how to do the linear regression in python??

 

📊 **Unlocking the Power of Linear Regression with Python's SciPy Library!** 📈

Hey there, data enthusiasts! Today, we're diving into the world of linear regression using Python's powerful SciPy library. Strap in as we explore how to perform linear regression, calculate the coefficient of determination (R-squared), and unleash the potential of your data with just a few lines of code!

### 📊 What is Linear Regression?

Linear regression is a fundamental statistical technique used to model the relationship between two variables. It's like fitting a straight line to a scatter plot of data points, allowing us to make predictions and understand the underlying relationship between the variables.

### 💻 Let's Get Coding!

First things first, fire up your Python environment and make sure you have SciPy installed. If not, a quick `pip install scipy` should do the trick. Once that's done, import the necessary libraries:

```python

from scipy.stats import linregress

```

Now, assuming you have two arrays, `x` and `y`, representing your independent and dependent variables respectively, let's perform linear regression:

```python

x = [1, 2, 3, 4, 5]

y = [2, 3, 4, 5, 6]

slope, intercept, r_value, p_value, std_err = linregress(x, y)

```

Boom! 🎉 You've just calculated the slope, intercept, and other statistical goodies for your linear regression model. But wait, there's more! We also want to know how well our model fits the data, right? Enter the R-squared value!

### 📈 Calculating R-squared

R-squared, also known as the coefficient of determination, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). Here's how you can calculate it:

```python

r_squared = r_value**2

print("R-squared:", r_squared)

```

note the formula for rsquared:

def CORREL(YEXP,YPRED_sc):
  NEXP=YEXP.shape[0]

  NUM=0.
  DEN1=0.
  DEN2=0.
  NYT = 0

  Y_EXP_AVE = np.average(YEXP)
  Y_CALC_AVE = np.average(YPRED_sc)

  for i in range(NEXP):
      NUM+=(YEXP[i]-Y_EXP_AVE)*(YPRED_sc[i]-Y_CALC_AVE)
      DEN1+=(YEXP[i]-Y_EXP_AVE)**2
      DEN2+=(YPRED_sc[i]-Y_CALC_AVE)**2

  CORREL = NUM/np.sqrt(DEN1*DEN2)
  return CORREL

from scipy.stats import linregress
def calculate_r_squared(x, y):
  slope, intercept, r_value, p_value, std_err = linregress(x, y)
  return r_value ** 2

### 🚀 Putting It All Together

```python

print("Slope:", slope)

print("Intercept:", intercept)

print("R-squared:", r_squared)

```

And there you have it! With just a few lines of code, you've performed linear regression and calculated the R-squared value using Python's SciPy library. Now go forth and conquer your data with the power of regression analysis! 🔍📊

Happy coding! 💻✨

interactive example:

https://colab.research.google.com/drive/1wy7S-TF7ShrPAgHEyu9BnL8echNhIcZJ?usp=sharing