# MRC_shortcuts: Compute Multiple Regression shortcuts with three predictors... In pwr2ppl: Power Analyses for Common Designs (Power to the People)

## Description

Compute Multiple Regression shortcuts with three predictors (will expand to handle two to five) Requires correlations between all variables as sample size. Means and sds are option. Also computes Power(All)

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```MRC_shortcuts( ry1 = NULL, ry2 = NULL, ry3 = NULL, r12 = NULL, r13 = NULL, r23 = NULL, n = 100, alpha = 0.05, my = 0, m1 = 0, m2 = 0, m3 = 0, s1 = 1, s2 = 1, s3 = 1, sy = 1 ) ```

## Arguments

 `ry1` Correlation between DV (y) and first predictor (1) `ry2` Correlation between DV (y) and second predictor (2) `ry3` Correlation between DV (y) and third predictor (3) `r12` Correlation between first (1) and second predictor (2) `r13` Correlation between first (1) and third predictor (3) `r23` Correlation between second (2) and third predictor (3) `n` Sample size `alpha` Type I error (default is .05) `my` Mean of DV (default is 0) `m1` Mean of first predictor (default is 0) `m2` Mean of second predictor (default is 0) `m3` Mean of third predictor (default is 0) `s1` Standard deviation of first predictor (default is 1) `s2` Standard deviation of second predictor (default is 1) `s3` Standard deviation of third predictor (default is 1) `sy` Standard deviation of DV (default is 1)

## Value

Multiple Regression shortcuts with three predictors

## Examples

 ```1 2``` ```MRC_shortcuts(ry1=.40,ry2=.40,ry3=-.40, r12=-.15, r13=-.60,r23=.25, n=110, my=1,m1=1,m2=1,m3=1,sy=7,s1=1,s2=1,s3=2) ```

pwr2ppl documentation built on April 4, 2021, 9:06 a.m.