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Inferential Statistics Free Course

Name: Inferential Statistics
Rating: 4.55 (262 reviews)

Inferential Statistics

4.55 Beginner level 1.5 learning hrs 4.9K+ Learners

Instructor:

Mr. Anirudh Rao

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Skills you will gain

Data Collection

Statistical Analysis

Probability

Central Limit Theorem

Hypothesis Testing

Chi-Square Test

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Key Highlights

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About this course

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

Introduction to Statistics

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.

Data Collection for Statistics

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.

Types of Statistical Analysis

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.

Deep Dive into Inferential Statistics

Probability and Central Limit Theorem

Understanding Hypothesis Testing

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.

Chi Square Test and ANOVA

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Inferential Statistics

4.55

1.5 Hours

Beginner

4.9K+ learners enrolled so far

Get free course content

Master in-demand skills & tools

Test your skills with quizzes

Trusted by 10 Million+ Learners globally

4.6

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4.94

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Learner reviews of the Free Courses

4.55

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MUBASHIR HABIB

5.0

★★★★ ★

“Inferential Statistics: Making Smart Guesses About the Big Picture”

Imagine you want to know the average height of all adults in your city. That's a tall order! You can't measure everyone. So, what do you do? This is where inferential statistics comes in. Instead of measuring everyone, you measure the height of a group of adults (a sample). From this small group, you can make educated guesses (inferences) about the height of all adults in the city. It's like trying to figure out what's in a whole bag of marbles by looking at just a handful. Here's how it works: You take a sample: This is like grabbing a handful of marbles from the bag. You analyze the sample: You look closely at the marbles in your hand, measuring their size, color, etc. You make inferences: Based on what you see in your hand, you make educated guesses about the marbles in the whole bag. But, there's always a bit of uncertainty. Just because the marbles in your hand are mostly blue doesn't mean the whole bag is blue. This is why inferential statistics uses probability to measure how confident you can be in your guesses. Inferential statistics is used everywhere: Scientists use it to test new medicines. Marketers use it to understand customer preferences. Pollsters use it to predict election results. In essence, inferential statistics helps us make informed decisions in a world full of uncertainty.

Sadam Ammar

5.0

★★★★ ★

“Great Course with Clear Explanations and Practical Examples”

This course provided an excellent overview of statistics, with in-depth explanations and engaging examples. The instructor made complex topics easy to understand, and the quizzes helped reinforce the material effectively. I highly recommend it to anyone looking to build a strong foundation in statistics for data analysis.

Sakshi Barge

5.0

★★★★ ★

India

“The best experience I have experienced here”

Understanding Core Concepts: You begin by learning fundamental ideas like sampling distributions, confidence intervals, and hypothesis testing. These provide the foundation for making inferences. Real-World Applications: You explore how inferential statistics is applied in fields like business, healthcare, and social sciences to draw conclusions and make decisions. Hands-On Practice: Using tools like Python, R, or Excel, you perform statistical tests (e.g., t-tests, chi-square tests, ANOVA) on datasets, which enhances your practical skills.

Joshua Senteu

5.0

★★★★ ★

“Engaging and Informative Course..”

I thoroughly enjoyed the clear explanations and real-world applications of concepts in the inferential statistics course. The step-by-step approach to topics like hypothesis testing, confidence intervals, and the Central Limit Theorem made the material easy to grasp. The practical examples and exercises helped me understand how to apply statistical methods to draw meaningful conclusions from data. It was a valuable learning experience, and I feel more confident in my statistical analysis skills!

Maribel Soto-Handy, Ph. D, M.S. SME

5.0

★★★★ ★

United States

“Inferential Statistics: Drawing conclusions”

Inferential Statistics: Drawing Conclusions from Data Inferential statistics is a branch of statistics that uses sample data to conclude about a larger population. It allows us to make inferences or predictions about a population parameter, such as the population mean or proportion.

Lorenzo Hardeman

5.0

★★★★ ★

United States

“learning all the different things that are within inferential statistics”

the differences in the hypothesis of two different types, the different probabilities, central limit theorem. all those were exciting to learn

Muhammad SalekMiah

5.0

★★★★ ★

“Inferential statistics is a branch of statistics that allows us to make conclusions or inferences about a population based on a sample of data. It involves using probabilty.”

Inferential statistics is a branch of statistics that allows us to make conclusions or inferences about a population based on a sample of data. It involves using probability theory to estimate population parameters, test hypotheses, and make predictions. Techniques like confidence intervals, hypothesis testing, and regression analysis are commonly used in inferential statistics to determine the likelihood that a result observed in a sample reflects the true characteristics of the larger population.

RAJISH JOEL V RAYEN URK22CS3026

4.0

★★★ ★ ☆

India

“Exploring Statistical Concepts: A Deep Dive into Data Sampling and Hypothesis Testing"”

I appreciated how the course provided clear explanations and practical examples, especially in understanding complex concepts like the Central Limit Theorem and hypothesis testing. The interactive elements made the learning experience engaging and helped solidify my understanding of key statistical principles.

zubair pasha

5.0

★★★★ ★

India

“Good Learning and Helps to Achieve More Knowledge”

Helps to learn more about statistics and is a certified course.

J STANES JENSON URK22CS2061

5.0

★★★★ ★

India

“Great Learning offers an excellent online course on Inferential Statistics that covers all the fundamental concepts and skills in this branch of statistics.”

The course thoroughly covers the core topics in inferential statistics, including: Central Limit Theorem (CLT) and confidence intervals Hypothesis testing for numerical and categorical data Assumptions and conditions for different inferential methods Choosing the appropriate test statistic based on data type.

Our course instructor

Mr. Anirudh Rao

Machine Learning Expert

780.4K+ Learners

79 Courses

Anirudh has been working in the field of Data Science and has expertise over Python, Machine Learning and other concepts in the field of data analysis. He is also proficient in the concept of Deep Learning and its usage in a production environment. Expertise extends towards working on various projects in the domain of Artificial Intelligence and Neural Networks as well.

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