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Inferential Statistics
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Statistics is one of the most important fundamental components that sees its usage in almost all of the domains that use metrics of sorts. Inferential statistics is a popular division in the world of statistics that deals with making inferences about experiments at hand and/or with trackable metrics. The advantage is this, inferential statistics can process numerical data and categorical data with ease. This makes its usage seep into domains and whip up applications that we never thought of before. Since it is a key component of many other domains and a vital part of statistics itself, we here at Great Learning have come up with this course to help you get started with all the fundamentals and to help you build your skillsets in statistics.
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Course Outline
Statistics is the discipline of science that deals with studying the collection of data, analyzing, interpreting, and presenting empirical data. It explores the variation in the set of data.
Statistics uses many approaches to collect the data for statistical analysis. Direct observation, Experiments, and surveys are the most popular methods to obtain data for statistical analysis. The survey collects data from people through Gallup polls, pre-election polls, marketing surveys, and other forms.
Descriptive Statistical analysis deals with organizing and interpreting data using graphical and numerical methods, such as Inferential Statistical Analysis, Predictive Analysis, Prescriptive Analysis, Casual Analysis, Mechanistic Analysis are the standard statistical analysis techniques used in Descriptive Statistics.
This module starts by explaining what hypothesis testing is. Further, you will learn about two types of hypothesis testing, the process involved in it, and the crucial terms one must know in hypothesis testing in detail.
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