Linear regression is a statistical method used to create a mathematical model that can help predict an outcome based on the relationship between the dependent variable and the independent variable. In simple terms, it’s like trying to find the best-fitted line through a group of points that can help predict future values.
This article explores simple and multiple linear regression, including the steps needed to build a multiple linear regression model. By the end of this article, you will have a basic understanding of the key concepts of linear regression, the process involved in building a multiple linear regression model and the assumptions you must check for every linear regression model.
Simple Linear Regression
Simple linear regression is a statistical method used to model the relationship between a dependent variable and a single independent variable. The primary aim of simple linear regression is to help predict a specific outcome based on the independent variable.
Regression involves drawing a best-fit line through the provided data, where the square of the distance between each point and the line has been at its minimum. This line can be expressed as a formula known as the regression equation.
Once the equation has been developed, you can use it to predict new values based on the independent variable’s input.
Multiple Linear Regression
Multiple linear regression is an extension of the simple linear regression method to include more than one independent variable. The idea behind multiple linear regression is to explore the relationship between a single dependent variable and two or more independent variables.
To develop a multiple linear regression model, you will need to identify the independent variables contributing to the dependent variable as well as the strength and direction of the relationships between the variables. Steps to Build a
Multiple Linear Regression Model
The first step in building a multiple linear regression model is to identify the dependent variable and independent variables. The dependent variable is usually the variable of interest, while the independent variables are the variables used to predict the dependent variable.
For example, if you wanted to predict a student’s final grade based on their class attendance, study hours, and quiz scores, the final grade would be your dependent variable, while attendance, study hours, and quiz scores would be your independent variables. 2.
Check the Caveats/Assumptions
Before creating a multiple linear regression model, you must check a series of assumptions known as the Model Assumptions. The Model Assumptions are linearity, homoscedasticity, multivariate normality, independence of errors, and lack of multicollinearity.
Linearity – To develop a multiple linear regression model, the relation between the dependent and independent variables should be linear. If the relationship between the variables isn’t linear, it will be challenging to use linear regression to create a model to predict the dependent variable.
Homoscedasticity – Homoscedasticity assumes that the residuals have constant variance around the regression line. When the variance increases or decreases as the values of the independent variable increase, we refer to it as heteroscedasticity.
Multivariate normality – Multivariate normality is the assumption that the residuals follow a normally distributed pattern. This can be checked by creating a normal probability plot of the residuals
Independence of errors – This assumption suggests that there is no systematic relationship between the errors of predicting the dependent variable.
Lack of multicollinearity – Multicollinearity means there is a high correlation between two or more independent variables in the model. This can lead to an unstable model that’s difficult to interpret.
3. Creating Dummy Variables
Categorical independent variables must be converted to dummy variables (0, 1) so that they can be included in the model.
Dummy variables are created to represent the different categories of an independent variable. For example, the independent variable Gender could have two categories, male and female, which can be represented as two separate dummy variables, where Female is 1 and Male is 0.
4. Avoiding Dummy Variable Trap
Dummy variable trap occurs when the dummy variables are correlated with each other, or you include a column for each category when you only need one.
To overcome this, you need to omit one dummy variable column while including the rest in your model. 5.
Building the Model
Finally, once you have identified the necessary variables and checked the assumptions, you can start building the model. You can use stepwise regression techniques to add or remove variables until you have a model that is free from errors and includes only relevant independent variables.
It’s crucial to understand that creating an error-prone model can lead to incorrect predictions and may not be suitable for decision-making processes.
In conclusion, linear regression is a powerful statistical tool that can help predict outcomes based on the relationship between the dependent variable and independent variable. One can use simple or multiple linear regression models, depending on the number of independent variables.
The process of building a multiple linear regression model involves identifying the variables, checking caveats/assumptions, creating and avoiding dummy variables, and building the model itself. It’s essential to pay close attention to these steps to avoid building models that are error-prone and not suitable for decision-making purposes.
Overall, linear regression models are an excellent way to make informed decisions based on data analysis, providing the right model developed and checked against the correct assumptions. Implementing
Multiple Linear Regression in Python
In this section, we will be discussing how to implement multiple linear regression in Python.
The aim is to show how to implement the steps from the previous section using Python code.
Importing the Dataset
We will be using the Startup dataset to apply multiple linear regression. The dataset contains various information about startups, including R&D expenditure, administration expenditure, marketing expenditure, state, and profits.
To load the dataset, we will be using the pandas library. “`
import pandas as pd
dataset = pd.read_csv(‘startup.csv’)
To visualize the data, we will use the matplotlib library. This will help us identify the variables that have a strong correlation.
import matplotlib.pyplot as plt
plt.plot(dataset[‘R&D Spend’], dataset[‘Profit’], ‘o’)
plt.title(‘Correlation Between R&D Spend and Profit’)
The first step in data preprocessing is splitting the dataset into a matrix of features and a dependent variable. “`
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
Next, we need to convert our categorical data into numerical data.
We can use LabelEncoder to convert our categorical data, such as the states, into numerical data. “`
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
However, we cannot directly use our LabelEncoder on our categorical data; we need to use OneHotEncoder to create dummy variables for our categorical data.
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers=[(‘encoder’, OneHotEncoder(), )], remainder=’passthrough’)
X = ct.fit_transform(X)
Splitting the Test and Train Set
We will now split our dataset into a training set and test set to evaluate our model’s accuracy. We will be using the train_test_split function from the sklearn library.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
Fitting the Model
We can now create our multiple linear regression model and fit it to our training data. We will be using the LinearRegression class from the sklearn library.
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
Predicting the Test Set Results
Finally, we can use our test data to evaluate our model’s performance. We use the predict function from the LinearRegression class to predict our y-values based on our X-values.
y_pred = regressor.predict(X_test)
Linear regression is a powerful tool that has both advantages and disadvantages. One of its biggest advantages is its simplicity, and it requires little computational power to generate accurate results.
However, its simplicity presents a disadvantage; it’s less accurate than more complex models when dealing with sophisticated datasets. Another significant disadvantage of linear regression is that it relies heavily on the assumptions listed earlier.
Violating any of these assumptions can lead to erroneous results, which is why it is essential to regularly check the model’s assumptions. Overall, linear regression is an excellent tool for many applications, but it’s essential to consider the dataset’s size and the relevance of the features when deciding whether to use it.
It is also crucial to be aware of its limitations to generate the best and most helpful interpretations of the models’ results. In conclusion, linear regression is a powerful statistical method used to create a mathematical model that can help predict an outcome based on the relationship between the dependent variable and the independent variable.
Simple linear regression models the relationship between one dependent variable and a single independent variable, while multiple linear regression extends this method to include multiple independent variables. To build a multiple linear regression model, you must identify variables, check assumptions, create dummy variables, and build the model.
Ultimately, linear regression models are an excellent way to make informed decisions based on data analysis, but it’s crucial to consider the dataset’s size, relevance of the features, and limitations when interpreting the model’s results. By following the steps and considerations outlined in this article, you’ll be able to use linear regression to make more accurate predictions and informed decisions.