# Linear Regression Calculator

## Multiple Variables

Uses an unlimited number of variables.

Power regression - Ln transformation (natural log) over all the variables: Y=exp(b0)⋅X1b1⋅⋅Xpbp .
Enter raw data directly
Enter raw data from excel
 *Include ✔✘ ✔✘ Transform LogLnSqrt^2 LogLnSqrt^2 LogLnSqrt^2 Groups X1 X2 Y Data pval: avg: n: S: Skewness: Normality:** Outliers:
 Groups Pred Y Residual Data pval: avg: n: S: Skewness Normality Outliers

Header: You may change the groups' names to their real names.
Data: When entering data, press Enter or Comma , or Space after each value.
* All variables will be included in the automatic iterations mode.
** Normality colors based on α=0.05

You may copy data from Excel, Google sheets or any tool that separate data with Tab and Line Feed.
Copy the data, one block of consecutive columns includes the header, and paste below. Y must be the right column (more) . click to see example: Correlation matrix
ANOVA table
anova
tresults2
validation message

## Information

The calculator uses variables transformations, calculates the Linear equation, R, p-value, outliers and the adjusted Fisher-Pearson coefficient of skewness.
After checking the residuals' normality, multicollinearity, homoscedasticity and priori power, the program interprets the results.
Then, it draws a histogram, a residuals QQ-plot, a correlation matrix, a residuals x-plot and a distribution chart.
You may transform the variables, exclude any predictor or run backward stepwise selection automatically based on the predictor's p-value.

Right-tailed F test. Checks if the entire regression model is statistically significant. Why?
Hypotheses
H0: Y = b0
H1: Y=b0+b1X1+...+bpXp
Test statistic F distribution ## R Code

The following R code should produce the same results: