# corr: Compute power for Pearson's Correlation Takes correlation and... In pwr2ppl: Power Analyses for Common Designs (Power to the People)

## Description

Compute power for Pearson's Correlation Takes correlation and range of values

## Usage

 `1` ```corr(r, nlow, nhigh, alpha = 0.05, tails = 2, by = 1) ```

## Arguments

 `r` Correlation `nlow` Starting sample size `nhigh` Ending sample size `alpha` Type I error (default is .05) `tails` one or two-tailed tests (default is 2) `by` Incremental increase in sample size from low to high

## Value

Power for Pearson's Correlation

## Examples

 `1` ```corr(r=.30, nlow=60, nhigh=100,by=2) ```

### Example output

```Power for n of 60 = 0.6537
Power for n of 62 = 0.6689
Power for n of 64 = 0.6836
Power for n of 66 = 0.6978
Power for n of 68 = 0.7114
Power for n of 70 = 0.7246
Power for n of 72 = 0.7373
Power for n of 74 = 0.7495
Power for n of 76 = 0.7612
Power for n of 78 = 0.7724
Power for n of 80 = 0.7832
Power for n of 82 = 0.7936
Power for n of 84 = 0.8035
Power for n of 86 = 0.8131
Power for n of 88 = 0.8222
Power for n of 90 = 0.8309
Power for n of 92 = 0.8393
Power for n of 94 = 0.8473
Power for n of 96 = 0.8549
Power for n of 98 = 0.8622
Power for n of 100 = 0.8692
n  Power
1   60 0.6537
2   62 0.6689
3   64 0.6836
4   66 0.6978
5   68 0.7114
6   70 0.7246
7   72 0.7373
8   74 0.7495
9   76 0.7612
10  78 0.7724
11  80 0.7832
12  82 0.7936
13  84 0.8035
14  86 0.8131
15  88 0.8222
16  90 0.8309
17  92 0.8393
18  94 0.8473
19  96 0.8549
20  98 0.8622
21 100 0.8692
```

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