We may earn an affiliate commission when you visit our partners.
Course image
Elena Moltchanova

Basics of Statistical Inference and Modelling Using R is part one of the Statistical Analysis in R professional certificate.

Read more

Basics of Statistical Inference and Modelling Using R is part one of the Statistical Analysis in R professional certificate.

This course is directed at people with limited statistical background and no practical experience, who have to do data analysis, as well as those who are “out of practice”. While very practice oriented, it aims to give the students the understanding of why the method works (theory), how to implement it (programming using R) and when to apply it (and where to look if the particular method is not applicable in the specific situation).

Three deals to help you save

What's inside

Learning objectives

  • Sample and population. sampling distribution. parameter estimates and confidence intervals.
  • Central limit theorem
  • Hypothesis testing. p-values. standard tests: t-test, the test of binomial proportions, chi-squared test. statistical and practical significance.
  • Exploratory data analysis and data visualisation using r.
  • Analysis of variance (anova) and post-hoc tests using r.
  • Multivariate analysis using linear regression and analysis of variance with covariates (ancova). assumptions, diagnostics, interpretation. model comparison and selection.
  • Numerical methods: the use of simulations, non-parametric bootstrap and permutation tests using r.
  • Identifying the research question.
  • Experimental design (basics of power analysis) and missing data.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers statistical methods that are foundational to many analytic approaches in many fields, making it relevant to learners of varying professional backgrounds
Taught by instructors with experience in applying statistics to real-world problems

Save this course

Save Basics of Statistical Inference and Modelling Using R to your list so you can find it easily later:
Save

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 Basics of Statistical Inference and Modelling Using R with these activities:
Review your notes from previous statistics courses
Refreshing your knowledge of statistics will help you build a stronger foundation for this course.
Browse courses on Statistics
Show steps
  • Gather your notes from previous statistics courses
  • Review the notes and identify areas where you need to refresh your knowledge
  • Find additional resources to help you understand the concepts
Read 'Statistical Inference' by George Casella and Roger L. Berger
Reading this book will provide you with a comprehensive understanding of statistical inference concepts.
Show steps
  • Purchase or borrow the book
  • Read the book and take notes
  • Complete the practice problems at the end of each chapter
Solve practice problems on statistical inference
Solving practice problems will reinforce your understanding of statistical inference concepts and improve your ability to apply them.
Browse courses on Statistical Inference
Show steps
  • Find practice problems
  • Solve problems independently
  • Check your answers
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on R programming
Following tutorials on R programming will help you develop proficiency in the language and improve your ability to use it for data analysis.
Browse courses on R Programming
Show steps
  • Follow tutorials step-by-step
  • Find tutorials that cover relevant topics
  • Practice using the techniques you learn
Create a cheat sheet of statistical formulas and concepts
Creating a cheat sheet will provide you with a quick reference to key statistical formulas and concepts.
Show steps
  • Gather statistical formulas and concepts
  • Organize the formulas and concepts into a cheat sheet
  • Review the cheat sheet regularly
Participate in a study group with other students
Participating in a study group will allow you to collaborate with other students, discuss concepts, and improve your understanding.
Browse courses on Statistical Analysis
Show steps
  • Find a study group or organize your own
  • Attend study group meetings regularly
  • Contribute to the discussions and ask questions
Create a data visualization portfolio
Creating a portfolio of data visualizations will allow you to showcase your skills and understanding of data analysis concepts.
Browse courses on Data Visualization
Show steps
  • Gather and select data
  • Choose appropriate visualization techniques
  • Create and refine visualizations
  • Compile and present your portfolio

Career center

Learners who complete Basics of Statistical Inference and Modelling Using R will develop knowledge and skills that may be useful to these careers:
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. This course provides a strong foundation in statistical inference and modeling, which are essential skills for Statisticians. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for statistical analysis.
Machine Learning Engineer
Machine Learning Engineers use statistical methods to develop and implement machine learning models. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Machine Learning Engineers. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for machine learning.
Business Analyst
Business Analysts use statistical methods to analyze data and make recommendations to improve business operations. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Business Analysts. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for business analysis.
Operations Research Analyst
Operations Research Analysts use statistical methods to optimize business operations. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Operations Research Analysts. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for operations research.
Data Scientist
Data Scientists use a variety of techniques, including statistical inference and modeling, to extract insights from data. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Data Scientists. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for data science.
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Data Analysts. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for data analysis.
Risk Analyst
Risk Analysts use statistical methods to assess and manage risk. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Risk Analysts. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for risk analysis.
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze financial data and make investment decisions. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Quantitative Analysts. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for quantitative analysis.
Market Researcher
Market Researchers collect, analyze, and interpret data to help businesses understand their customers and make informed decisions. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Market Researchers. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for market research.
Health Economist
Health Economists use statistical methods to analyze data and make recommendations to improve health outcomes. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Health Economists. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for health economics.
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Actuaries. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for actuarial work.
Financial Analyst
Financial Analysts use financial data to make investment recommendations and advise clients on financial matters. This course provides a strong foundation in statistical inference and modeling, which are essential skills for Financial Analysts. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for financial analysis.
Epidemiologist
Epidemiologists use statistical methods to investigate the causes and distribution of disease. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Epidemiologists. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for epidemiological research.
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Biostatisticians. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for biostatistical analysis.
Software Engineer
Software Engineers use statistical methods to develop and implement software applications. This course provides a solid foundation in statistical inference and modeling, which are essential skills for Software Engineers. The course covers topics such as sampling distribution, parameter estimation, hypothesis testing, and linear regression, which are all valuable tools for software development.

Reading list

We've selected 13 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 Basics of Statistical Inference and Modelling Using R.
Provides a practical introduction to statistical methods using R, the open-source programming language for statistical computing and graphics. It is aimed at students and researchers with a limited background in statistics and no previous experience with R.
Classic text that provides a comprehensive overview of statistical inference. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to regression and multilevel/hierarchical models. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a practical introduction to statistical methods using R. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to statistical methods for psychology. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to probability and statistics for engineers and scientists. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to probability and statistics for computer scientists. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to Bayesian data analysis. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a practical introduction to machine learning for hackers. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to deep learning. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to reinforcement learning. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.
Provides a comprehensive introduction to natural language processing with Python. It covers a wide range of topics, from basic concepts to advanced methods, and is written in a clear and accessible style.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Basics of Statistical Inference and Modelling Using R.
Advanced Statistical Inference and Modelling Using R
Most relevant
Comparative Research Designs and Methods
Most relevant
Design Patterns in C++: Behavioral - Observer to Visitor
Most relevant
Survival Analysis in R for Public Health
Data Science: Linear Regression
Introduction to Analytics Modeling
Getting Started with R in the Microsoft Data Platform
Statistics and R
Comprehensive Linear Modeling with R
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser