Linear regression basics and implementation in Python
What is Linear Regression (LR)?
 Linear regression (LR) models the linear relationship between the independent (
X
) variable with that of the dependent variable (y
).  For example, how the likelihood of blood pressure is influenced by a person’s age and weight. This relationship can be explained using linear regression.
 In LR, the
y
variable should be continuous, whereas theX
variable can be continuous or categorical. If bothX
andy
are continuous, the linear relationship can be estimated using correlation coefficient (r) or the coefficient of determination (RSquared)  LR is useful if the relationships between the
X
andy
variables are linear  LR is helpful to predict the value of
y
based on the value of theX
variable
Note: Dependent variable also called a response, outcome, regressand, criterion, or endogenous variable. Independent variable also called explanatory, covariates, predictor, regressor, or exogenous variable.
Types of Linear Regression (LR)?
 Univariate LR: Linear relationships between
y
andX
variables can be explained by a singleX
variable
\( y = a + bX + \epsilon \)
Where, a = yintercept, b = slope of the regression line (unbiased estimate) and \( \epsilon \) = error term (residuals)

Multiple LR: Linear relationships between
y
andX
variables can be explained by multipleX
variables\( y = a + b_1X_1 + b_2X_2 + b_3X_3 + ... + b_nX_n + \epsilon \)
Where, a = yintercept, b = slope of the regression line (unbiased estimate) and \( \epsilon \) = error term (residuals)  The yintercept (a) is a constant and slope (b) of the regression line is a regression coefficient.
 How to perform multiple linear regression
Linear Regression (LR) Assumptions
 The relationship between the
X
andy
variables should be linear  Errors (residuals) should be independent of each other
 Errors (residuals) should be normally distributed with a mean of 0
 Errors (residuals) should have equal variance (Homoscedasticity)
Linear Regression (LR) Outputs
Correlation coefficient (r)
 Correlation coefficient (r) describes a linear relationship between
X
andy
variables. r can range from 1 to 1.  r > 0 indicates a positive linear relationship between
X
andy
variables. As one of the variable increases, the other variable also increases. r = 1 is a perfect positive linear relationship  Similarly, r < 0 indicates a negative linear relationship between
X
andy
variables. As one of the variable increases, the other variable decreases, and vice versa. r = 1 is perfect negative linear relationship  r = 0 indicates, there is no linear relationship between the
X
andy
variables
Coefficient of determination (RSquared or rSquared)
 RSquared (R^{2}) is a square of correlation coefficient (r) and usually represented as percentages.
 RSquared explains the variation in the
y
variable that is explained by independent variables in the fitted regression.  Multiple correlation coefficient (R), which is the square root of the RSquared, is used to assess the prediction
quality of the
y
variable in multiple regression analysis. Its value range from 0 to 1.  RSquared can range from 0 to 1 (0 to 100%). Rsquared = 1 (100%) indicates that the fitted regression line explains all the variability of Y variable around its mean.
Residuals (regression error)
 Residuals or error in regression represents the distance of the observed data points from the predicted regression line
\( residuals = actual \ y (y_i)  predicted \ y \ (\hat{y}_i) \)
Root Mean Square Error (RMSE)
 RMSE represents the standard deviation of the residuals. It gives an estimate of the spread of observed data points across the predicted regression line.
Linear Regression (LR) in Python
 For performing the LR, we will use the plant species richness data to study the influence of
island area on the native plant richness of islands. The data is collected from 22 different coastal islands (McMaster 2005).  The dataset contains native plant richness (
ntv_rich
) as a dependent variable (y
) and island area as the independent variable (X
).  We will use statsmodels and
bioinfokit v1.0.8
or later for performing LR in Python (check how to install Python packages)
Note: If you have your own dataset, you should import it as a pandas dataframe. Learn how to import data using pandas
Let’s perform Linear Regression in Python
import statsmodels.api as sm
from bioinfokit.analys import stat, get_data
import numpy as np
import pandas as pd
df = get_data('plant_richness_lr').data
df.head(2)
ntv_rich area
0 1.897627 1.602060
1 1.633468 0.477121
X = df['area'] # independent variable
y = df['ntv_rich'] # dependent variable
# to get intercept  this is optional
X = sm.add_constant(X)
# fit the regression model
reg = sm.OLS(y, X).fit()
reg.summary()
OLS Regression Results
==============================================================================
Dep. Variable: ntv_rich Rsquared: 0.828
Model: OLS Adj. Rsquared: 0.819
Method: Least Squares Fstatistic: 96.13
Date: Sat, 13 Feb 2021 Prob (Fstatistic): 4.40e09
Time: 19:56:31 LogLikelihood: 4.0471
No. Observations: 22 AIC: 4.094
Df Residuals: 20 BIC: 1.912
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>t [0.025 0.975]

const 1.3360 0.096 13.869 0.000 1.135 1.537
area 0.3557 0.036 9.805 0.000 0.280 0.431
==============================================================================
Omnibus: 0.057 DurbinWatson: 1.542
Prob(Omnibus): 0.972 JarqueBera (JB): 0.278
Skew: 0.033 Prob(JB): 0.870
Kurtosis: 2.453 Cond. No. 6.33
==============================================================================
# regression metrics
res= stat()
res.reg_metric(y=np.array(y), yhat=np.array(reg.predict(X)), resid=np.array(reg.resid))
res.reg_metric_df
Metrics Value
0 Root Mean Square Error (RMSE) 0.2013
1 Mean Squared Error (MSE) 0.0405
2 Mean Absolute Error (MAE) 0.1554
3 Mean Absolute Percentage Error (MAPE) 0.0854
Learn how to train linear regression model using neural networks (PyTorch)
Linear Regression (LR) interpretation
Regression line
 The regression line with
equation [
y = 1.3360 + (0.3557*area)
], is helpful to predict the value of the native plant richness (ntv_rich
) from the given value of the island area (area
).  Regression can be useful in predicting the native plant richness of any value within the range of the island area. It also predicts native plant richness from area outside the given range, but such extrapolation may not be useful.
Regression coefficients (slope) and constant (yintercept)
 The regression coefficients or slope (0.3557) represent the change in the
y
per unit change in theX
variable. It means the value of native plant richness increases by 0.3557 with each unit increase in island area.  The yintercept (1.3360)
represents the value of
y
when theX
variable has a value of 0. Here need to be cautious to interpret the yintercept as sometimes the value (X
=0) does not make any sense (e.g. island area, speed of the car, or height of the person). In such cases, the values within the range ofX
should be considered interpreting the yintercept.  The p values associated with the
area
is significant (p < 0.05). It suggests that the island area significantly influences the native plant richness.
ANOVA
 In regression, the ANOVA tests the null hypothesis that there is no relationship between the independent variable (
X
) and dependent (y
) variable i.e it tests the null hypothesis that regression coefficient equal to zero (b=0).  From ANOVA F test, the p value is significant (<0.05),
which suggests that there is a significant relationship between native plant richness and island area. The independent
variable (
X
) can reliably predict the dependent (y
) variable.
Coefficient of determination (RSquared and adjusted RSquared)
 The coefficient of determination (RSquared) is 0.828 (82.8%), which suggests that 82.8% of the variance in
ntv_rich
can be explained byarea
alone. Adjusted RSquared is useful where there are multipleX
variables in the model (how to interpret adjusted RSquared)
Linear Regression (LR) plot
Generate regression plot,
from bioinfokit import visuz
# get predicted Y and add to original dataframe
df['yhat']=reg.predict(X)
df.head(2)
ntv_rich area yhat
0 1.897627 1.602060 1.905964
1 1.633468 0.477121 1.505779
# create regression plot with defaults
visuz.stat.regplot(df=df, x='area', y='ntv_rich', yhat='yhat')
# plot will be saved in same dir (reg_plot.png)
# set parameter show=True, if you want view the image instead of saving
Check Linear Regression (LR) Assumptions
Residuals vs fitted (y_hat) plot: This plot used to check for linearity, variances and outliers in the regression data
# get residuals and standardized residuals and add to original dataframe
df['res']=pd.DataFrame(reg.resid)
df['std_res']=reg.get_influence().resid_studentized_internal
df.head(2)
ntv_rich area yhat std_res res
0 1.897627 1.602060 1.905964 0.040767 0.008337
1 1.633468 0.477121 1.505779 0.655482 0.127689
# create fitted (y_hat) vs residuals plot
visuz.stat.reg_resid_plot(df=df, yhat='yhat', resid='res', stdresid='std_res')
# plot will be saved in same dir (resid_plot.png and std_resid_plot.png)
# set parameter show=True, if you want view the image instead of saving
From the plot,
 As the data is pretty equally distributed around the line=0 in the residual plot, it meets the assumption of residual equal variances. The outliers could be detected here if the data lies far away from the line=0.
 In the standardized residual plot, the residuals are within 2 and +2 range and suggest that it meets assumptions of linearity
Quantilequantile (QQ) plot: This plot used to check the data normality assumption
import statsmodels.api as sm
import matplotlib.pyplot as plt
# create QQ plot
# line=45 option to plot the data around 45 degree line
sm.qqplot(df['std_res'], line='45')
plt.xlabel("Theoretical Quantiles")
plt.ylabel("Standardized Residuals")
plt.show()
From the plot,
 As the standardized residuals lie around the 45degree line, it suggests that the residuals are normally distributed
Learn how to train linear regression model using neural networks (PyTorch)
References
 Abdi H. Multiple correlation coefficient. Encyclopedia of measurement and statistics. 2007;648:651.
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