800 600 400 200 1.25 2.50 3.75 5.00 Size (1000 ft3) Dependent variable is: Price R squared = 59.5% s = 53.79 with 1064 – 2 = 1062 degrees of freedom Variable Coefficient SE(Coeff) t-ratio P-value Intercept -3.11686 4.688 -0.665 0.5063 Size 94.4539 2.393 39.5 s0.0001 300 150 -150 125 250 375 500 Predicted 400 300 200 100 -300 -50 200 Residuals ($1000s) Residuals # of Houses Price ($1000's)
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.
domly selected houses that had been sold include data
b) The intercept is negative. Discuss its value, taking note
of its P-value.
c) The output reports s = 53.79. Explain what that
means in this context.
d) What’s the value of the standard error of the slope of
the regression line?
e) Explain what that means in this context.
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