The least-squares regression equation is y = 758.4x + 12.9 12,935 where y is the median income and is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7500. (a) Predict the median income of a region in which 30% of adults 25 years and older have at least a bachelor's degree.
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.
The least-squares regression equation is y = 758.4x + 12.9 12,935 where y is the
(a) Predict the median income of a region in which 30% of adults 25 years and older have at least a bachelor's degree.
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