Data science tools: Statistical Hypothesis Testing-1

Data science tools: Statistical Hypothesis Testing-1, available at $19.99, has an average rating of 5, with 43 lectures, 1 quizzes, based on 28 reviews, and has 1125 subscribers.

You will learn about How to formulate and conduct statistical hypothesis test This course is ideal for individuals who are Data science, Machine learning, business, statistics It is particularly useful for Data science, Machine learning, business, statistics.

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Summary

Title: Data science tools: Statistical Hypothesis Testing-1

Price: $19.99

Average Rating: 5

Number of Lectures: 43

Number of Quizzes: 1

Number of Published Lectures: 43

Number of Published Quizzes: 1

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to formulate and conduct statistical hypothesis test

Who Should Attend

Data science, Machine learning, business, statistics

Target Audiences

Data science, Machine learning, business, statistics

Hypothesis testing is one of the most important concepts in statistics, especially in inferential statistics. The basis of the statistical hypothesis test and different terminologies (p-value, level of significance, type 1 and type 2 errors)will be explained elaborately. Students will be capable to infer a population mean, proportion, differences between means or proportions, and the relationships between variables and many others. The students will come to know the process of formulating and conducting the hypothesis test step by step. They will gain an insight view of different types of a statistical hypothesis tests. First of all, students will get basic ideas about normal distribution, which is the basis of all the statistical tests and the most widely used distribution too. Along with the normal distribution, they get knowledge about an empirical rule. They will be able to distinguish between the t-test and z-test. This course also includes the test for qualitative data, which is the chi-square test. The course will lay the foundation for the advanced level of a statistical hypothesis test. It will be very helpful to understand and infer the different models and algorithms in data science and machine learning. Specially, those who are interested to advance their careers in data science and machine learning should complete the course

Course Curriculum

Chapter 1: Introduction

Lecture 1: 1.1-Introduction to the course

Lecture 2: 1.2-Insrtructor

Lecture 3: 1.3-what is statistical hypothesis testing

Lecture 4: 1.4-Learning outcomes of the course

Chapter 2: Definition and basic terminologies

Lecture 1: 2.1-defination of statistical hypothesis

Lecture 2: 2.2-Steps of hypothesis testing: null hypothesis and alternative hypothesis

Lecture 3: 2.3-Continue-Steps of hypothesis testing-more-steps

Lecture 4: 2.4-Criteria of a hypothesis-test-type-I and type-II errors

Lecture 5: 2.5-Example-type-I and type-II errors

Lecture 6: 2.6-Statistical significant

Lecture 7: 2.7-Understanding p-value

Lecture 8: 2.8-p-value example

Lecture 9: 2.9-type of statistical tests

Lecture 10: 2.10-conditions for hypothesis test

Chapter 3: z-test or normal test for one sample mean

Lecture 1: 3.0-Normal distribution

Lecture 2: 3.1-introduction-z-normal-test-

Lecture 3: 3.2-One Sample z-Test for the Population Mean-basics

Lecture 4: 3.3-One Sample z-Test-Population Mean-TwoTailedexample-1

Lecture 5: 3.4-One Sample z-Test-Population Mean–OneTailed-example-2

Lecture 6: 3.5-One Sample z-Test-Population Mean-More-examples

Chapter 4: One Sample z-test for sample proportion

Lecture 1: 4.1-One Sample z-test-proportion-TwoTailed-example1

Lecture 2: 4.2-p-value-z-test-proportion-TwoTailed-example1-continue

Lecture 3: 4.3-One Sample z-test-proportion-more-examples

Chapter 5: z-test for two samples mean and proportion

Lecture 1: 5.1-z-test-two-samples-mean-two-tailed

Lecture 2: 5.2-z-test-two-samples-proportion-two-tailed

Chapter 6: t-test-one-sample-mean

Lecture 1: 6.1-t-test-basics-conditions

Lecture 2: 6.2-t-test-one-sample-mean-two-tailed

Lecture 3: 6.3-continue-t-test-one-sample-mean-two-tailed-p-value

Lecture 4: 6.4-t-test-one-sample-mean-one-tailed

Chapter 7: t-test for two samples means

Lecture 1: (7.1) t-test-two-samples-independent-basics

Lecture 2: 7.2-t-test-two-samples-independent-example

Lecture 3: 7.3-t-test-two-samples-paired-basics

Lecture 4: 7.4-t-test-two-samples-paired-example

Chapter 8: Chi-square test of goodness-of-fit and independence

Lecture 1: 8.1-chi-square-test-basics

Lecture 2: 8.2-chi-square-goodness-of-fit-example-1

Lecture 3: 8.3-chi-square-goodness-of-fit-more-examples

Lecture 4: 8.4-chi-square-test-of-independence-example-1

Lecture 5: 8.5-chi-square-test-of-independence-example-2

Chapter 9: Analysis of variance (ANOVA)

Lecture 1: 9.1-introduction-ANOVA-basics

Lecture 2: 9.2-ANOVA-oneway-example-1

Lecture 3: 9.3-ANOVA-oneway-more-examples

Chapter 10: Bonus lecture

Lecture 1: Bonus lecture

Lecture 2: Binomial distribution

Instructors

Mohammad Rafiqul Islam
Statistician and Datascientist

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1 stars: 1 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 2 votes
5 stars: 25 votes

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