Consider the regression équation In =3.94+0.4X₁₁ +0.7X₂-a. Predict the value of Y when X₁ =7.4 and X₂ = 5.2.b. Interpret the meaning of the regression coefficients 100 b,, and 100-b₂.a. The predicted value of Y is(Type an integer or decimal rounded to two decimal places as needed.)b. Choose the correct answer below.OA. Holding constant the other variables, for every one unit increase in X, or X₂, the value of Y increases by b, or by percent, respectively.B. Holding constant the other variables, for every one percent increase in X, or X₂, the value of Y increases by b, or b₂ percent, respectively.C. Holding constant the other variables, for every one unit increase in X, or X₂, the value of Y increases by 100 b, or 100-b₂ percent, respectively.OD. Holding constant the other variables, for every one percent increase in X, or X₂, the value of Y increases by b, or b₂ units, respectively.OE. For every decrease of one unit of In Y, holding bo constant, the estimated change in X, or X₂ is 100-b, or 100-b₂ units, respectively.

Given regression equation: Yi^=3.94+0.4X1i+0.7X2i

Answered: Consider the regression équation In…

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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A researcher that there is a linear association between the level of potassium content (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line for the data is computed to y=37+28x rate. It was also computed that r=.59 A. What does the value . 59 tell you? B. What does the value 37 tell you? C. What does the value 28 tell you ? 2. For the line in question 1 what percentage of variability in potassium can be explained by variability in fiber?
Interpret the regression results.
Given the regression equation Y = 43 - 10X, the change in Y when X changes by -3 is A. -30 B. 30 C. -13 D. 13
Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.
4
In multiple regression, when all independent variables are considered but the order in which they are added to the model is determined by the amount it contributes to the dependent variable, it is called: a.Two step b.Stepwise c.Simultaneous d.Backward step
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. The table shows the shoe size and heights (in.) for 6 men. Shoe size, x Height, y Find the regression equation. ŷ=x+ (Round to three decimal places as needed.) Choose the correct graph below. O A. Height (in.) 75+ 65- 8 Shoe size N O B. Height (in.) 75+ 65+ 8.5 65.0 Shoe size 9.5 66.0 o 11.0 67.0 O C. Height (in.) 75 65 8 Shoe size 12.0 73.0 Q 13.0 71.0 D. Height (in.) 75- 65 13.5 73.0 Shoe size Q o
2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?
The rest of the D is.... c. 107.1 d. Not meaningful
d. Draw the regression line on the scatter diagram using the yp points.e. Find y for x = 50 x 20 10 30 40 60 70 y 20 20 30 50 60 80
Use the regression equation to predict the value of y for x = -1.6. Assume that the variables x and y have a significant correlation. -5-34 1 -1-2 X 0 2 3 -4 y - -10 8 9 1 -2-6 -1 3 6 -8 OA. 1.214 OB. -2.803 O C. 2.980 OD. -3.907

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