## Implement Jackknife Using Parallel Computing

This example is from the `jackknife` function reference page, but runs in parallel.

Generate a sample data of size 10000 from a normal distribution with mean 0 and standard deviation 5.

```sigma = 5; rng('default') y = normrnd(0,sigma,10000,1);```

Run `jackknife` in parallel to estimate the variance. To do this, use `statset ` to create the options structure and set the `UseParallel` field to true.

```opts = statset('UseParallel',true); m = jackknife(@var,y,1,'Options',opts);```

Compare the known bias formula with the jackknife bias estimate.

```n = length(y); bias = -sigma^2/n % Known bias formula jbias = (n-1)*(mean(m)-var(y,1)) % jackknife bias estimate```
```Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 6). bias = -0.0025 jbias = -0.0025```

Compare how long it takes to compute in serial and in parallel.

`tic;m = jackknife(@var,y,1);toc % Serial computation`
```Elapsed time is 1.638026 seconds. ```
`tic;m = jackknife(@var,y,1,'Options',opts);toc % Parallel computation`
`Elapsed time is 0.507961 seconds.`

`jackknife` does not use random numbers, so gives the same results every time, whether run in parallel or serial.