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Descriptive statistics

This section illustrates how data is summarized without the use of pre-conditioned assumptions or pre-built models. It uses numerous examples from the medical field to demonstrate descriptive analysis.

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Univariate statistical plots and usage

This section provides practical examples for utilizing the Jupyter notebook to generate different statistical plots, such as distributional plots. It also provides a detailed explanation for the methodologies used and responses to queries such "why the source data is insufficient." You'll also be taught how to utilize these plots in a wide range of situations.

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Bivariate and multivariate statistics

Bivariate and multivariate statistics are described in this section. It offers as an instance to show how to solve problems when more than one variable is required to arrive at a solution. By contrasting correlations, it also proposes a solution.

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Addition and multiplication rule

The numerous principles for computing probability are explained in this part. The addition and multiplication rules are also discussed, along with examples showing how to solve them.

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Binomial and normal distribution

In addition to offering examples with solutions, this section examines the application of the binomial distribution for discrete issues and the normal distribution for continuous functions.

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Poisson probability function

The probability quality control idea is covered in this section. The Poisson probability function's various uses and functionalities are covered.

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Types of Data

By assisting you in understanding their graphical representations, this part introduces you to several kinds of data. Further, it describes each of its parts.

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Basics of Statistics

This section provides a basic explanation of statistics and how it relates to data. The basic concepts for working with data, such as creating problems, obtaining data, and finding a solution with a real-world example, are also covered, as well as the job of a statistician within an organisation.

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Measures of Central Tendency

The measure of central tendency in descriptive statistics describes the average or median value of the given data set. It analyses data sets using graphs and tables.

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Measures of Dispersion

This section explains the standard deviation using a formula to get a solution. Through the derived observations, it depicts the relative trend towards the best accurate solution. To better grasp this, you will also be shown how to interact with code in the Jupyter notebook. In the later portion of this lesson, you will also learn how to visually portray the observation, data, and metadata.

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Bayes theorem

The history of the Bayes theorem is discussed in this section. By resolving the previously modelled problems, it then proceeds to explain the theorem. It also clarifies the different assumptions and theories that underlie the theorem.

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Marginal Probability

You will become familiar with marginal probability and its characteristics. Further on assistance in your comprehension of the idea, the section also offers a solution when the condition is a margin.

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Introduction to Probability

The common approach to problem solving when there is uncertainty is covered in this section. With the help of examples and solving problems, you will master about probability means and its various concepts, such as empirical probability.

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Case Study for Statistics Data Science

This section covers the Cardio Good Fitness case study and provides a solution utilising the Jupyter notebook to help you study descriptive statistics.

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Understanding distributions and histograms

The Jupyter notebook is going to be used showcase how to plot data in this part by enabling learners understand various data distributions.

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Probability and Machine Learning

With a real-time example, this part demonstrates how a machine interprets the signals and generates a solution for it utilising pre-fed data.

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Test Case for Statistics Data Science

An HIV test case has been used in this section to illustrate how the Bayes theorem works. It explains how a program interprets the steps required to categorize the healthy and affected individuals in the provided dataset.