Flash cards
Review the key moves
1/4
Core idea
What is the main idea behind Data Science - Regression Table?
Lesson checks
Practice each idea before moving on
Short Mimo-style checks built from this lesson's code, terms, and sequence.
1Quick choice
Which statement best captures the main point of this lesson?
2Fill blank
Complete the missing token from the example code.
___ pandas as pd3Order
Put the learning moves in the order that makes the concept easiest to apply.
The output from linear regression can be summarized in a regression table.
Create a Linear Regression Table in Python
Regression Table with Average_Pulse as Explanatory Variable
4Data move
Before charting or modeling a dataset, which move should come first?
Regression Table
The output from linear regression can be summarized in a regression table.
The content of the table includes
- Information about the model
- Coefficients of the linear regression function
- Regression statistics
- Statistics of the coefficients from the linear regression function
- Other information that we will not cover in this module
Regression Table with Average_Pulse as Explanatory Variable
You can now begin your journey on analyzing advanced output!
Create a Linear Regression Table in Python
Here is how to create a linear regression table in Python:
Example
import pandas as pd
import statsmodels.formula.api as smf
full_health_data = pd.read_csv("data.csv", header=0, sep=",")
model = smf.ols('Calorie_Burnage ~ Average_Pulse', data =
full_health_data)
results = model.fit()
print(results.summary())Example Explained
- Import the library statsmodels.formula.api as smf. Statsmodels is a statistical library in Python.
- Use the full_health_data set.
- Create a model based on Ordinary Least Squares with smf.ols(). Notice that the explanatory variable must be written first in the parenthesis. Use the full_health_data data set.
- By calling .fit(), you obtain the variable results. This holds a lot of information about the regression model.
- Call summary() to get the table with the results of linear regression.