# Some key concepts explained

By Afshine Amidi and Shervine Amidi

## Use a distribution table to compute a probability

Let $X\sim\mathcal{N}(\mu,\sigma)$ with $\mu, \sigma$ known and $a,b\in\mathbb{R}$.

❐ **Question:** Compute $P(a\leqslant X\leqslant b)$.

❐ **Step 1 ― Standardize $X$**

We introduce $Z$, such that

❐ **Step 2 ― Express the probability in terms of $Z$**

We have:

❐ **Step 3 ― Find each term using the distribution table**

Given that the values of $\frac{a-\mu}{\sigma}$ and $\frac{b-\mu}{\sigma}$ are known, we just have to look them up in a distribution table similar to this one.

Summing up We just computed the value of the probability by standardizing the normal variable to be able to look up the values in a standard normal distribution table.

## Confidence intervals

### Compute the confidence interval for $\mu$

__Note__: the example below is specific to the case where the variance is known and $n$ is large. The following reasoning can be reproduced for other cases in a similar fashion.

Let $X_1, ..., X_n$ be a random sample with mean $\mu$ and standard deviation $\sigma$ where **only** $\sigma$ is **known**, and let $\alpha\in[0,1]$.

❐ **Question:** Compute a confidence interval on $\mu$ with confidence level $1-\alpha$, that we note $CI_{1-\alpha}$.

❐ **Step 1 ― Write in mathematical terms what we are seaching for**

We want to find a confidence interval $CI_{1-\alpha}$ of confidence level $1-\alpha$ for $\mu$:

❐ **Step 2 ― Consider the sample mean of $X$**

We consider $\overline{X}$, which is such that:

❐ **Step 3 ― Standardize $\overline{X}$**

We introduce $Z$, such that:

In general, this relationship is valid for large $n$ but it is always true in the particular case when the $X_i$ are normal.

❐ **Step 4 ― Use $Z$ to find the quantiles**

We can find the quantiles of $Z$ which are such that:

Given that $Z$ follows a standard normal distribution, the quantity $z_{\frac{\alpha}{2}}$ can be found in the distribution table.

❐ **Step 5 ― Re-write $Z$ in terms of $\overline{X}$**

Knowing that $Z=\frac{\overline{X}-\mu}{\frac{\sigma}{\sqrt{n}}}$, we can re-write the previous expression:

❐ **Step 6 ― Deduce the confidence interval**

By taking into account steps 1 and 5, we can now deduce the confidence interval for $\mu$:

### Compute the confidence interval for $\sigma^2$

Let $X_1, ..., X_n$ be a random sample with mean $\mu$ and standard deviation $\sigma$ where $\sigma$ is **unknown**, and let $\alpha\in[0,1]$.

❐ **Question:** Compute a confidence interval on $\sigma^2$ with confidence level $1-\alpha$, that we note $CI_{1-\alpha}$.

❐ **Step 1 ― Write in mathematical terms what we are seaching for**

We want to find a confidence interval $CI_{1-\alpha}$ of confidence level $1-\alpha$ for $\sigma^2$:

❐ **Step 2 ― Consider the sample variance of $X$**

We consider $s^2$, which is such that:

❐ **Step 3 ― Standardize $s^2$**

We introduce $K$, such that:

Here, $K$ follows a $\chi^2$ distribution with $n-1$ degrees of freedom.

❐ **Step 4 ― Use $K$ to find the quantiles**

We can find the quantiles $\chi_1^2, \chi_2^2$ of $K$ which are such that:

Given that $K$ follows a $\chi^2$ distribution with $n-1$ degrees of freedom, the quantiles can be found in the distribution table.

❐ **Step 5 ― Re-write $K$ in terms of $s^2$**

Knowing that $K=\frac{s^2(n-1)}{\sigma^2}$, we can re-write the previous expression:

❐ **Step 6 ― Deduce the confidence interval**

By taking into account steps 1 and 5, we can now deduce the confidence interval for $\sigma^2$: