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Dr. Nikunj Maheshwari
By the end of this project, you will learn how to build and analyse linear regression model in R, a free, open-source program that you can download. You will learn how to load and clean a real world dataset. Next, you will learn how to build a linear regression model and various plots to analyze the model’s performance. Lastly, you will learn how to predict future values using the model. By the end of this project, you will become confident in building a linear regression model on real world dataset and the know-how of assessing the model’s performance using R programming language.
Read more
By the end of this project, you will learn how to build and analyse linear regression model in R, a free, open-source program that you can download. You will learn how to load and clean a real world dataset. Next, you will learn how to build a linear regression model and various plots to analyze the model’s performance. Lastly, you will learn how to predict future values using the model. By the end of this project, you will become confident in building a linear regression model on real world dataset and the know-how of assessing the model’s performance using R programming language.
Read more
By the end of this project, you will learn how to build and analyse linear regression model in R, a free, open-source program that you can download. You will learn how to load and clean a real world dataset. Next, you will learn how to build a linear regression model and various plots to analyze the model’s performance. Lastly, you will learn how to predict future values using the model. By the end of this project, you will become confident in building a linear regression model on real world dataset and the know-how of assessing the model’s performance using R programming language.
Linear regression models are useful in identifying critical relationships between predictors (or factors) and output variable. These relationships can impact a business in the future and can help business owners to make decisions.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/21owyx/building
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What's inside
Syllabus
Building and analyzing linear regression model in R
By the end of this project, you will learn how to build and analyse linear regression model in R, a free, open-source program that you can download. You will learn how to load and clean a real world dataset. Next, you will learn how to build a linear regression model and various plots to analyze the model’s performance. Lastly, you will learn how to predict future values using the model. By the end of this project, you will become confident in building a linear regression model on real world dataset and the know-how of assessing the model’s performance using R programming language.
Linear regression models are useful in identifying critical relationships between predictors (or factors) and output variable. These relationships can impact a business in the future and can help business owners to make decisions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on using free, open-source software for analysis, which is extremely cost-efficient
Teaches how to build data models in R, which is an essential skill in many technical roles
Covers loading and cleaning datasets, which is a crucial step in data analysis
Emphasizes assessing model performance, which is vital for evaluating the reliability of predictions
Suitable for learners without prior knowledge of R programming, as it starts from the basics
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Reviews summary
In-depth regression analysis
Learners say Building and analyzing linear regression models in R is a great option for brushing up on R-studio, regression analysis, and simple linear modeling. Students particularly enjoyed the basic explanations and relevant examples provided in the materials. Additionally, learners mention that the course is well-suited for beginners with little to no experience with R. Though the explanations of statistics in the course are not very detailed, learners mention that Dr. Nikunj is a great instructor and that they learned a great deal.
Dr. Nikunj is an engaging instructor.
"Dr. Nikunj was a good instructor and I learned a great deal in any case!"
Course is ideal for learners with little to no experience with R.
"Good course, and very beginner-friendly even to ones which hadn't had any experience with R before."
The course is great for refreshing knowledge on regression analysis and R.
"Overall, if you want to refresh some thing concerning regression, it is decent."
"Very nice introductory R course to building simple and most common linear models."
The concepts of regression analysis for beginners are well-explained.
"A simple and understandable way of utilizing regression in R to analyze real world data sets."
Some of the statistical explanations were brief and not thorough.
"I did however miss a more extensive explanation on the statistics calculated in the course and why each step in the course was taken."
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 Building and analyzing linear regression model in R with these
activities:
Refresh Your Math Skills
Show steps
Strengthen your mathematical foundation for better understanding of linear regression.
Browse courses on
Algebra
Show steps
-
Review basic algebra concepts.
-
Practice solving equations and inequalities.
-
Review trigonometry and calculus concepts as needed.
Read 'An Introduction to Statistical Learning'
Show steps
Review the fundamental concepts of statistical learning and machine learning.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read Chapters 1-3 of the book.
-
Complete the exercises at the end of each chapter.
-
Summarize the key concepts in each chapter.
Join a Study Group
Show steps
Collaborate with peers to reinforce concepts and improve understanding.
Browse courses on
Linear Regression
Show steps
-
Find or create a study group with other students taking the course.
-
Meet regularly to discuss course material, work on assignments together, and quiz each other.
-
Share resources and insights with your group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Linear Regression with R
Show steps
Develop proficiency in using R programming for linear regression.
Browse courses on
Linear Regression
Show steps
-
Load a dataset into R.
-
Fit a linear regression model to the dataset.
-
Evaluate the model's performance.
Explore R Markdown for Reporting
Show steps
Enhance communication and presentation skills in R.
Browse courses on
Linear Regression
Show steps
-
Follow a tutorial on using R Markdown.
-
Create an R Markdown document that includes your linear regression analysis.
-
Export your document to a variety of formats, including HTML, PDF, and Word.
Create a Visual Representation of Your Linear Regression Model
Show steps
Enhance comprehension of linear regression concepts through visual representation.
Browse courses on
Linear Regression
Show steps
-
Plot the data points and the fitted regression line.
-
Add annotations to the plot to highlight key features.
-
Generate a summary table of the regression results.
Read 'Elements of Statistical Learning'
Show steps
Delve deeper into advanced concepts of linear regression and machine learning.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Read Chapters 4-6 of the book.
-
Complete the exercises at the end of each chapter.
-
Summarize the key concepts in each chapter.
Volunteer as a Data Analyst
Show steps
Gain practical experience in applying linear regression in a real-world setting.
Browse courses on
Linear Regression
Show steps
-
Identify a non-profit organization or research institution that needs assistance with data analysis.
-
Offer your services and explain your skills in linear regression.
-
Work with the organization to identify a suitable project.
-
Apply linear regression techniques to analyze data and extract insights.
Refresh Your Math Skills
Show steps
Strengthen your mathematical foundation for better understanding of linear regression.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Review basic algebra concepts.
- Practice solving equations and inequalities.
- Review trigonometry and calculus concepts as needed.
Read 'An Introduction to Statistical Learning'
Show steps
Review the fundamental concepts of statistical learning and machine learning.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3 of the book.
- Complete the exercises at the end of each chapter.
- Summarize the key concepts in each chapter.
Join a Study Group
Show steps
Collaborate with peers to reinforce concepts and improve understanding.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course.
- Meet regularly to discuss course material, work on assignments together, and quiz each other.
- Share resources and insights with your group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Linear Regression with R
Show steps
Develop proficiency in using R programming for linear regression.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Load a dataset into R.
- Fit a linear regression model to the dataset.
- Evaluate the model's performance.
Explore R Markdown for Reporting
Show steps
Enhance communication and presentation skills in R.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Follow a tutorial on using R Markdown.
- Create an R Markdown document that includes your linear regression analysis.
- Export your document to a variety of formats, including HTML, PDF, and Word.
Create a Visual Representation of Your Linear Regression Model
Show steps
Enhance comprehension of linear regression concepts through visual representation.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Plot the data points and the fitted regression line.
- Add annotations to the plot to highlight key features.
- Generate a summary table of the regression results.
Read 'Elements of Statistical Learning'
Show steps
Delve deeper into advanced concepts of linear regression and machine learning.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 4-6 of the book.
- Complete the exercises at the end of each chapter.
- Summarize the key concepts in each chapter.
Volunteer as a Data Analyst
Show steps
Gain practical experience in applying linear regression in a real-world setting.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify a non-profit organization or research institution that needs assistance with data analysis.
- Offer your services and explain your skills in linear regression.
- Work with the organization to identify a suitable project.
- Apply linear regression techniques to analyze data and extract insights.
Career center
Learners who complete Building and analyzing linear regression model in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use statistical techniques to identify trends and patterns in data, and they communicate their findings to stakeholders in a clear and concise way. This course will help you develop the skills you need to become a successful Data Analyst. You will learn how to load and clean data, build linear regression models, and analyze the results of your models. You will also learn how to use R, a free and open-source programming language that is widely used in the field of data analysis.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They work in a variety of fields, including finance, healthcare, and marketing. This course will help you develop the skills you need to become a successful Statistician. You will learn how to build linear regression models, analyze the results of your models, and communicate your findings to stakeholders in a clear and concise way.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They use a variety of statistical and programming techniques to build models that can learn from data and make predictions. This course will help you develop the skills you need to become a successful Machine Learning Engineer. You will learn how to build linear regression models, analyze the results of your models, and use R to deploy your models to the cloud.
Data Scientist
Data Scientist
Data Scientists use a variety of statistical and programming techniques to extract insights from data. They work in a variety of fields, including finance, healthcare, and marketing. This course will help you develop the skills you need to become a successful Data Scientist. You will learn how to build linear regression models, analyze the results of your models, and communicate your findings to stakeholders in a clear and concise way.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They develop models to predict the performance of stocks, bonds, and other financial instruments. This course will help you develop the skills you need to become a successful Quantitative Analyst. You will learn how to build linear regression models, analyze the results of your models, and use R to develop trading strategies.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They work with stakeholders to identify the problem, collect and analyze data, and develop solutions. This course will help you develop the skills you need to become a successful Business Analyst. You will learn how to build linear regression models, analyze the results of your models, and communicate your findings to stakeholders in a clear and concise way.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. They use this information to develop marketing strategies and campaigns. This course will help you develop the skills you need to become a successful Market Researcher. You will learn how to build linear regression models, analyze the results of your models, and use R to conduct market research surveys.
Product Manager
Product Manager
Product Managers lead the development and launch of new products. They work with engineers, designers, and marketers to ensure that the product meets the needs of the customer. This course will help you develop the skills you need to become a successful Product Manager. You will learn how to build linear regression models, analyze the results of your models, and use R to track the performance of your product.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. They use their expertise to help businesses solve problems, improve efficiency, and achieve their goals. This course will help you develop the skills you need to become a successful Consultant. You will learn how to build linear regression models, analyze the results of your models, and communicate your findings to clients in a clear and concise way.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They analyze financial statements, develop models, and make recommendations to clients on how to invest their money. This course will help you develop the skills you need to become a successful Financial Analyst. You will learn how to build linear regression models, analyze the results of your models, and use R to develop investment strategies.
Risk Manager
Risk Manager
Risk Managers identify and manage risks for businesses. They use a variety of techniques to assess risks, develop mitigation strategies, and ensure that businesses are prepared for potential events. This course will help you develop the skills you need to become a successful Risk Manager. You will learn how to build linear regression models, analyze the results of your models, and use R to develop risk management strategies.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risks. They work in a variety of industries, including insurance, healthcare, and finance. This course will help you develop the skills you need to become a successful Actuary. You will learn how to build linear regression models, analyze the results of your models, and use R to develop actuarial models.
Underwriter
Underwriter
Underwriters assess and manage risks for insurance companies. They use a variety of techniques to determine the likelihood of an event occurring and the potential financial impact of the event. This course will help you develop the skills you need to become a successful Underwriter. You will learn how to build linear regression models, analyze the results of your models, and use R to develop underwriting guidelines.
Insurance Agent
Insurance Agent
Insurance Agents sell insurance policies to individuals and businesses. They work with clients to identify their needs and develop a plan to protect them from financial risks. This course may be useful to you if you are interested in becoming an Insurance Agent. You will learn how to build linear regression models, analyze the results of your models, and use R to develop insurance policies.
Loan Officer
Loan Officer
Loan Officers evaluate and approve loan applications. They work with clients to determine their eligibility for a loan and to develop a repayment plan. This course may be useful to you if you are interested in becoming a Loan Officer. You will learn how to build linear regression models, analyze the results of your models, and use R to develop loan underwriting guidelines.
For more career information including salaries, visit:
OpenCourser.com/course/21owyx/building
Reading list
We've selected 15 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
Building and analyzing linear regression model in R.
Provides a comprehensive introduction to statistical learning, including linear regression models. It valuable reference for anyone interested in understanding and applying statistical learning techniques.
Provides a comprehensive overview of Bayesian data analysis, including the use of linear regression models for Bayesian inference. It valuable reference for anyone interested in learning more about Bayesian data analysis and its applications.
Save
Provides a comprehensive overview of causal inference, including the use of linear regression models for causal inference. It valuable reference for anyone interested in learning more about causal inference and its applications.
Provides a comprehensive introduction to statistical learning, including linear regression models. It valuable reference for anyone interested in understanding and applying statistical learning techniques.
Provides a comprehensive overview of linear regression models. It valuable reference for anyone interested in learning more about linear regression models and their applications.
Provides a detailed overview of linear models, including regression models. It useful reference for anyone interested in learning more about linear models and their applications.
Provides a comprehensive introduction to machine learning, including linear regression models. It valuable reference for anyone interested in understanding and applying machine learning techniques.
Provides a unique and insightful perspective on regression modeling. It valuable resource for anyone interested in understanding the underlying principles of regression modeling.
Provides a unique and insightful perspective on statistical modeling, including linear regression models. It valuable resource for anyone interested in understanding the underlying principles of statistical modeling.
Provides a comprehensive overview of generalized linear models, including linear regression models. It valuable reference for anyone interested in learning more about generalized linear models and their applications.
Provides a comprehensive overview of linear regression analysis. It valuable reference for anyone interested in learning more about linear regression analysis and its applications.
Provides a practical guide to regression analysis, including linear regression models. It valuable resource for anyone interested in using regression analysis to solve real-world problems.
Provides a comprehensive overview of applied statistics, including linear regression models. It valuable reference for anyone interested in learning more about applied statistics and its applications.
Provides a practical guide to applied linear regression. It valuable resource for anyone interested in using linear regression models to solve real-world problems.
Provides a comprehensive overview of statistical models, including linear regression models. It valuable reference for anyone interested in learning more about statistical models and their applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/21owyx/building
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Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Dr. Nikunj Maheshwari
Language
English
Good to know
Focuses on using free, open-source software for analysis, which is extremely cost-efficient
Teaches how to build data models in R, which is an essential skill in many technical roles
Covers loading and cleaning datasets, which is a crucial step in data analysis
Emphasizes assessing model performance, which is vital for evaluating the reliability of predictions
Suitable for learners without prior knowledge of R programming, as it starts from the basics
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