Inferential Statistics Tutorial | Inferential Statistics Using Python | Statistics | Intellipaat
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With this Inferential Statistics Tutorial, we are exactly going to teach you. You will learn about different types of statistics, such as descriptive and inferential statistics, and how to interpret them. We will also cover topics such as hypothesis testing, confidence intervals, and regression analysis. Finally, you will learn how to use Python to perform inferential statistics.
We will begin with understanding What exactly Data is along with its types. Next up we will discuss basic statistical terms such as Mean, Median, Variation, Standard Deviation, etc. Later, we will cover multiple Sampling Techniques like Random, Systematic, and Stratified Sampling, alongside terms like Z-Score and Normal Distribution.
Moving further we will understand how you can configure these Inferential Stats using Python programming language. We will also look into important Statistical Concepts like Confidence Intervals, Hypothesis Testing, One Sample t-tests, and ANOVA. Additionally, we will cover Statistics Interview Questions
✅What is the difference between inferential and descriptive statistics?
Essentially, descriptive statistics state facts and proven outcomes from a population, whereas inferential statistics analyze samplings to make predictions about larger populations.
✅ What is inferential statistics also known as?
Inferential statistical techniques are used to analyze the sample's behavior. These include the models used for regression analysis and hypothesis testing. The first-step sample is used to draw conclusions. Assumptions or predictions about the entire population are used to draw inferences.
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