## Code Challenges

- FizzBuzz
- Mean of a List
- Median of a List
- Variance of a List
- Standard Deviation of a List
- Sample Covariance
- Correlation Coefficient
- Simple Linear Regression
- Vector Dot Product
- Random Matrix

- Save
- Run All Cells
- Clear All Output
- Runtime
- Download
- Difficulty Rating

## Loading Runtime

The following code cell loads two lists of data:

`experience`

: A float value representing each employee's current years of experience rounded to one decimal place.`salary`

: An integer value representing each employee's current salary in dollars.

There are a total of 30 observations. (This is fake data.)

**Make sure to run this code cell –in order to load the data into the notebook's memory– before continuing.**

We will specify the Simple Linear Regression equation for the line of best fit as follows:

ŷ = β_{0} + β_{1}x + ε

Where:

- ŷ represents the model's predicted
`salary`

values - β
_{0}is the model's intercept coefficient - β
_{1}is the model's slope coefficient - x represents the independent variable
`experience`

- ε is the error term

### The Challenge:

Write a function called `simple_regression`

that takes in a list of `experience`

values and a list of `salary`

values as arguments.

Calculate the intercept and slope coefficients of the line of best fit (β_{0} and β_{1}), and return these two coefficients in this specific order within a list: [β_{0}, β_{1}]

### Example:

This example returns the coefficients of a regression line fit to the first five observations (rows) of the provided dataframe.

`simple_regression([1.2, 1.4, 1.6, 2.1, 2.3], [39344, 46206, 37732, 43526, 39892])`

Returns: `[41529.041474654376, -109.90783410138302]`

**In order to test your code, five observations (rows) will be selected at random from the dataframe and will their corresponding variables will be passed to your function as lists.**