The following model was fitted to a sample of 30 families in order to explain household milk consumption:y = b0 + b1x1 + b2x2 + ε wherey = milk consumption, in quarts per weekx1 = weekly income, in hundreds of dollarsx2 = family sizeThe least squares estimates of the regression parameters were as follows:b0 = -0.025 b1 = 0.052 b2 = 1.14Predict the weekly milk consumption of a family of four with an income of $600 per week.
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 following model was fitted to a sample of 30 families in order to explain household milk consumption:
y = b0 + b1x1 + b2x2 + ε
where
y = milk consumption, in quarts per week
x1 = weekly income, in hundreds of dollars
x2 = family size
The least squares estimates of the regression parameters were as follows:
b0 = -0.025 b1 = 0.052 b2 = 1.14
Predict the weekly milk consumption of a family of four with an income of $600 per week.
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