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Arimoro Olayinka Imisioluwa
Welcome to this project-based course Performing Confirmatory Data Analysis in R. In this project, you will learn how to perform extensive confirmatory data analysis, which is similar to performing inferential statistics in R.
Read more
Welcome to this project-based course Performing Confirmatory Data Analysis in R. In this project, you will learn how to perform extensive confirmatory data analysis, which is similar to performing inferential statistics in R.
Read more
Welcome to this project-based course Performing Confirmatory Data Analysis in R. In this project, you will learn how to perform extensive confirmatory data analysis, which is similar to performing inferential statistics in R.
By the end of this 2-hour long project, you will understand how to perform chi-square tests, which includes, the goodness of fit test, test for independence, and test for homogeneity. Also, you will learn how to calculate correlation for numeric variables and perform regression analysis. Also, you will learn how to interpret the results of a test and make viable decisions. By extension, you will learn how to explore some built-in R datasets to perform the different tests.
Note, you do not need to be a data scientist or statistical analyst to be successful in this guided project, just a familiarity with basic statistics and performing hypothesis test in R suffice for this project. A fundamental prerequisite is having a good understanding of the theory of hypothesis test. So, I recommend that you should take the Hypothesis Testing in R project before taking this project.
Register for this course and see more details by visiting:
OpenCourser.com/course/rshq5x/performing
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What's inside
Syllabus
Project Overview
Welcome to this project-based course Performing Confirmatory Data Analysis in R. In this project, you will learn how to perform extensive confirmatory data analysis, which is similar to performing inferential statistics in R. By the end of this 2-hour long project, you will understand how to perform chi-square tests, which includes, the goodness of fit test, test for independence, and test for homogeneity. Also, you will learn how to calculate correlation for numeric variables and perform regression analysis. Also, you will learn how to interpret the results of a test and make viable decisions. By extension, you will learn how to explore some built-in R datasets to perform the different tests. Note, you do not need to be a data scientist or statistical analyst to be successful in this guided project, just a familiarity with basic statistics and performing hypothesis test in R suffice for this project. A very important prerequisite is having a good understanding of the theory of hypothesis test. So, I recommend that you should take the Hypothesis Testing in R project before taking this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines inferential statistics in R, which is highly relevant to data analysis roles
Develops skills in performing chi-square tests, including goodness of fit, test for independence, and test for homogeneity
Teaches how to calculate correlation for numeric variables and perform regression analysis
Taught by an instructor who is recognized for their work in data analysis
Advises students to have a good understanding of the theory of hypothesis testing as a prerequisite
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Reviews summary
Confirmatory data analysis workshop
learners say this online course in Confirmatory Data Analysis features a coursework project in R. While one learner wishes that the audio quality and explanations were better, another learner states that this course is good and requires a background in Hypothesis Testing, Descriptive Statistics, and Regression Analysis.
Includes a project.
"A good coursework project in Confirmatory Data Analysis with R programming."
May require prerequisite knowledge.
"It requires an introductory background in Hypothesis Testing, Descriptive Statistics, and Regression Analysis."
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 Performing Confirmatory Data Analysis in R with these
activities:
Review basic statistics and the R programming language
Show steps
Refreshes the basic statistical concepts and R programming skills necessary for success in this course.
Browse courses on
Hypothesis Testing
Show steps
-
Review statistical concepts such as hypothesis testing, confidence intervals, and regression analysis.
-
Install and familiarize yourself with the R programming environment, including loading data, manipulating dataframes, and visualizing data.
Explore online tutorials on confirmatory data analysis
Show steps
Provides additional exposure to confirmatory data analysis techniques and reinforces understanding of course concepts.
Browse courses on
Confirmatory Data Analysis
Show steps
-
Search for reputable online tutorials on confirmatory data analysis.
-
Follow along with the tutorials, hands-on, to practice performing chi-square tests, calculating correlation, and conducting regression analysis.
-
Experiment with different datasets and scenarios to deepen your understanding.
Review the book 'Statistical Methods for Psychology' by David C. Howell
Show steps
Provides additional exposure to confirmatory data analysis concepts and real-world applications in psychology.
View
Fundamental Statistics for the Behavioral...
on Amazon
Show steps
-
Read the relevant chapters on hypothesis testing, chi-square tests, correlation analysis, and regression analysis.
-
Take notes and summarize key concepts.
-
Complete the practice problems and exercises in the book.
Two other activities
Expand to see all activities and additional details
Show all five activities
Complete practice problems on confirmatory data analysis
Show steps
Reinforces course material and builds proficiency in performing confirmatory data analysis techniques.
Browse courses on
Hypothesis Testing
Show steps
-
Obtain practice problems from textbooks, online resources, or the course instructor.
-
Solve the problems independently, step by step, showing all your work.
-
Check your answers against provided solutions or consult with the instructor for feedback.
Participate in a data analysis competition or hackathon
Show steps
Challenges students to apply their skills in a competitive environment and encourages teamwork and problem-solving.
Show steps
-
Identify a relevant data analysis competition or hackathon.
-
Form a team or work independently.
-
Gather and analyze data, develop models, and present your findings.
Review basic statistics and the R programming language
Show steps
Refreshes the basic statistical concepts and R programming skills necessary for success in this course.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Review statistical concepts such as hypothesis testing, confidence intervals, and regression analysis.
- Install and familiarize yourself with the R programming environment, including loading data, manipulating dataframes, and visualizing data.
Explore online tutorials on confirmatory data analysis
Show steps
Provides additional exposure to confirmatory data analysis techniques and reinforces understanding of course concepts.
Browse courses on
Confirmatory Data Analysis
Show steps
Show steps
Show steps
- Search for reputable online tutorials on confirmatory data analysis.
- Follow along with the tutorials, hands-on, to practice performing chi-square tests, calculating correlation, and conducting regression analysis.
- Experiment with different datasets and scenarios to deepen your understanding.
Review the book 'Statistical Methods for Psychology' by David C. Howell
Show steps
Provides additional exposure to confirmatory data analysis concepts and real-world applications in psychology.
View
Fundamental Statistics for the Behavioral...
on Amazon
Show steps
Show steps
Show steps
- Read the relevant chapters on hypothesis testing, chi-square tests, correlation analysis, and regression analysis.
- Take notes and summarize key concepts.
- Complete the practice problems and exercises in the book.
Two other activities
Expand to see all activities and additional details
Show all five activities
Complete practice problems on confirmatory data analysis
Show steps
Reinforces course material and builds proficiency in performing confirmatory data analysis techniques.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Obtain practice problems from textbooks, online resources, or the course instructor.
- Solve the problems independently, step by step, showing all your work.
- Check your answers against provided solutions or consult with the instructor for feedback.
Participate in a data analysis competition or hackathon
Show steps
Challenges students to apply their skills in a competitive environment and encourages teamwork and problem-solving.
Show steps
Show steps
Show steps
- Identify a relevant data analysis competition or hackathon.
- Form a team or work independently.
- Gather and analyze data, develop models, and present your findings.
Career center
Learners who complete Performing Confirmatory Data Analysis in R will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the field of healthcare. This course in Performing Confirmatory Data Analysis in R aligns perfectly with the responsibilities of Biostatisticians, as it provides a comprehensive understanding of statistical testing procedures. By gaining proficiency in these techniques, learners can analyze medical data effectively, evaluate treatment outcomes, and contribute to the development of new therapies. This course enhances the analytical capabilities of Biostatisticians, enabling them to make informed decisions and advance the field of healthcare.
Statistician
Statistician
Statisticians are highly sought after in fields such as research, healthcare, and finance, where they apply statistical methods to analyze data, draw inferences, and make predictions. This course in Performing Confirmatory Data Analysis in R aligns perfectly with the core responsibilities of a Statistician. It provides a comprehensive understanding of statistical testing procedures, enabling learners to conduct rigorous data analysis, interpret results accurately, and communicate findings effectively. By enhancing their statistical expertise, learners can excel in this challenging and rewarding field.
Data Analyst
Data Analyst
Data Analysts play a crucial role in various industries by examining data to uncover patterns, trends, and insights. This course in Performing Confirmatory Data Analysis in R provides a solid foundation for aspiring Data Analysts by equipping them with the skills to perform advanced statistical tests, interpret results, and make informed decisions based on data. By mastering these techniques, learners can enhance their analytical capabilities and contribute effectively to data-driven decision-making processes within organizations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts leverage statistical models and data analysis techniques to assess risk, make investment decisions, and develop trading strategies in the financial industry. This course in Performing Confirmatory Data Analysis in R aligns closely with the core responsibilities of Quantitative Analysts. It provides a strong foundation in statistical testing procedures, enabling learners to analyze financial data effectively, identify trends, and make informed decisions. By enhancing their statistical expertise, learners can gain a competitive edge in this highly demanding field.
Data Scientist
Data Scientist
Data Scientists combine statistical knowledge with programming skills to extract valuable insights from data. This course in Performing Confirmatory Data Analysis in R complements the skillset of aspiring Data Scientists by providing a deep understanding of statistical testing procedures. By mastering these techniques, learners can enhance their ability to analyze complex datasets, develop predictive models, and communicate results effectively. This course serves as a valuable asset for individuals seeking to advance their careers in the rapidly growing field of Data Science.
Actuary
Actuary
Actuaries assess risk and uncertainty in various fields, including insurance, finance, and healthcare. This course in Performing Confirmatory Data Analysis in R provides a strong foundation for aspiring Actuaries by developing their understanding of statistical testing procedures. By mastering these techniques, learners can analyze data effectively, evaluate risks accurately, and make sound financial decisions. This course empowers Actuaries to excel in their profession and contribute to the stability of the financial system.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data and test economic theories. This course in Performing Confirmatory Data Analysis in R complements the skillset of aspiring Econometricians by providing a deep understanding of statistical testing procedures. By mastering these techniques, learners can analyze economic data effectively, identify trends, and develop predictive models. This course serves as a valuable asset for individuals seeking to advance their careers in the field of Econometrics.
Epidemiologist
Epidemiologist
Epidemiologists investigate the patterns and causes of diseases in populations. This course in Performing Confirmatory Data Analysis in R aligns well with the responsibilities of Epidemiologists, as it provides a solid foundation in statistical methods for data analysis. By gaining proficiency in these techniques, learners can contribute to disease surveillance, outbreak investigations, and the development of public health policies. This course enhances the analytical capabilities of Epidemiologists, enabling them to make informed decisions and protect the health of communities.
Market Researcher
Market Researcher
Market Researchers play a vital role in understanding consumer behavior, market trends, and competitive landscapes. This course in Performing Confirmatory Data Analysis in R equips learners with the skills to analyze market data effectively, identify patterns, and draw meaningful conclusions. By gaining proficiency in statistical techniques, learners can contribute to data-driven decision-making, product development, and marketing strategies, enhancing their competitiveness in the field of Market Research.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts apply mathematical and statistical techniques to optimize business processes and improve efficiency. This course in Performing Confirmatory Data Analysis in R provides a strong foundation for aspiring Operations Research Analysts by developing their understanding of statistical testing procedures. By mastering these techniques, learners can analyze data effectively, identify areas for improvement, and develop data-driven solutions. This course empowers Operations Research Analysts to make a significant impact on organizational performance.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business stakeholders and technical teams by translating business requirements into technical specifications. This course in Performing Confirmatory Data Analysis in R provides Business Analysts with a valuable skillset for data-driven decision-making. By mastering statistical techniques, learners can analyze business data effectively, identify areas for improvement, and communicate insights clearly to stakeholders. This course empowers Business Analysts to make a significant impact on organizational performance.
Data Engineer
Data Engineer
Data Engineers design and manage data infrastructure and ensure the availability, reliability, and security of data. This course in Performing Confirmatory Data Analysis in R may be useful for Data Engineers who need to analyze data to identify trends, patterns, and anomalies. By gaining proficiency in statistical techniques, Data Engineers can enhance their ability to optimize data pipelines and improve the efficiency of data processing systems.
Risk Manager
Risk Manager
Risk Managers assess and manage risks within organizations. This course in Performing Confirmatory Data Analysis in R may be useful for Risk Managers who need to analyze data to identify and quantify risks. By gaining proficiency in statistical techniques, Risk Managers can enhance their ability to develop and implement risk management strategies.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While not directly related to the field of statistics, this course in Performing Confirmatory Data Analysis in R may be useful for Software Engineers who work on data-intensive applications or who need to analyze data to improve software performance. By gaining proficiency in statistical techniques, Software Engineers can enhance their ability to design and implement data-driven solutions.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investment opportunities. While not directly related to the field of statistics, this course in Performing Confirmatory Data Analysis in R may be useful for Financial Analysts who need to analyze financial data to identify trends, patterns, and risks. By gaining proficiency in statistical techniques, Financial Analysts can enhance their ability to make informed investment decisions.
For more career information including salaries, visit:
OpenCourser.com/course/rshq5x/performing
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
Performing Confirmatory Data Analysis in R.
Provides a comprehensive overview of statistical methods used in psychology, including confirmatory data analysis techniques. It covers topics such as hypothesis testing, chi-square tests, correlation, and regression analysis.
Provides a comprehensive and rigorous treatment of statistical inference. It covers topics such as point estimation, hypothesis testing, and Bayesian inference.
Save
Provides an in-depth treatment of advanced R topics. It covers topics such as object-oriented programming, data structures, and parallel computing.
Provides a unique and comprehensive overview of statistics. It covers topics such as probability, statistical inference, and statistical modeling.
Provides a comprehensive introduction to causal inference. It covers topics such as graphical models, structural equation modeling, and counterfactual analysis.
Provides a practical introduction to machine learning using R. It covers topics such as data preprocessing, model selection, and model evaluation.
Save
Provides a comprehensive introduction to R for data science. It covers topics such as data manipulation, statistical analysis, and graphical presentation.
Save
Provides a collection of recipes for solving common problems in R. It covers topics such as data manipulation, statistical analysis, and graphical presentation.
Provides a practical guide to using R for data analysis in the social sciences. It covers topics such as data manipulation, statistical analysis, and graphical presentation.
Provides a comprehensive introduction to statistical learning methods, including supervised and unsupervised learning. It covers topics such as linear regression, logistic regression, and decision trees.
Provides a comprehensive introduction to Bayesian data analysis. It covers topics such as Bayesian probability, hierarchical models, and Markov chain Monte Carlo methods.
Provides a comprehensive and practical guide to R programming. It covers topics such as data structures, control flow, and debugging.
Provides a practical introduction to machine learning for people with no prior experience. It covers topics such as supervised learning, unsupervised learning, and natural language processing.
Provides a practical introduction to deep learning for people with no prior experience. It covers topics such as convolutional neural networks, recurrent neural networks, and transfer learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rshq5x/performing
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Good to know
Examines inferential statistics in R, which is highly relevant to data analysis roles
Develops skills in performing chi-square tests, including goodness of fit, test for independence, and test for homogeneity
Teaches how to calculate correlation for numeric variables and perform regression analysis
Taught by an instructor who is recognized for their work in data analysis
Advises students to have a good understanding of the theory of hypothesis testing as a prerequisite
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