Pre-study scores versus post-study scores for a class of 120 college freshman English students were considered. The residual plot for the least squares regression line showed no pattern. The least squares regression line was \hat{y} = 0.2 + 0.9xy^=0.2+0.9x with a correlation coefficient r = 0.76. What percent of the variation of post-study scores can be explained by the variation in pre-study scores? 57.8% 87.2% 52.0% 76.0% We cannot determine the answer using the information given.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Pre-study scores versus post-study scores for a class of 120 college freshman English students were considered. The residual plot for the least squares regression line showed no pattern. The least squares regression line was \hat{y} = 0.2 + 0.9xy^=0.2+0.9x with a
57.8%
87.2%
52.0%
76.0%
We cannot determine the answer using the information given.
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