9.Suppose that the Ri^2 obtained in a secondary regression of Xi on a model'sother explanatory variables is 0.6. In this case, the VIF for ^Bi will equalindicating by the "rule of thumb" that severe multicollinearity is.A. 2.6; presentB. 2.5; not presentC. 5.6; presentD. 1.67; not presentAnswer B

Given that Ri2 = 0.6 We have to find variance inflation factor VIF to detect the presence of…

Answered: 9.Suppose that the Ri^2 obtained in a…

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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11. What is the standard error of the slope of the regression line (SEb)? Please put the answer in bold.
Given are five observations collected in a regression study on two variables. xi 2 6 9 13 20 yi 7 18 9 26 23 Compute b0 and b1 (to 1 decimal).b1 b0 Complete the estimated regression equation (to 1 decimal).^y = + x Use the estimated regression equation to predict the value of y when x = 6 (to 1 decimal).^y =
In an analysis of a sample of bivariate data concerning the soil acidity x (in pH) and germination time y (in days) for tomato seeds, a linear regression model is constructed and follows the equation y^=−1.22x+5y^=−1.22x+5 Select the most appropriate statement about the linear correlation exhibited. A. The data set exhibits strong positive linear correlation. B.We cannot determine anything about the strength or direction of linear correlation from this information alone. C. The data set exhibits strong negative linear correlation. D. The linear correlation coefficient is -1.22.
) Is there evidence of a nonlinear relationship between birth weight andmother’s weight gain?
Suppose that you run a correlation and find the correlation coefficient is 0.338 and the regression equation is ˆy=−12.7+4.3xy^=-12.7+4.3x.The mean for the xx data values was 7, and the mean for the y data values was 17.A T Test for the slope of the regression line is performed, and the p-value is greater than the level of significance of 0.05. Use the appropriate method to predict the y value when x is 4.4.
Complete the missing information for this regression model. Note: N=26N=26. (Except for P-values, report all values accurate to 3 decimal places. For P-values, report accurate to 4 decimal places.) ˆYY^ = 26.759 + 0.818X10.818X1 −- 2.999X22.999X2 + X3X3 2.382 0.547 1.674 0.734 Standard Errors 11.234 1.495 t-ratios 0.0991 P-values
We are interested in estimating the following model log(wage) = Bo + Bieduc + Bzexper + u where • wage=hourly wage, in US dollars; • educ=number of years of education; • exper=number of years of work experience. The variable ctuit is the change in college tuition facing students from age 17 to age 18 and is used as an IV for educ. We run the first stage regression for educ and get the following output: Source s df MS Number of obs 1,230 F (2, 1227) 550.19 Model 3220.84426 2 1610.42213 Prob > F 0.0000 Residual 3591.43541 1,227 2.92700523 0.4728 R-squared Adj R-squared 0.4719 Total 6812.27967 1,229 5.54294522 Root MSE 1.7108 educ Coef. Std. Err. t P>|t| [95% Conf. Interval] ctuit -.1859575 .0608175 -3.06 0.002 -.3052752 -.0666398 exper -.521161 .0157156 -33.16 0.000 -.5519933 -.4903286 _cons 18.63905 .1757961 106.03 0.000 18.29415 18.98394 Is the assumption of instrument relevance satisfied? Why yes, or why not?
A researcher found a linear correlation between course grades and the average number of hours spent on a mobile phone each day. The line of best fit has equation y ^ = 3.7 − 0.786 x with correlation coefficient r = − 0.84. a. Does this prove that spending too much time on a phone causes students to get lower grades? b. What does the regression line predict your final grade in a course will be if you spend an average of 150 minutes per day on your phone?
For the linear regression model Y = bo + b1(X): The p-value for the intercept is large: about 0.98 The p-value for the slope is very small: less than 2 times 10^(-16) What can we conclude? Since the p-value for the intercept is large, we can conclude that there is not a strong correlation between X and Y. Since the p-value for the intercept is large, we can conclude that there is a very strong correlation between X and Y. Since the p-value for the slope is very small, we can conclude that there is a very weak correlation between X and Y. Since the p-value for the slope is very small, we can conclude that there is a very strong correlation between X and Y. We are not able to assess the strength of the correlation between X and Y with the output provided.
A study of king penguins looked for a relationship between how deep the penguins dive in meters (x) and how long they stay underwater in minutes (y). The study reported that the regression equation was ˆy=−0.044x+4.937y^=-0.044x+4.937 and the coefficient of determination was 0.81What is the correlation coefficient for this data set (round your answer to 2 decimal places)?
The regression line for a given dataset is found to be y^ =76−7.51xx. There is a pair of values x =7 and y =42 in this dataset taken from a specific individual. Compute the residual (error) of the predicted value y^ for this individual. Give your answer to two decimal places
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"† describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in French fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. FryingTime AcrylamideConcentration 150 155 240 115 240 190 270 180 300 140 300 265 (a) Construct a scatterplot of these data. A scatterplot has 6 points.The horizontal axis is labeled "x" and ranges from 100 to 350.The vertical axis is labeled "y" and ranges from 50 to 350.The points are plotted from left to right in a horizontal direction starting from the lower left side of the diagram.Along the horizontal axis, there is 1 point at 150, 2 points…

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9.Suppose that the Ri^2 obtained in a secondary regression of Xi on a model's other explanatory variables is 0.6. In this case, the VIF for ^Bi will equal indicating by the "rule of thumb" that severe multicollinearity is