# 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

# Create a Linear Regression Table in Python

# Here is how to create a linear regression table in Python:

import pandas as pd

import statsmodels.formula.api as smf

full_health_data = pd.read_csv(“Documents/Data Science/data.csv”, header=0, sep=”,”)

model = smf.ols(‘Calorie_Burnage ~ Average_Pulse’, data = full_health_data)

results = model.fit()

print(results.summary())

OLS Regression Results ============================================================================== Dep. Variable: Calorie_Burnage R-squared: 0.000 Model: OLS Adj. R-squared: -0.006 Method: Least Squares F-statistic: 0.04975 Date: Tue, 20 Jul 2021 Prob (F-statistic): 0.824 Time: 16:38:23 Log-Likelihood: -1145.8 No. Observations: 163 AIC: 2296. Df Residuals: 161 BIC: 2302. Df Model: 1 Covariance Type: nonrobust ================================================================================= coef std err t P>|t| [0.025 0.975] --------------------------------------------------------------------------------- Intercept 346.8662 160.615 2.160 0.032 29.682 664.050 Average_Pulse 0.3296 1.478 0.223 0.824 -2.588 3.247 ============================================================================== Omnibus: 124.542 Durbin-Watson: 1.620 Prob(Omnibus): 0.000 Jarque-Bera (JB): 938.541 Skew: 2.927 Prob(JB): 1.58e-204 Kurtosis: 13.195 Cond. No. 811. ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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.

# Regression Table – Info

The “Information Part” in Regression Table

Dep. Variable: is short for “Dependent Variable”. Calorie_Burnage is here the dependent variable. The Dependent variable is here assumed to be explained by Average_Pulse.

Model: OLS is short for Ordinary Least Squares. This is a type of model that uses the Least Square method.

Date: and Time: shows the date and time the output was calculated in Python.

import pandas as pd

import statsmodels.formula.api as smf

full_health_data = pd.read_csv(“Documents/Data Science/data.csv”, header=0, sep=”,”)

model = smf.ols(‘Calorie_Burnage ~ Average_Pulse’, data = full_health_data)

results = model.fit()

print(results.summary())

OLS Regression Results ============================================================================== Dep. Variable: Calorie_Burnage R-squared: 0.000 Model: OLS Adj. R-squared: -0.006 Method: Least Squares F-statistic: 0.04975 Date: Tue, 20 Jul 2021 Prob (F-statistic): 0.824 Time: 16:43:57 Log-Likelihood: -1145.8 No. Observations: 163 AIC: 2296. Df Residuals: 161 BIC: 2302. Df Model: 1 Covariance Type: nonrobust ================================================================================= coef std err t P>|t| [0.025 0.975] --------------------------------------------------------------------------------- Intercept 346.8662 160.615 2.160 0.032 29.682 664.050 Average_Pulse 0.3296 1.478 0.223 0.824 -2.588 3.247 ============================================================================== Omnibus: 124.542 Durbin-Watson: 1.620 Prob(Omnibus): 0.000 Jarque-Bera (JB): 938.541 Skew: 2.927 Prob(JB): 1.58e-204 Kurtosis: 13.195 Cond. No. 811. ============================================================================== Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

# The Information Part in Regression Table

Dep. Variable: Calorie_Burnage

Model: OLS

Method: Least Squares

Date: Tue, 20 Jul 2021

Time: 16:43:57

No. Observations: 163

# The “Coefficients Part” in Regression Table

## coef

Intercept 346.8662

Average_Pulse 0.3296

Coef is short for coefficient. It is the output of the linear regression function.

The linear regression function can be rewritten mathematically as:

Calorie_Burnage = 0.3296 * Average_Pulse + 346.8662

These numbers means:

If Average_Pulse increases by 1, Calorie_Burnage increases by 0.3296 (or 0,3 rounded)

If Average_Pulse = 0, the Calorie_Burnage is equal to 346.8662 (or 346.9 rounded).

Remember that the intercept is used to adjust the model’s precision of predicting!

Do you think that this is a good model?

# Define the Linear Regression Function in Python

# Define the linear regression function in Python to perform predictions.

# What is Calorie_Burnage if Average_Pulse is: 120, 130, 150, 180?

def Predict_Calorie_Burnage(Average_Pulse):

return(0.3296 * Average_Pulse + 346.8662)

# Try some different values:

print(Predict_Calorie_Burnage(120))

print(Predict_Calorie_Burnage(130))

print(Predict_Calorie_Burnage(150))

print(Predict_Calorie_Burnage(180))

386.4182 389.7142 396.3062 406.1942

# Regression Table: P – Value

The “Statistics of the Coefficients Part” in Regression Table

=================================================================================

## std err t P>|t| [0.025 0.975]

```
160.615 2.160 0.032 29.682 664.050
1.478 0.223 0.824 -2.588 3.247
```

Now, we want to test if the coefficients from the linear regression function has a

significant impact on the dependent variable (Calorie_Burnage).

This means that we want to prove that it exists a relationship

between Average_Pulse and Calorie_Burnage, using statistical tests.

There are four components that explains the statistics of the coefficients:

std err stands for Standard Error

t is the “t-value” of the coefficients

P>|t| is called the “P-value”

[0.025 0.975] represents the confidence interval of the coefficients

We will focus on understanding the “P-value” in this module.

The P-value

The P-value is a statistical number to conclude if there is a relationship between Average_Pulse and Calorie_Burnage.

We test if the true value of the coefficient is equal to zero (no relationship).

The statistical test for this is called Hypothesis testing.

A low P-value (< 0.05) means that the coefficient is likely not to equal zero. A high P-value (> 0.05) means that we cannot conclude that the explanatory variable

affects the dependent variable (here: if Average_Pulse affects Calorie_Burnage).

A high P-value is also called an insignificant P-value.

Hypothesis Testing

Hypothesis testing is a statistical procedure to test if your results are valid.

In our example, we are testing if the true coefficient of Average_Pulse and the intercept is equal to zero.

Hypothesis test has two statements. The null hypothesis and the alternative hypothesis.

The null hypothesis can be shortly written as H0

The alternative hypothesis can be shortly written as HA

Mathematically written:

H0: Average_Pulse = 0

HA: Average_Pulse ≠ 0

H0: Intercept = 0

HA: Intercept ≠ 0

The sign ≠ means “not equal to”

Hypothesis Testing and P-value

The null hypothesis can either be rejected or not.

If we reject the null hypothesis, we conclude that it exist a relationship between Average_Pulse and Calorie_Burnage.

The P-value is used for this conclusion.

A common threshold of the P-value is 0.05.

Note: A P-value of 0.05 means that 5% of the times, we will falsely reject the null hypothesis.

It means that we accept that 5% of the times, we might falsely have concluded a relationship.

If the P-value is lower than 0.05, we can reject the null hypothesis and conclude that

it exist a relationship between the variables.

However, the P-value of Average_Pulse is 0.824. So, we cannot conclude a relationship

between Average_Pulse and Calorie_Burnage.

It means that there is a 82.4% chance that the true coefficient of Average_Pulse is zero.

The intercept is used to adjust the regression function’s ability to predict more precisely.

It is therefore uncommon to interpret the P-value of the intercept.

# R – Squared

R-Squared and Adjusted R-Squared describes how well the linear regression model fits the data points:

R-squared: 0.000

Adj. R-squared: -0.006

The value of R-Squared is always between 0 to 1 (0% to 100%).

A high R-Squared value means that many data points are close to the linear regression function line.

A low R-Squared value means that the linear regression function line does not fit the data well.

Visual Example of a Low R – Squared Value (0.00)

Our regression model shows a R-Squared value of zero,

which means that the linear regression function line does not fit the data well.

This can be visualized when we plot the linear regression

function through the data points of Average_Pulse and Calorie_Burnage.

# Linear Regression Using One Explanatory Variable

# In this example, we will try to predict Calorie_Burnage with Average_Pulse using Linear Regression:

# Three lines to make our compiler able to draw:

import sys

import matplotlib

%matplotlib inline

import pandas as pd

import matplotlib.pyplot as plt

from scipy import stats

full_health_data = pd.read_csv(“Documents/Data Science/data.csv”, header=0, sep=”,”)

x = full_health_data[“Average_Pulse”]

y = full_health_data[“Calorie_Burnage”]

slope, intercept, r, p, std_err = stats.linregress(x, y)

def myfunc(x):

return slope * x + intercept

mymodel = list(map(myfunc, x))

plt.scatter(x, y)

plt.plot(x, mymodel)

plt.ylim(ymin=0, ymax=2000)

plt.xlim(xmin=0, xmax=200)

plt.xlabel(“Average_Pulse”)

plt.ylabel (“Calorie_Burnage”)

plt.show()

Visual Example of a High R – Squared Value (0.79)

However, if we plot Duration and Calorie_Burnage, the R-Squared increases. Here, we see that the data points

are close to the linear regression function line:

# Linear Regression Using One Explanatory Variable

# In this example, we will try to predict Calorie_Burnage with Duration using Linear Regression:

# Three lines to make our compiler able to draw:

import sys

import matplotlib

%matplotlib inline

import pandas as pd

import matplotlib.pyplot as plt

from scipy import stats

full_health_data = pd.read_csv(“Documents/Data Science/data.csv”, header=0, sep=”,”)

x = full_health_data[“Duration”]

y = full_health_data[“Calorie_Burnage”]

slope, intercept, r, p, std_err = stats.linregress(x, y)

def myfunc(x):

return slope * x + intercept

mymodel = list(map(myfunc, x))

plt.scatter(x, y)

plt.plot(x, mymodel)

plt.ylim(ymin=0, ymax=2000)

plt.xlim(xmin=0, xmax=200)

plt.xlabel(“Duration”)

plt.ylabel (“Calorie_Burnage”)

plt.show()

Summary – Predicting Calorie_Burnage with Average_Pulse

How can we summarize the linear regression function with Average_Pulse as explanatory variable?

Coefficient of 0.3296, which means that Average_Pulse has a very small effect on Calorie_Burnage.

High P-value (0.824), which means that we cannot conclude a relationship between Average_Pulse and Calorie_Burnage.

R-Squared value of 0, which means that the linear regression function line does not fit the data well.

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