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Basics of Statistical Inference and Modelling Using R

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).

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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.

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.

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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

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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 Basics of Statistical Inference and Modelling Using R with these activities:

Review your notes from previous statistics courses

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Refreshing your knowledge of statistics will help you build a stronger foundation for this course.

Browse courses on Statistics

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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

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Reading this book will provide you with a comprehensive understanding of statistical inference concepts.

View Statistical Inference (Chapman & Hall/CRC Texts... on Amazon

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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

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Solving practice problems will reinforce your understanding of statistical inference concepts and improve your ability to apply them.

Browse courses on Statistical Inference

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Find practice problems
Solve problems independently
Check your answers

Four other activities

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Follow tutorials on R programming

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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

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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

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Creating a cheat sheet will provide you with a quick reference to key statistical formulas and concepts.

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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

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Participating in a study group will allow you to collaborate with other students, discuss concepts, and improve your understanding.

Browse courses on Statistical Analysis

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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

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Creating a portfolio of data visualizations will allow you to showcase your skills and understanding of data analysis concepts.

Browse courses on Data Visualization

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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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Health Economist

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Software Engineer

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.

Introductory Statistics with R

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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.

Introductory Statistics with R (Statistics and...

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Introductory Statistics with R (Statistics and...

Basics of Statistical Inference and Modelling Using R

Two deals to help you save

What's inside

Learning objectives

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