There are a number of Python libraries that can be used to fit a linear regression, but in this course, we will use the `OLS.from_formula()`

function from `statsmodels.api`

because it uses simple syntax and provides comprehensive model summaries.

Suppose we have a dataset named `body_measurements`

with columns `height`

and `weight`

. If we want to fit a model that can predict weight based on height, we can create the model as follows:

model = sm.OLS.from_formula('weight ~ height', data = body_measurements)

We used the formula `'weight ~ height'`

because we want to predict `weight`

(it is the outcome variable) using `height`

as a predictor. Then, we can fit the model using `.fit()`

:

results = model.fit()

Finally, we can inspect a summary of the results using `print(results.summary())`

. For now, we’ll only look at the coefficients using `results.params`

, but the full summary table is useful because it contains other important diagnostic information.

print(results.params)

Output:

Intercept -21.67 height 0.50 dtype: float64

This tells us that the best-fit intercept is `-21.67`

, and the best-fit slope is `0.50`

.

### Instructions

**1.**

Using the `students`

dataset that has been loaded in **script.py**, create a linear regression model that predicts student `score`

using `hours_studied`

as a predictor and save the result as a variable named `model`

.

**2.**

Fit the model using the `.fit()`

method on `model`

(created in the previous step), and save the fitted model as `results`

.

**3.**

Print out the model coefficients using either `.params`

.