Suppose that the regression equation y = 16.99 + 0.32 x1 + 0.41 x2 + 5.31 x3 predicts an adult’s height (y) given the individual’s mother’s height (x1), his or her father’s height (x2), and whether the individual is male (x3 = 1) or female (x3 = 0). All heights are measured in inches. In this equation, the coefficient of ______ means that ______.     x1; if two individuals have mothers whose heights differ by 0.5 inch, then the individuals’ heights will differ by 0.32 inch. x2; if two individuals have mothers whose heights differ by 1 inch, then the individuals’ heights will differ by 0.41 inches. x2; if two individuals have fathers whose heights differ by 1 inch, then the individuals’ heights will differ by 0.41 inches. x1; if two individuals have mothers whose heights differ by 0.32 inch, then the individuals’ heights will differ by 1 inch. x3; a brother is expected to be 5.31 inches taller than his sister

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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.