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ACTIVITY Predict the value of Y given the regression line and the value of X in each item. 1. Y = 0.4X + 11;X = 2.5 2. Y = 21X + 123; X = 29 3. Y = 70X + 59; X = 38 4. Y = 0.43X + 1.1;X = 0.97 5. Y = X +;X = 6. Assume that the height of fathers (X) and the height of their eldest sons (Y) were significantly correlated. The equation of the regression line is Y = 0.23X + 134. Given the height of your own father, predict the height of his eldest son. The height is expressed in centimeters.

ACTIVITY Predict the value of Y given the regression line and the value of X in each item. 1. Y = 0.4X + 11;X = 2.5 2. Y = 21X + 123; X = 29 3. Y = 70X + 59; X = 38 4. Y = 0.43X + 1.1;X = 0.97 5. Y = X +;X = 6. Assume that the height of fathers (X) and the height of their eldest sons (Y) were significantly correlated. The equation of the regression line is Y = 0.23X + 134. Given the height of your own father, predict the height of his eldest son. The height is expressed in centimeters.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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ACTIVITY Predict the value of Y given the regression line and the value of X in each item. 1. Y = 0.4X + 11; X = 2.5 2. Y = 21X + 123; X = 29 3. Y = 70X + 59; X = 38 4. Y = 0.43X + 1.1; X = 0.97 5. Y = X +;X = 6. Assume that the height of fathers (X) and the height of their eldest sons (Y) were significantly correlated. The equation of the regression line is Y = 0.23X + 134. Given the height of your own father, predict the height of his eldest son. The height is expressed in centimeters.
Transcribed Image Text:ACTIVITY Predict the value of Y given the regression line and the value of X in each item. 1. Y = 0.4X + 11; X = 2.5 2. Y = 21X + 123; X = 29 3. Y = 70X + 59; X = 38 4. Y = 0.43X + 1.1; X = 0.97 5. Y = X +;X = 6. Assume that the height of fathers (X) and the height of their eldest sons (Y) were significantly correlated. The equation of the regression line is Y = 0.23X + 134. Given the height of your own father, predict the height of his eldest son. The height is expressed in centimeters.
EXAMPLES Example 1 In the regression line Y = 6X + 9, predict the value Eхample 2 A significant relationship exists between the test scores in Mathematics (X) and the final grade in Mathematics (Y) of grade 11 learners. The regression line is given by the equation Y = 0.3X + 80.9. Predict the final grade of a learner when her test score is 35. of Y when the value of X is 7. Solution: Step 1. Copy the linear equation. Y = 6X + 9 Step 2. Substitute the value of X in the equation Since X = 7, Solution: Step 1. Copy the linear equation. Y = 6(7) + 9 Y = 0.3X + 80.9 Step 2. Substitute the value of X in the equation Since X = 35, Step 3. Solve for Y Y = 42 +9 Y = 51 Y = 0.3(35) + 80.9 Therefore, we can predict that Y is 51 when X is 7. Step 3. Solve for Y Y = 10.5 + 80.9 Y = 91.4 | Therefore, we can predict that the final grade of the | learner is 91.4 (or 91) when she scores 35 in the Mathematics test.
Transcribed Image Text:EXAMPLES Example 1 In the regression line Y = 6X + 9, predict the value Eхample 2 A significant relationship exists between the test scores in Mathematics (X) and the final grade in Mathematics (Y) of grade 11 learners. The regression line is given by the equation Y = 0.3X + 80.9. Predict the final grade of a learner when her test score is 35. of Y when the value of X is 7. Solution: Step 1. Copy the linear equation. Y = 6X + 9 Step 2. Substitute the value of X in the equation Since X = 7, Solution: Step 1. Copy the linear equation. Y = 6(7) + 9 Y = 0.3X + 80.9 Step 2. Substitute the value of X in the equation Since X = 35, Step 3. Solve for Y Y = 42 +9 Y = 51 Y = 0.3(35) + 80.9 Therefore, we can predict that Y is 51 when X is 7. Step 3. Solve for Y Y = 10.5 + 80.9 Y = 91.4 | Therefore, we can predict that the final grade of the | learner is 91.4 (or 91) when she scores 35 in the Mathematics test.
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