Linear Regression with Multiple Variables in Python
Linear Regression with Multiple Variables in Python
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Linear Regression is a popular statistical technique used for understanding the relationship between a dependent variable and one or more independent variables. In this post, we'll explore how to perform multiple linear regression using Python.
First, let's import necessary libraries: NumPy for numerical operations and pandas for data manipulation. Later, we will generate some random data and create a DataFrame. We'll use Statistics' scikit-learn library for our regression analysis.
```python
import numpy as np
import pandas as pd
from sklearn.stats import linregress
```
We'll create an example dataset:
```python
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15]])
Y = np.array([1, 2, 3, 4, 5])
```
To create a DataFrame:
```python
df = pd.DataFrame(np.hstack((X.T, Y.T)).T)
df.columns = ["X1", "X2", "X3", "Y"]
```
Now, let's perform multiple linear regression on this dataset:
```python
slope, intercept, r_value, p_value, std_err = linregress(X.flatten(), Y)
print(f'Slope: {slope}')
print(f'Intercept: {intercept}')
print(f'R-squared: {r_value2}')
```
This simple example shows the basics of multiple linear regression using Python. This analysis can be extended to more complex datasets and real-world scenarios, opening up endless possibilities for statistical modeling.
For further study, consider exploring advanced features in pandas and NumPy or reading about multiple regression formula and hypothesis testing in statistics.
Additional Resources:
[Linear Regression with Multiple Independent Variables in Python using sklearn](https://scikit-learn.org/stable/auto_...
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