# Lab 2H - Eyeballing Normal

## Lab 2H - Eyeballing Normal

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red in your journal.

### What's normal?

• The normal distribution is a curve we often see in real data.

– We see it in people's blood pressures and in measurement errors.

• When data appears to be normally distributed, we can use the normal model to:

– Simulate normally distributed data.

– Easily compute probabilities.

• In this lab, we'll look at some previous data sets to see if we can find data that are roughly normally distributed.

### The normal distribution

• The normal distribution is symmetric about the mean:

– The `mean` is found in the very center of the distribution.

– And the curve looks the same to the left of the mean as it does on the right.

• Use the following to draw a normal distribution:

``````plotDist('norm', mean = 0, sd = 1)
``````

### The mean and sd of it

• To draw a normal curve, we need to know exactly 2 things:

– The `mean` and `sd`.

• The `sd`, or standard deviation, is a measure of spread that's similar to the `MAD`.

• Which part of the normal curve changes when the value of the `mean` changes?

• Which part of the normal curve changes when the value of the `sd` changes?

• Hint: Try changing the `mean` and `sd` values in the `plotDist` function.

### Finding normal distributions

• Load the `cdc` data and use the `histogram` function to answer the following:

• Think about the `height` and `weight` variables. Based on what you know about these variables, which of the variables do you think have distributions that will look like the normal distribution?

Make histograms of these variables. Which ones look like the normal distribution?

Hint: To help answer this question, try including the option `fit = "normal"` in the histogram function. You might also try faceting by `gender`.

### Using normal models

• Data scientists like using normal models because it often resembles real data.

But not EVERYTHING is normally distributed.

• As a data scientist in training, you must decide when a normal model seems appropriate.

– No model is ever perfect 100% of the time.

– If you choose a model, you should be able to justify why you chose it.

The difference in `percentages` between male and female survivors in a slasher film for 500 random shuffles.
The difference in `median` fares between survivors and non-survivors on the Titanic for 500 random shuffles.
The difference in `mean` fares between survivors and non-survivors on the Titanic for 500 random shuffles.