Question

The key advantage of bivariate regression over correlation is that regression can be used for prediction....

The key advantage of bivariate regression over correlation is that regression can be used for prediction. Explain this period how is it that regression can be used to predict values not in the data set, but correlation cannot?

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Explain the differences and similarities of correlation analysis and bivariate regression analysis
Explain the differences and similarities of correlation analysis and bivariate regression analysis
The regression line can never be used for prediction. True False The slope is the amount...
The regression line can never be used for prediction. True False The slope is the amount Y changes for every increase in X. True False "When you calculate a regression equation, you want the line with the most error. " True False "Correlation measures the linear relationship between two variables, while a regression analysis precisely defines this line. " True False The predicted value based on a regression equation is a perfect prediction. True False What represents the intercept (the...
k-nearest neighbors and regression trees are methods that can be used to predict numeric outcomes. Both...
k-nearest neighbors and regression trees are methods that can be used to predict numeric outcomes. Both tend to outperform linear regression for very large data sets. Explain one specific advantage that these methods have over linear regression.
You run a regression analysis on a bivariate set of data (n=12n=12). You obtain the regression...
You run a regression analysis on a bivariate set of data (n=12n=12). You obtain the regression equation y=0.631x+48.011y=0.631x+48.011 with a correlation coefficient of r=0.875r=0.875 (which is significant at α=0.01α=0.01). You want to predict what value (on average) for the explanatory variable will give you a value of 180 on the response variable. What is the predicted explanatory value? x = (Report answer accurate to one decimal place.)
Run a regression analysis on the following bivariate set of data with y as the response...
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 48 41.8 39.2 67.4 34.7 68.4 42.9 50.2 49 50.6 45.6 57 58.7 29.7 40.5 68.4 47.4 34.7 45.7 50.9 38.9 47.7 40.9 53.6 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage...
Run a regression analysis on the following bivariate set of data with y as the response...
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 81.1 86 77.5 60.4 92.7 126.5 104 132.5 85 53.1 64.3 95.5 64.6 31.7 39.7 5.7 82.3 121.3 82.4 98.2 29.2 -50.2 34.2 -1 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage...
Run a regression analysis on the following bivariate set of data with y as the response...
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 51.3 17.4 60.8 88.6 37.4 32.9 44.6 53.4 53.7 53.6 52.2 41.5 32.7 28.6 62.4 84.7 47.4 35.7 39.8 33.7 62.1 123 62 83.7 Find the correlation coefficient and report it accurate to three decimal places. r = _______ What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a...
Run a regression analysis on the following bivariate set of data with y as the response...
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 16.1 47.3 10.6 42.8 22.4 50.6 33.3 67.6 47.8 61.1 41.4 62.1 30.2 57.9 30.6 53.6 33 57.4 9.7 41.2 19.3 49.6 Verify that the correlation is significant at an α=0.05. If the correlation is indeed significant, predict what value (on average) for the explanatory variable will give you a value of 54 on the response variable. What is the predicted...
Match the statistics term with its BEST definition. Question 2 options: A key requirement for using...
Match the statistics term with its BEST definition. Question 2 options: A key requirement for using correlation and regression models is to collect this type of data. With bivariate data, the result of MINIMIZING the sum of squared distances between the observed and predicted values (residuals) for a linear model. This quantity is computed by subtracting the observed response variable from the predicted response variable. With bivariate data, when one variable increases a second variable decrease implies this relationship. A...
Run a regression analysis on the following bivariate set of data with y as the response...
Run a regression analysis on the following bivariate set of data with y as the response variable. x y 28.8 90.1 30.6 139.6 12.6 42.2 18.1 94.8 13.9 28.5 31.9 102.4 -3.9 -13.4 16.7 57.1 11 55.8 16.8 76.3 31.7 80.1 43.5 123.8 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage...