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Kiah Ong
This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.
This course is part of the Performance Based Admission courses for the Data Science program.
Read more
This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.
This course is part of the Performance Based Admission courses for the Data Science program.
Read more
This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.
This course is part of the Performance Based Admission courses for the Data Science program.
This course will focus on getting you acquainted with the basic ideas behind regression, it provides you with an overview of the basic techniques in regression such as simple and multiple linear regression, and the use of categorical variables.
Software Requirements: R
Upon successful completion of this course, you will be able to:
- Describe the assumptions of the linear regression models.
- Compute the least squares estimators using R.
- Describe the properties of the least squares estimators.
- Use R to fit a linear regression model to a given data set.
- Interpret and draw conclusions on the linear regression model.
- Use R to perform statistical inference based on the regression models.
Register for this course and see more details by visiting:
OpenCourser.com/course/0npobz/linear
What's inside
Syllabus
Module 1: Simple linear regression
Welcome to Linear Regression! In this course, we will cover the following topics: Simple Linear Regression, Multiple Linear Regression, and Regression Models with Qualitative Predictors. In Module 1, we will focus on defining the problem and setting up the simple linear regression model. Additionally, you will be introduced to the least square method as well as performing statistical inferences and predictions using R. There is a lot to read, watch, and consume in this module so, let’s get started!
Read more
Syllabus
Module 1: Simple linear regression
Welcome to Linear Regression! In this course, we will cover the following topics: Simple Linear Regression, Multiple Linear Regression, and Regression Models with Qualitative Predictors. In Module 1, we will focus on defining the problem and setting up the simple linear regression model. Additionally, you will be introduced to the least square method as well as performing statistical inferences and predictions using R. There is a lot to read, watch, and consume in this module so, let’s get started!
Read more
Module 2: Multiple Linear Regression
Welcome to Module 2 - Multiple linear Regression. This module will focus on deriving parameter estimation using matrices as well as using R to do prediction and inference. There is a lot to read, watch, and consume in this module so, let’s get started!
Module 3: Regression Models with Qualitative Predictors
Welcome to Module 3 – Regression Models with Qualitative Predictors. This module will focus on setting up a linear regression model that involves qualitative predictors. Additionally, we will use R to help us perform statistical inferences and Predictions. There is a lot to read, watch, and consume in this module so, let’s get started!
Summative Course Assessment
This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course. Be sure to review the course material thoroughly before taking the assessment.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Kiah Ong, who are recognized for their work in the field of Data Science
Explores fundamental concepts and methods in regression analysis, which are foundational in Data Science, Finance, Health Care, and Government sectors
Emphasizes the practical applications of regression analysis and statistical inference using R, a widely used language and toolset in Data Science
Provides a comprehensive overview of the topic through modules covering Simple Linear Regression, Multiple Linear Regression, and Regression Models with Qualitative Predictors
Part of the Performance Based Admission courses for the Data Science program, suggesting the possibility for further study and career advancement
Suitable for individuals with technical backgrounds in Mathematics, Statistics, Computer Science, or Engineering who aspire to make a career shift to Data Science or related fields
Save this course
Save Linear Regression to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Linear Regression with these
activities:
Resource Compilation: Regression Modeling Techniques and Tools
Show steps
Organize and curate a collection of valuable resources, including articles, tutorials, and tools, covering various regression modeling techniques and tools.
Browse courses on
Linear Regression
Show steps
-
Identify and collect relevant resources from various sources, such as research papers, websites, and online repositories.
-
Organize and categorize the resources based on different regression techniques or topics.
-
Provide a brief summary or description for each resource, explaining its purpose and relevance.
Review Matrix Algebra and Linear Transformations
Show steps
Strengthen your foundation in matrix algebra and linear transformations, which are essential concepts for understanding linear regression.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of matrices, vectors, and matrix operations.
-
Practice solving systems of linear equations using matrices.
-
Study the concept of linear transformations and how they relate to matrices.
Peer Study Group: Regression Modeling Techniques
Show steps
Engage with fellow learners in a collaborative setting to share knowledge, discuss concepts, and work through regression modeling problems together.
Browse courses on
Linear Regression
Show steps
-
Form a study group of 3-5 individuals.
-
Choose a topic or problem related to regression modeling for each session.
-
Take turns presenting, discussing, and solving problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Exploratory Data Analysis for Regression with R
Show steps
Enhance your skills in data preprocessing and exploration, essential steps in linear regression analysis.
Browse courses on
Regression Analysis
Show steps
-
Follow tutorials on exploratory data analysis techniques using R, such as data visualization, outlier detection, and transformation.
-
Apply these techniques to real-world datasets, identifying patterns and potential issues that may impact regression analysis.
Linear Regression Practice with Simulated Data
Show steps
Deepen your practical understanding of linear regression models by engaging in repetitive exercises with simulated data.
Browse courses on
Simple Linear Regression
Show steps
-
Generate simulated data with varying levels of complexity, including different numbers of predictors and correlation structures.
-
Apply simple and multiple linear regression models to the simulated data, evaluating model performance and interpreting results.
Regression Analysis Project: Real-World Case Study
Show steps
Demonstrate your proficiency in applying regression models by conducting a comprehensive analysis on a real-world dataset.
Browse courses on
Linear Regression
Show steps
-
Identify a dataset that aligns with your interests and research question.
-
Clean and explore the data, identifying potential outliers and transformations.
-
Build and evaluate multiple regression models, comparing their performance and interpreting the results.
-
Create a comprehensive report that summarizes your findings and insights.
Kaggle Competition: House Prices Prediction
Show steps
Challenge yourself in a competitive environment by participating in a Kaggle competition that involves building regression models for real-world problems.
Browse courses on
Linear Regression
Show steps
-
Study the competition guidelines and familiarize yourself with the dataset.
-
Develop and optimize your regression model, utilizing advanced techniques such as feature engineering and cross-validation.
-
Submit your predictions and track your progress on the leaderboard.
Contribute to Open Source Regression Libraries
Show steps
Gain hands-on experience with regression techniques and contribute to the open-source community by making contributions to popular libraries such as scikit-learn.
Browse courses on
Linear Regression
Show steps
-
Identify a regression-related issue or feature to work on.
-
Study the library's codebase and documentation.
-
Develop and test your code contribution.
Resource Compilation: Regression Modeling Techniques and Tools
Show steps
Organize and curate a collection of valuable resources, including articles, tutorials, and tools, covering various regression modeling techniques and tools.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify and collect relevant resources from various sources, such as research papers, websites, and online repositories.
- Organize and categorize the resources based on different regression techniques or topics.
- Provide a brief summary or description for each resource, explaining its purpose and relevance.
Review Matrix Algebra and Linear Transformations
Show steps
Strengthen your foundation in matrix algebra and linear transformations, which are essential concepts for understanding linear regression.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of matrices, vectors, and matrix operations.
- Practice solving systems of linear equations using matrices.
- Study the concept of linear transformations and how they relate to matrices.
Peer Study Group: Regression Modeling Techniques
Show steps
Engage with fellow learners in a collaborative setting to share knowledge, discuss concepts, and work through regression modeling problems together.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Form a study group of 3-5 individuals.
- Choose a topic or problem related to regression modeling for each session.
- Take turns presenting, discussing, and solving problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Exploratory Data Analysis for Regression with R
Show steps
Enhance your skills in data preprocessing and exploration, essential steps in linear regression analysis.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Follow tutorials on exploratory data analysis techniques using R, such as data visualization, outlier detection, and transformation.
- Apply these techniques to real-world datasets, identifying patterns and potential issues that may impact regression analysis.
Linear Regression Practice with Simulated Data
Show steps
Deepen your practical understanding of linear regression models by engaging in repetitive exercises with simulated data.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Generate simulated data with varying levels of complexity, including different numbers of predictors and correlation structures.
- Apply simple and multiple linear regression models to the simulated data, evaluating model performance and interpreting results.
Regression Analysis Project: Real-World Case Study
Show steps
Demonstrate your proficiency in applying regression models by conducting a comprehensive analysis on a real-world dataset.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify a dataset that aligns with your interests and research question.
- Clean and explore the data, identifying potential outliers and transformations.
- Build and evaluate multiple regression models, comparing their performance and interpreting the results.
- Create a comprehensive report that summarizes your findings and insights.
Kaggle Competition: House Prices Prediction
Show steps
Challenge yourself in a competitive environment by participating in a Kaggle competition that involves building regression models for real-world problems.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Study the competition guidelines and familiarize yourself with the dataset.
- Develop and optimize your regression model, utilizing advanced techniques such as feature engineering and cross-validation.
- Submit your predictions and track your progress on the leaderboard.
Contribute to Open Source Regression Libraries
Show steps
Gain hands-on experience with regression techniques and contribute to the open-source community by making contributions to popular libraries such as scikit-learn.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify a regression-related issue or feature to work on.
- Study the library's codebase and documentation.
- Develop and test your code contribution.
Career center
Learners who complete Linear Regression will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to help businesses solve complex problems. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Data Scientist, as you will often need to use regression to identify trends and patterns in data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models to help businesses solve complex problems. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Machine Learning Engineer, as you will often need to use regression to identify trends and patterns in data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Software Engineer, as you will often need to use regression to identify trends and patterns in data.
Data Analyst
Data Analyst
Data Analysts study data to help businesses make informed decisions. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Data Analyst, as you will often need to use regression to identify trends and patterns in data.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to help businesses make investment decisions. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Financial Analyst, as you will often need to use regression to identify trends and patterns in financial data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to help investment firms make investment decisions. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Quantitative Analyst, as you will often need to use regression to identify trends and patterns in financial data.
Market Researcher
Market Researcher
Market Researchers study consumer behavior to help businesses develop marketing strategies. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Market Researcher, as you will often need to use regression to identify trends and patterns in consumer behavior.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to help businesses improve their operations. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as an Operations Research Analyst, as you will often need to use regression to identify trends and patterns in operational data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses make informed decisions. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Statistician, as you will often need to use regression to identify trends and patterns in data.
Business Analyst
Business Analyst
Business Analysts study business processes to help businesses improve their efficiency and effectiveness. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Business Analyst, as you will often need to use regression to identify trends and patterns in business data.
Econometrician
Econometrician
Econometricians use mathematical and statistical methods to study economic data. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as an Econometrician, as you will often need to use regression to identify trends and patterns in economic data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines to help businesses store and process data. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Data Engineer, as you will often need to use regression to identify trends and patterns in data.
Financial Risk Manager
Financial Risk Manager
Financial Risk Managers use mathematical and statistical methods to assess financial risk. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as a Financial Risk Manager, as you will often need to use regression to identify trends and patterns in financial data.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. The skills and knowledge you learn in this course will help you build a strong foundation in regression, which is a statistical method used to analyze relationships between variables. This will be essential for your success as an Actuary, as you will often need to use regression to identify trends and patterns in data.
For more career information including salaries, visit:
OpenCourser.com/course/0npobz/linear
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Linear Regression.
Provides a comprehensive introduction to machine learning, including linear regression, logistic regression, and neural networks.
Provides a comprehensive introduction to statistical learning, including linear regression, generalized linear models, and support vector machines.
Provides a comprehensive introduction to statistical machine learning, including linear regression, generalized linear models, and support vector machines.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, including linear regression, logistic regression, and Bayesian networks.
Provides comprehensive coverage of the theory and applications of linear regression analysis. It valuable resource for students, researchers, and practitioners in various fields who use linear regression models.
Provides a practical guide to Bayesian statistics using R and Stan, including linear regression, generalized linear models, and hierarchical models.
Provides a comprehensive introduction to linear models and their applications using R. It covers a wide range of topics, including simple and multiple linear regression, ANOVA, and generalized linear models.
Provides a comprehensive treatment of the theory and applications of linear regression analysis. It covers a wide range of topics, including model selection, diagnostics, and forecasting.
Provides a practical guide to machine learning using R, including linear regression, logistic regression, and decision trees.
Provides a practical guide to machine learning using Python, including linear regression, logistic regression, and decision trees.
Save
Provides a comprehensive introduction to deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to regression analysis using real-world examples. It covers a wide range of topics, including simple and multiple linear regression, ANOVA, and generalized linear models.
Provides a practical guide to fitting and interpreting linear regression models. It covers a wide range of topics, including model selection, diagnostics, and forecasting.
Provides an introduction to regression modeling with a focus on actuarial and financial applications. It covers a wide range of topics, including linear regression, generalized linear models, and Bayesian regression.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0npobz/linear
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Level
Intermediate
Via
Coursera
Institution
Illinois Tech
Instructor
Kiah Ong
Language
English
Good to know
Taught by Kiah Ong, who are recognized for their work in the field of Data Science
Explores fundamental concepts and methods in regression analysis, which are foundational in Data Science, Finance, Health Care, and Government sectors
Emphasizes the practical applications of regression analysis and statistical inference using R, a widely used language and toolset in Data Science
Provides a comprehensive overview of the topic through modules covering Simple Linear Regression, Multiple Linear Regression, and Regression Models with Qualitative Predictors
Part of the Performance Based Admission courses for the Data Science program, suggesting the possibility for further study and career advancement
Suitable for individuals with technical backgrounds in Mathematics, Statistics, Computer Science, or Engineering who aspire to make a career shift to Data Science or related fields
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