# Foundations for statistical inference - Confidence intervals¶

## Sampling from Ames, Iowa¶

If you have access to data on an entire population, say the size of every house in Ames, Iowa, it's straight forward to answer questions like, "How big is the typical house in Ames?" and "How much variation is there in sizes of houses?". If you have access to only a sample of the population, as is often the case, the task becomes more complicated. What is your best guess for the typical size if you only know the sizes of several dozen houses? This sort of situation requires that you use your sample to make inference on what your population looks like.

In [ ]:

```
# for Mac OS users only!
# if you run into any SSL certification issues,
# you may need to run the following command for a Mac OS installation.
# $/Applications/Python 3.x/Install Certificates.command
import os, ssl
if (not os.environ.get('PYTHONHTTPSVERIFY', '') and
getattr(ssl, '_create_unverified_context', None)):
ssl._create_default_https_context = ssl._create_unverified_context
```

Since we will take some random samples and calculate confidence based on these samples, it is a good idea to set a seed value. This way we ensure generating same random numbers when executing the same codes on different machines (for a specific seed value). We can specify any seed value but we must use the same value to reproduce the same results.

In [1]:

```
import numpy as np
np.random.seed(999)
```

## The data¶

In the previous tutorial, "Sampling Distributions", we looked at the population data of houses from Ames, Iowa. Let's start by loading that data set.

In [2]:

```
import pandas as pd
ames = pd.read_csv('https://www.openintro.org/stat/data/ames.csv')
```

Here we'll start with a simple random sample of size 60 from the population. Specifically, this is a simple random sample of size 60. Note that the data set has information on many housing variables, but for the first portion of the tutorial we'll focus on the size of the house, represented by the variable

`Gr.Liv.Area`

.
In [3]:

```
population = ames['Gr.Liv.Area']
sample60 = population.sample(60)
```

#### Exercise 1

Describe the distribution of your sample. What would you say is the "typical" size within your sample? Also state precisely what you interpreted "typical" to mean.#### Exercise 2

Would you expect another student's distribution to be identical to yours? Would you expect it to be similar? Why or why not?## Confidence intervals¶

One of the most common ways to describe the typical or central value of a distribution is to use the mean. In this case we can calculate the mean of the sample using,

In [4]:

```
sample_mean = sample60.mean()
```

Return for a moment to the question that first motivated this tutorial: based on this sample, what can we infer about the population? Based only on this single sample, the best estimate of the average living area of houses sold in Ames would be the sample mean, usually denoted as $\bar{X}$ (here we're calling it

`sample_mean`

). That serves as a good *point estimate*but it would be useful to also communicate how uncertain we are of that estimate. This can be captured by using a*confidence interval*. We can calculate a 95% confidence interval for a sample mean by adding and subtracting 1.96 standard errors to the point estimate (See Section 5.2 if you are unfamiliar with this formula).In [5]:

```
import numpy as np
se = np.std(sample60)/np.sqrt(60)
lower = sample_mean - (1.96 * se)
upper = sample_mean + (1.96 * se)
print(lower, upper)
```

1395.234137053052 1630.5991962802814

This is an important inference that we've just made: even though we don't know what the full population looks like, we're 95% confident that the true average size of houses in Ames lies between the values lower and upper. There are a few conditions that must be met for this interval to be valid.

#### Exercise 3

For the confidence interval to be valid, the sample mean must be normally distributed and have standard error s/√n . What conditions must be met for this to be true?## Confidence levels¶

#### Exercise 4

What does "95% confidence" mean?
In this case we have the luxury of knowing the true population mean since we have data on the entire population. This value can be calculated using the following command:

In [6]:

```
population.mean()
```

Out[6]:

1499.6904436860068

#### Exercise 5

Does your confidence interval capture the true average size of houses in Ames? If you are working on this lab in a classroom, does your neighborâ€™s interval capture this value?#### Exercise 6

Each student in your class should have gotten a slightly different confidence interval. What proportion of those intervals would you expect to capture the true population mean? Why? If you are working in this lab in a classroom, collect data on the intervals created by other students in the class and calculate the proportion of intervals that capture the true population mean.
Using Python, we're going to recreate many samples to learn more about how sample means and confidence intervals vary from one sample to another.
Here is the rough outline:

- Obtain a random sample.
- Calculate and store the sample's mean and standard deviation.
- Repeat steps (1) and (2), 50 times.
- Use these stored statistics to calculate many confidence intervals.

In [7]:

```
sample_mean = np.zeros(50)
sample_sd = np.zeros(50)
n = 60
```

Now we're ready for the loop where we calculate the means and standard deviations of 50 random samples.

In [8]:

```
np.random.seed(999)
for i in range(50):
sample60 = population.sample(n) # obtain a sample of size n = 60 from the population
sample_mean[i] = sample60.mean() # save sample mean in ith element of sample_mean
sample_sd[i] = np.std(sample60) # save sample sd in ith element of sample_sd
```

Lastly, we construct the confidence intervals.

In [9]:

```
se_array = sample_sd/np.sqrt(n)
lower_bounds = sample_mean - (1.96 * se_array)
upper_bounds = sample_mean + (1.96 * se_array)
```

Lower bounds of these 50 confidence intervals are stored in

`lower_bounds`

, and the upper bounds are in `upper_bounds`

. Let's view the first interval.
In [10]:

```
print(lower_bounds[10], upper_bounds[10])
```

1373.2983863329387 1626.168280333728

## On Your Own¶

- Plot all the confidence intervals (
`lower_bounds`

and`upper_bounds`

). What proportion of your confidence intervals include the true population mean? Is this proportion exactly equal to the confidence level? If not, explain why. - Pick a confidence level of your choosing, provided it is not 95%. What is the appropriate critical value?
- Calculate 50 confidence intervals at the confidence level you chose in the previous question. You do not need to obtain new samples, simply calculate new intervals based on the sample means and standard deviations you have already collected. Plot all intervals and calculate the proportion of intervals that include the true population mean. How does this percentage compare to the confidence level selected for the intervals?

This lab was adapted by Vural Aksakalli and Imran Ture from OpenIntro by Andrew Bray and Mine Ă‡etinkaya-Rundel.

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