In this guide, you’ll see how to perform a linear regression in Python using *statsmodels.*

Here are the topics to be reviewed:

- Background about linear regression
- Review of an example with the full dataset
- Review of the Python code
- Interpretation of the regression results

## About Linear Regression

Linear regression is used as a predictive model that assumes a *linear* relationship between the dependent variable (which is the variable we are trying to predict/estimate) and the independent variable/s (input variable/s used in the prediction).

Under Simple Linear Regression, only *one* independent/input variable is used to predict the dependent variable. It has the following structure:

*Y = C + M*X*

- Y = Dependent variable (output/outcome/prediction/estimation)
- C = Constant (Y-Intercept)
- M = Slope of the regression line (the effect that X has on Y)
- X = Independent variable (input variable used in the prediction of Y)

In reality, a relationship may exist between the dependent variable and *multiple* independent variables. For these types of models (assuming linearity), we can use Multiple Linear Regression with the following structure:

*Y = C + M _{1}*X_{1} + M_{2}*X_{2} + …*

## An Example (with the full dataset)

For illustration purposes, let’s suppose that you have a fictitious economy with the following parameters, where the index_price is the dependent variable, and the 2 independent/input variables are:

- interest_rate
- unemployment_rate

We will use Pandas DataFrame to capture the data in Python:

import pandas as pd data = {'year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016], 'month': [12,11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1], 'interest_rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75], 'unemployment_rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1], 'index_price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719] } df = pd.DataFrame(data) print(df)

Here is the full dataset:

```
year month interest_rate unemployment_rate index_price
0 2017 12 2.75 5.3 1464
1 2017 11 2.50 5.3 1394
2 2017 10 2.50 5.3 1357
3 2017 9 2.50 5.3 1293
4 2017 8 2.50 5.4 1256
5 2017 7 2.50 5.6 1254
6 2017 6 2.50 5.5 1234
7 2017 5 2.25 5.5 1195
8 2017 4 2.25 5.5 1159
9 2017 3 2.25 5.6 1167
10 2017 2 2.00 5.7 1130
11 2017 1 2.00 5.9 1075
12 2016 12 2.00 6.0 1047
13 2016 11 1.75 5.9 965
14 2016 10 1.75 5.8 943
15 2016 9 1.75 6.1 958
16 2016 8 1.75 6.2 971
17 2016 7 1.75 6.1 949
18 2016 6 1.75 6.1 884
19 2016 5 1.75 6.1 866
20 2016 4 1.75 5.9 876
21 2016 3 1.75 6.2 822
22 2016 2 1.75 6.2 704
23 2016 1 1.75 6.1 719
```

## The Python Code using Statsmodels

Now let’s apply the following syntax to perform the linear regression in Python using statsmodels:

import pandas as pd import statsmodels.api as sm data = {'year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016], 'month': [12,11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1], 'interest_rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75], 'unemployment_rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1], 'index_price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719] } df = pd.DataFrame(data) x = df[['interest_rate','unemployment_rate']] y = df['index_price'] x = sm.add_constant(x) model = sm.OLS(y, x).fit() predictions = model.predict(x) print_model = model.summary() print(print_model)

This is the result that you’ll get once you run the code in Python:

```
OLS Regression Results
==============================================================================
Dep. Variable: index_price R-squared: 0.898
Model: OLS Adj. R-squared: 0.888
Method: Least Squares F-statistic: 92.07
Date: Sat, 30 Jul 2022 Prob (F-statistic): 4.04e-11
Time: 13:24:29 Log-Likelihood: -134.61
No. Observations: 24 AIC: 275.2
Df Residuals: 21 BIC: 278.8
Df Model: 2
Covariance Type: nonrobust
=====================================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------------
const 1798.4040 899.248 2.000 0.059 -71.685 3668.493
interest_rate 345.5401 111.367 3.103 0.005 113.940 577.140
unemployment_rate -250.1466 117.950 -2.121 0.046 -495.437 -4.856
==============================================================================
Omnibus: 2.691 Durbin-Watson: 0.530
Prob(Omnibus): 0.260 Jarque-Bera (JB): 1.551
Skew: -0.612 Prob(JB): 0.461
Kurtosis: 3.226 Cond. No. 394.
==============================================================================
```

## Interpreting the Regression Results

Highlighted (in yellow above) several important components within the results:

**Adjusted. R-squared**reflects the fit of the model. R-squared values range from 0 to 1, where a higher value generally indicates a better fit, assuming certain conditions are met.**const coefficient**is your Y-intercept. It means that if both the interest_rate and unemployment_rate coefficients are zero, then the expected output (i.e., the Y) would be equal to the const coefficient.**interest_rate coefficient**represents the change in the output Y due to a change of one unit in the interest rate (everything else held constant)**unemployment_rate coefficient**represents the change in the output Y due to a change of one unit in the unemployment rate (everything else held constant)**std err**reflects the level of accuracy of the coefficients. The lower it is, the higher is the level of accuracy**P >|t|**is your*p-value*. A p-value of less than 0.05 is considered to be statistically significant**Confidence Interval**represents the range in which our coefficients are likely to fall (with a likelihood of 95%)

You may want to check the following tutorial that includes an example of multiple linear regression using both sklearn and statsmodels.

For further information about *statsmodels**, *please refer to the statsmodels documentation.