Рет қаралды 21
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Significance vs. Confidence Level - Inferential Statistics
Statistics with R Specialization
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
Statistical Inference, Statistical Hypothesis Testing, R Programming
This course is an excellent overview of inferential statistic tests / hypothesis tests and confidence intervals. The organization and material is quite good, with exercises and applications using R.,Awesome. I loved the way this course is done. I know what Test Statistic to use for what type of data and under which conditions. I am preparing a cheat-sheet that will be shared with all later on.
Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give you a brief peek at what's to come in the next two weeks by discussing how the methods we're learning can be extended to other estimators. We will also discuss crucial considerations like decision errors and statistical vs. practical significance. The labs for this week will illustrate concepts of sampling distributions and confidence levels.
Significance vs. Confidence Level - Inferential Statistics
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