Which of the following is true or the most appropriate explanation about regression analysis? Notice that the variable I am interested in predicting is designated as my independent variable, and the variable that I am using to predict become my predictor or dependent variable. The purpose of regression analysis is to make predictions about the value of the dependent variable given certain values of the predictor variable. O Stronger correlations lead to less accurate predictions. The simple linear regression is really a more powerful tool than simple correlation analysis

Which of the following is true or the most appropriate explanation about regression analysis? Notice that the variable I am interested in predicting is designated as my independent variable, and the variable that I am using to predict become my predictor or dependent variable. The purpose of regression analysis is to make predictions about the value of the dependent variable given certain values of the predictor variable. O Stronger correlations lead to less accurate predictions. The simple linear regression is really a more powerful tool than simple correlation analysis

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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Two variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. Oŷ = 0.32x 30 Oy = 0.82x - 30 Oy y = -0.32x + 30 -0.82x + 30
The correlation between first year college GPA and high school GPA is 0.683. If a simple linear regression was conducted to predict first year college GPA from high school GPA what does the correlation tell us about the fit of the model?
If the estimated intercept of the regression equation is negative, we can say the estimated correlation coefficient between the two variables is also negative. O True O False
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True or false? If the statement is false, use one or two sentences to justify your answer.
Thank you for your response! that was helpful. I thought the research scenario was imlying a positive correlation, such that as height increases, weight increases. Thus, i thought correlation would be most appropriate. Especially considering that we do not have a data set to predict from. I did not know we can do a linear regression without having a known variable to predict from. Just clarifying, would it still not be appropriate to do a correlation?
Can you please check my work
what can go wrong and the caution that needs to be exercised when using regression models.
The following data shows memory scores collected from adults of different ages. Age (X) Memory Score (Y) 25 10 32 10 39 9 48 9 56 7 Use the data to find the regression equation for predicting memory scores from age. The regression equation is: Ŷ = 4.33X + 0.11 Ŷ = -0.11X + 4.33 Ŷ = -0.11X + 13.26 Ŷ = -0.09X + 5.4 Ŷ = -0.09X + 12.6 Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 28 For the calculations, leave two places after the decimal point and do not round: Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 43 For the calculations, leave two places after the decimal point and do not round: Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 50 For the calculations, leave two places after the decimal point and do not round:
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?