Last updated 1/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 486.42 MB | Duration: 1h 15m
Learn the fundamentals of estimation, test of hypothesis, normality test, ANOVA, contingency table Chi square test
What you'll learn
Statistical Estimation
Test Of Hypothesis
Normality Test
One-way ANOVA
Two-way ANOVA
Contingency Table
Chi square Test
Requirements
You will need to have a computer or a mobile handset with an internet connection
Basic knowledge in statistics will be needed
Description
In this Spotle masterclass you will learn:The concepts of statistical estimation, test of hypothesis, normality test, ANOVA, contingency table Chi square test explained through detailed videosTo conduct statistical tests with real-life examplesAll concepts through exercises, solved problemsYour take-aways:A solid foundation in statistical decision making to build a data science career.ANOVA or Analysis of Variance is a group of statistical models to test if there exists a significant difference between means. It tests whether the means of various groups are equal or not. In ANOVA, the variance observed in a variable is partitioned into different components based on the sources of variation. An important fact to note is that while we use ANOVA to find out whether the means differ significantly, we compare the variances, hence the name - Analysis of Variance.While studying test of hypothesis, we have seen how we can compare two means from two population. When we compare two or more than two means, we use ANOVA.ANOVA is easy to compute and can be manually computed using simple algebra rather than complex matrix calculations. This was one of the reasons for its early popularity.Following are the examples where ANOVA can be used.A group of psychiatric patients are trying three different therapies: Counselling, medication and biofeedback. You want to see if one therapy is better than the others.A manufacturer has two different processes to make light bulbs. They want to know if one process is better than the other.Students from different colleges take the same exam. You want to see if one college outperforms the other.
Overview
Section 1: Statistical Inference - Estimation
Lecture 1 Statistical Estimation
Section 2: Test Of Hypothesis
Lecture 2 Test Of Hypothesis
Section 3: Normality Test
Lecture 3 The Basics Of Normality Test
Lecture 4 Test Of Normality - Part 2
Lecture 5 Test Of Normality - Part 3
Section 4: Analysis Of Variance
Lecture 6 What Is ANOVA
Lecture 7 Benefits And Practical Usage Of ANOVA
Lecture 8 One-way ANOVA
Lecture 9 Two-way ANOVA
Section 5: Cross Tabulation And Chi Square Test
Lecture 10 Contingency Table - Part 1
Lecture 11 Contingency Table - Part 2
Anyone who wants to learn the fundamentals of statistical decision making,Anyone who wants to start a career in data science
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