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Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way. No math is involved other than a few equations in which addition and subtraction are used. The purpose of this part of the course is for students to understand what linear regression is and when it is used. Students will learn the differences between simple linear regression and multiple linear regression. They will be able to understand the output of linear regression, test model accuracy and assumptions. Students will also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. All this theory is explained in the slides, which are made available to the students, as well as in the e-book that is freely available for students who enroll in the course.
Read more
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way. No math is involved other than a few equations in which addition and subtraction are used. The purpose of this part of the course is for students to understand what linear regression is and when it is used. Students will learn the differences between simple linear regression and multiple linear regression. They will be able to understand the output of linear regression, test model accuracy and assumptions. Students will also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. All this theory is explained in the slides, which are made available to the students, as well as in the e-book that is freely available for students who enroll in the course.
Read more
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way. No math is involved other than a few equations in which addition and subtraction are used. The purpose of this part of the course is for students to understand what linear regression is and when it is used. Students will learn the differences between simple linear regression and multiple linear regression. They will be able to understand the output of linear regression, test model accuracy and assumptions. Students will also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. All this theory is explained in the slides, which are made available to the students, as well as in the e-book that is freely available for students who enroll in the course.
In the second part of the course, students will learn how to apply what they learned using Stata. In this part, students will use Stata to fit multiple regression models, produce graphs that describe model fit and assumptions, and to use variable specific commands that will make the output more readable. This part assumed very basic knowledge of Stata.
Register for this course and see more details by visiting:
OpenCourser.com/course/0so45p/linear
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Helps learners get started with multiple regression without any math background
Covers core theory and concepts behind linear regression in an intuitive way
Provides hands-on experience in applying multiple regression models using Stata
Helps learners understand the differences between simple linear regression and multiple linear regression
Assumes basic knowledge of Stata, making it suitable for learners with some experience in data analysis
Provides additional materials such as an e-book and slides, enhancing the learning experience
Save this course
Save Linear Regression using Stata 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 using Stata with these
activities:
Go back to Summary Statistics
Show steps
Reviewing summary statistics will help you better understand the data you are working with.
Browse courses on
Summary Statistics
Show steps
-
Review the concepts of mean, median, mode, and standard deviation
-
Practice calculating these statistics using a calculator or software
Review your notes from previous courses on statistics or mathematics
Show steps
Refreshing your knowledge of these topics will help you better understand the material in this course.
Browse courses on
Statistics
Show steps
-
Go over your notes from previous courses on statistics or mathematics
-
Focus on the topics that are most relevant to this course
-
Make a note of any areas where you need to brush up on your knowledge
Read and review 'Introductory Econometrics: A Modern Approach' by Wooldridge
Show steps
This book introduces many of the concepts covered in the course, and reviewing it can help you better understand the material.
Show steps
-
Read the chapters relevant to the course material
-
Summarize the key concepts in your own words
-
Make a note of any questions you have
Five other activities
Expand to see all activities and additional details
Show all eight activities
Find and complete online tutorials on Stata or linear regression
Show steps
There are many helpful tutorials available online that can supplement your learning.
Show steps
-
Search for tutorials on Stata or linear regression
-
Watch or read through the tutorials
-
Complete the exercises provided in the tutorials
Practice Linear Regression in Stata
Show steps
Many students find that practicing a new program such as Stata helps them better retain knowledge about its functionality.
Show steps
-
Practice fitting a simple linear regression model
-
Practice fitting a multiple regression model
-
Practice diagnosing model assumptions
-
Practice interpreting model output
Discuss the course material with classmates
Show steps
Talking with classmates about the material will provide different perspectives and improve your own understanding of the concepts.
Show steps
-
Form a study group with 2-3 other students
-
Meet regularly to discuss the course material
Develop a cheat sheet of key formulas and concepts
Show steps
Creating a cheat sheet will help you organize and retain the most important information from the course.
Show steps
-
Identify the key formulas and concepts from the course material
-
Write out the formulas and concepts on a single page
-
Review the cheat sheet regularly
Create a Stata script to automate your workflow
Show steps
Creating a Stata script to automate a workflow provides an opportunity to solidify understanding of the different steps involved in the workflow while also enhancing programming skills.
Browse courses on
Programming
Show steps
-
Identify a repetitive task that can be automated
-
Write a Stata script to automate the task
-
Test the script to ensure it works as expected
-
Document the script for future use
Go back to Summary Statistics
Show steps
Reviewing summary statistics will help you better understand the data you are working with.
Browse courses on
Summary Statistics
Show steps
Show steps
Show steps
- Review the concepts of mean, median, mode, and standard deviation
- Practice calculating these statistics using a calculator or software
Review your notes from previous courses on statistics or mathematics
Show steps
Refreshing your knowledge of these topics will help you better understand the material in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Go over your notes from previous courses on statistics or mathematics
- Focus on the topics that are most relevant to this course
- Make a note of any areas where you need to brush up on your knowledge
Read and review 'Introductory Econometrics: A Modern Approach' by Wooldridge
Show steps
This book introduces many of the concepts covered in the course, and reviewing it can help you better understand the material.
Show steps
Show steps
Show steps
- Read the chapters relevant to the course material
- Summarize the key concepts in your own words
- Make a note of any questions you have
Five other activities
Expand to see all activities and additional details
Show all eight activities
Find and complete online tutorials on Stata or linear regression
Show steps
There are many helpful tutorials available online that can supplement your learning.
Show steps
Show steps
Show steps
- Search for tutorials on Stata or linear regression
- Watch or read through the tutorials
- Complete the exercises provided in the tutorials
Practice Linear Regression in Stata
Show steps
Many students find that practicing a new program such as Stata helps them better retain knowledge about its functionality.
Show steps
Show steps
Show steps
- Practice fitting a simple linear regression model
- Practice fitting a multiple regression model
- Practice diagnosing model assumptions
- Practice interpreting model output
Discuss the course material with classmates
Show steps
Talking with classmates about the material will provide different perspectives and improve your own understanding of the concepts.
Show steps
Show steps
Show steps
- Form a study group with 2-3 other students
- Meet regularly to discuss the course material
Develop a cheat sheet of key formulas and concepts
Show steps
Creating a cheat sheet will help you organize and retain the most important information from the course.
Show steps
Show steps
Show steps
- Identify the key formulas and concepts from the course material
- Write out the formulas and concepts on a single page
- Review the cheat sheet regularly
Create a Stata script to automate your workflow
Show steps
Creating a Stata script to automate a workflow provides an opportunity to solidify understanding of the different steps involved in the workflow while also enhancing programming skills.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Identify a repetitive task that can be automated
- Write a Stata script to automate the task
- Test the script to ensure it works as expected
- Document the script for future use
Career center
Learners who complete Linear Regression using Stata will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts apply statistical techniques to extract meaningful insights from data. This course will provide you with a strong foundation in linear regression, a widely used statistical technique. Linear regression is used to predict outcomes based on one or more independent variables. By understanding the theory behind linear regression and how to apply it using Stata, you will be well-equipped to succeed as a Data Analyst.
Data Scientist
Data Scientist
Data Scientists use statistical techniques, including linear regression, to analyze data and extract meaningful insights. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Data Scientist.
Statistician
Statistician
Statisticians use statistical techniques, including linear regression, to analyze data and draw conclusions. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Statistician.
Risk Analyst
Risk Analyst
Risk Analysts use statistical techniques, including linear regression, to analyze risk data and identify potential risks. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Risk Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical techniques, including linear regression, to analyze financial data and make investment recommendations. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Quantitative Analyst.
Sales Analyst
Sales Analyst
Sales Analysts use statistical techniques, including linear regression, to analyze sales data and identify trends. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Sales Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical techniques, including linear regression, to analyze operational data and identify areas for improvement. This course will provide you with a strong foundation in linear regression, which will be essential for your success as an Operations Research Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use various statistical techniques, including linear regression, to analyze financial data and make investment recommendations. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Financial Analyst.
Economist
Economist
Economists use statistical techniques, including linear regression, to analyze economic data and make predictions. This course will provide you with a strong foundation in linear regression, which will be essential for your success as an Economist.
Market Researcher
Market Researcher
Market Researchers use statistical techniques, including linear regression, to analyze market data and identify trends. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Market Researcher.
Survey Researcher
Survey Researcher
Survey Researchers use statistical techniques, including linear regression, to analyze survey data and identify trends. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Survey Researcher.
Epidemiologist
Epidemiologist
Epidemiologists use statistical techniques, including linear regression, to analyze data in the field of public health. This course will provide you with a strong foundation in linear regression, which will be essential for your success as an Epidemiologist.
Health Data Analyst
Health Data Analyst
Health Data Analysts use statistical techniques, including linear regression, to analyze data in the field of healthcare. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Health Data Analyst.
Biostatistician
Biostatistician
Biostatisticians use statistical techniques, including linear regression, to analyze data in the field of biology. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Biostatistician.
Product Manager
Product Manager
Product Managers use statistical techniques, including linear regression, to analyze product data and identify areas for improvement. This course will provide you with a strong foundation in linear regression, which will be essential for your success as a Product Manager.
For more career information including salaries, visit:
OpenCourser.com/course/0so45p/linear
Reading list
We've selected 12 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 using Stata.
Provides a comprehensive introduction to linear regression analysis, from basic concepts to advanced topics. It valuable resource for students and practitioners in various fields, including statistics, economics, and business.
Save
Provides a detailed overview of linear regression models, including both ordinary least squares (OLS) and generalized least squares (GLS). It comprehensive resource for researchers and practitioners who need to understand and apply linear regression models.
Provides a practical guide to applying linear regression models in various settings. It covers a wide range of topics, including model selection, diagnostics, and interpretation. It valuable resource for practitioners who need to use linear regression models for real-world problems.
Provides a practical guide to regression analysis using real-world examples. It covers a wide range of topics, including model selection, diagnostics, and interpretation. It valuable resource for practitioners who need to use regression analysis for real-world problems.
Provides a comprehensive overview of linear models using the R statistical software package. It covers a wide range of topics, including model fitting, diagnostics, and interpretation. It valuable resource for practitioners who need to use linear models for real-world problems.
Provides a comprehensive overview of statistical learning methods, including linear regression. It valuable resource for students and researchers who need a broad understanding of statistical learning methods.
Provides a comprehensive overview of statistical learning methods, including linear regression. It valuable resource for students and researchers who need a deep understanding of statistical learning methods.
Save
Provides a comprehensive overview of machine learning methods, including linear regression. It valuable resource for students and researchers who need a broad understanding of machine learning methods.
Save
Provides a comprehensive overview of data mining methods, including linear regression. It valuable resource for students and researchers who need a broad understanding of data mining methods.
Provides a comprehensive overview of linear regression using the SAS statistical software package. It valuable resource for practitioners who need to use linear regression for real-world problems.
Provides a comprehensive overview of applied regression analysis. It valuable resource for researchers and practitioners who need to use regression analysis for real-world problems.
Provides a comprehensive overview of introductory statistics for business and economics. It valuable resource for students who need a basic understanding of statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0so45p/linear
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Effort
3.5 total hours
Via
Udemy
Good to know
Helps learners get started with multiple regression without any math background
Covers core theory and concepts behind linear regression in an intuitive way
Provides hands-on experience in applying multiple regression models using Stata
Helps learners understand the differences between simple linear regression and multiple linear regression
Assumes basic knowledge of Stata, making it suitable for learners with some experience in data analysis
Provides additional materials such as an e-book and slides, enhancing the learning experience
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