本帖最后由 qzhou3 于 2014-10-7 00:55 编辑
回复 1# puccafly
我用英文解释一遍
What you have is a linear regression. For linear regression, the R2 ranges from 0 to 1. If it is 0, the two variables, X and Y, are not associated. If R2 is 1, the two variables are perfectly associated. In real life, variables are rarely perfectly associated. Thus R2 is rarely 1.
The lower case r, called correlation coefficient, is the correlation between two variables. It ranges from -1 to +1, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation. The upper case R is the same as r if there is only one independent variable (predictor).
R2 is called coefficient of determination. It tells how much variance in Y can be explained by X. In your case, how much variance in living space is explained by income.
In your example, you use X, the income, to predict Y, the living space in square meters. Your model can predict 97.67% of the variance in living space. This is a super strong association, almost too good to be true...
When you use excel or statistical software to perform regression analysis, the calculation is based on least squares method. That is, the sum of squares of the distance from your data points to the regression line is the smallest. Hence your points are rarely sitting on the line; when a point is on the line, the error for that point is 0.
Your orange line is not a formula. It is just a connection of the points. |
发表于 2014-10-7 00:45
|