15.02 -0.02 0.00 18 14 17.54 -3.54 12.53 22 21 12 13 16 16 15.86 0.14 0.02 11 10 11.66 -1.66 2.76 2.162

Question 1 1 pts In a simple linear regression, what does b refer to in the regression line equation y = a + bx? The mean value of x O The change in y relative to a 1 unit change in x y The correlation between x an y O The predicted value of y when x=0 y O The mean value of y

Question 3 1 pts Which of the following equations is feasible for the regression line in the graph below? 8- 7 6 y value 3 2. 1 . 0 4 5 x value O y = 2.5 + x O y = 2.0 - 2.8x O y = 1.4 + 1.2x O y = 1.2 + 2.4x