Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Multiple linear regression is an extension of simple linear regression to model the relationship between a scalar dependent variable and two or more explanatory variables.

Here are a few key aspects about multiple regression:

  • It allows us to analyze the joint relationships and relative influence of multiple independent variables on a single dependent variable.

  • The regression equation becomes:

y = b0 + b1x1 + b2x2 +…+ bnxn

Where b0 is the intercept, b1 to bn are coefficients of variables x1 to xn.

  • Both continuous and categorical variables can be used as independents.

  • It helps determine the individual and partial contribution of each independent variable in explaining the variance in dependent variable.

  • Interpretation of coefficients is similar to simple linear regression – they indicate the expected change in Y with each one unit increase in the corresponding X, keeping others constant.

  • Evaluation metrics like R-squared, F-statistic, p-values, etc. are used to assess overall model fit and significance.

  • Assumptions include linearity, no multicollinearity, homoscedasticity, independence of errors, normality of residuals.

  • Variable selection techniques help determine the optimal set of predictors.

So in summary, multiple regression extends linear modeling to multiple independent factors simultaneously.

Join the conversation
Muhammad Tufail 4 months ago
Good! Nice Class