Question: ```{r starting} library(learnSTATS) final

```{r starting} library(learnSTATS) final <- final_data(idnum = 306315) head(final) ```

	gender	perception	cyberloafing	distance	length
1	female	fair	13.387640	11.510885	5.755707
2	female	fair	13.813303	11.715128	8.306279
3	female	fair	5.671577	8.066906	5.577577
4	female	fair	13.261067	11.702560	15.752091
5	female	fair	8.538383	6.183221	12.665940
6	female	fair	15.590460	9.532643	16.858023

6 rows

# Data Screening

- Assume all data are accurate. - No missing data is present!

## Outliers:

- Calculate your Mahalanobis distance scores across cyberloafing, distance, and length. - Include a summary of your mahal scores that are greater than the cutoff. - How many outliers did you have? - Leave the outliers in the data.

```{r outliers}

```

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