## Function Linear-Regression-Verbose-Summaries

(

**linear-regression-verbose-summaries** < n > < x > < y > < x2 > < y2 > < xy > )

Calculates almost every statistic of a linear regression: the slope and

intercept of the line, the standard error on each, the correlation coefficient,

the coefficient of determination, also known as r-square, and an ANOVA table as

described in the manual.

If you don't need all this information, consider using the ` `

-brief'' or

` `

-minimal'' functions, which do less computation.

This function differs from ` linear-regression-verbose' in that it takes summary<br> variables: `

x' and ` y' are the sums of the independent variable and dependent<br> variables, respectively; `

x2' and ` y2' are the sums of the squares of the<br> independent variable and dependent variables, respectively; and `

xy' is the sum

of the products of the independent and dependent variables.

You should first look at your data with a scatter plot to see if a linear model

is plausible. See the manual for a fuller explanation of linear regression

statistics.