Data Science- Hypothesis Testing Using Minitab and R
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Data Science- Hypothesis Testing Using Minitab and R

Introduction to Hypothesis Testing, Performing Parametric and non parametric tests, Analysis of Variance
4.8 (14 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
903 students enrolled
Created by ExcelR Solutions
Last updated 4/2017
English
Current price: $10 Original price: $30 Discount: 67% off
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Includes:
  • 4 hours on-demand video
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Formulate Null and alternative hypothesis statement; perform Hypothesis testing techniques for various output and input types; Have an understating of "Analysis of Variance"
View Curriculum
Requirements
  • It is recommended (though not mandated) that participants have clear understanding of high school mathematics and basic statistics
Description

Data Science - Hypothesis Testing using Minitab, R is designed to cover majority of the capabilities from Analytics & Data Science perspective, which includes the following:

  • Learn how to formulate a hypothesis statements with business context
  • Preform 2 sample t test for continuous output with discrete inputs in 2 categories
  • Using ANOVA for continuous outputs with more than 2 discrete categories of inputs
  • Performing 2 – proportion test for discrete output and discrete inputs in 2 categories
  • The Chi – Square test for discrete outputs with multiple discrete inputs 
  • Introduction to Non Parametric tests like: Mann Whitney test, Paired T test, Moods median Test and Tukey pairwise comparisons  
  • Learn about Hypothesis testing techniques & how to accomplish the same using R and Minitab
Who is the target audience?
  • Participants who want to take up a career in Data Analytics, Quality management and other allied fields
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Curriculum For This Course
24 Lectures
03:56:01
+
Hypothesis Testing
4 Lectures 28:30

Describes about course content and route map for the entire course


Preview 02:13

The cardinal ‘formula’ for a data scientist; The outcomes relating to Null Hypothesis and Alternate Hypothesis

Preview 11:02

Null Hypothesis statement and Alternate hypothesis statement; formulating them with some examples

Hypothesis Testing Formulation
10:59

The difference between one tail and two tail in a distribution curve; The four main types of tests and the testing flowpath. Performing the sample t - test using a case study

Hypothesis Testing- Various Types of Tests
04:16
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Hypothesis Testing -Parametric Using Minitab
16 Lectures 02:48:58

Identification of the input and output variable of interest; Types of tests to for various combinations of Y and X; Flow path to determine the test to be conducted based on the input and output parameters; Parametric tests for normal data and non parametric tests for non normal data; Understanding the business problem; Identifying the inputs and output variables of interest in the case study and choosing the appropriate test;

1 Sample Z- Test Part -1
08:28

Formulating the Null and the alternate hypothesis for normality test; Choice of null hypothesis based on absence of action and the vice versa for alternate hypothesis; checking for normality in Minitab; interpreting the Q–Q plot; Comparing the computed ‘p’ value with α (alpha) for taking the decision on whether or not to take the action; Step to performing the 1 sample Z test, selection of appropriate hypothesis in minitab. Formulation of the conclusion statement at the end of the test

1 Sample Z Test Part-2
13:13

Perform check test on data for normality; selection of the appropriate test to be conducted based on conditions of data;

1 Sample T Test
15:36

Performing Non parametric test for non normal data; 1 sample Sign test for one sample using population median measure

1 Sample Sign Test
06:26

Understanding what external conditions are, in an experiment; identifying the existence of external conditions given various scenarios

Paired T Test
11:59

Defining the Null and Alternate hypothesis statements for the given case study; defining the Ho and Ha for the various comparative tests that are a part of the t - test, Using minitab.

2 Sample T-Test Part-1
18:13

Conducting the comparative tests viz Normality test, Equal variance test using Minitab

2 Sample T-Test Part-2
07:36

Learn the hypothesis statements for 2 sample t - test and the 2 sample t - test using minitab, Iterative hypothesis testing

2 Sample T-Test Part-3
08:50

Formulating the null and the alternate hypothesis statements based on the test flowpath; conduct of Normality test for all samples

One Way ANOVA Part-1
08:33

Learn how to conduct Normality test for all samples, Variance test for more than 2 populations

One Way ANOVA Part-2
18:13

Formulation of Null and alternate hypothesis statement for ANOVA test for comparisons; Conduct of one way ANOVA using Minitab.

One Way ANOVA Part-3
03:16

The distinction between one way ANOVA, Two Way ANOVA and Multiple ANOVA or MANOVA.

ANOVA-1,2, Multiple Way
02:54

Application of 2 proportion test based on the output & input data types; Formulating the hypothesis statements; Iterative testing of 2 proportion test

2 Proportion Test
11:54

Understanding the business problem, Identifying the Y and X and their data types; Choosing the appropriate tests based on the flow path diagram; Perform Normality test using ‘graphical summary’ option in Minitab; Performing one way Anova and reconciling the results with the Hypothesis statement;

Tukey Pairwise Comparisons Part-1
15:33

Selecting all the possible graphical outputs in Minitab for analysis; Using Tukey test for comparisons of means; Interpreting the Tukey pair wise comparison based on ‘letter’ sharing method; Interpreting residual plots, Interval plot, Individual plot and box plot, Inferring the sample with the highest mean and lowest mean based on the difference measures

Tukey Pairwise Comparisons Part-2
13:15

Recap of the various types of tests based on the data types of X and Y, understand the purpose of using hypothesis testing for predictive analytics

Recap Of Hypothesis Testing
04:59
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Hypothesis Testing -Non Parametric Using Minitab
4 Lectures 38:33

Choosing correct test based on the number of inputs; Formulating the combined Hypothesis statement for Null and Alternate; Conducting the Chi Square Test using minitab

Chi Square Test
08:52

Perform the check for normality test for 2 samples; choosing the non – parametric test of comparisons of medians when the data is found to be not normal

Mann Whitney Test
10:59

Checking for normality test; performing the paired T test when the external conditions are the same; formulating the Null and the alternative hypothesis statements;

Paired T Test Assumption
09:19

Understanding the logic behind the mood’s median test; Mood’s Median Vs Kruskal Wallis where to use; Performing both the tests and comparing the results;

Moods Median Test
09:23
About the Instructor
ExcelR Solutions
4.2 Average rating
675 Reviews
6,963 Students
8 Courses
Pioneer in professional management trainings & consulting

Certifications:

Certified Six Sigma Master Black Belt

Project Management Professional (PMP)

Agile Certified Practitioner (PMI - ACP)

Risk Management Professional (PMI-RMP)

Certified Scrum Master

Agile Project Management – Foundation & Practitioner from APMG

Bharani Kumar is an Alumnus of premier institutions like IIT & ISB with 15+ years professional experience and worked in various MNCs such as HSBC, ITC, Infosys, Deloitte in various capacities such as Data Scientist, Project Manager, Service Delivery Manager, Process Consultant, Delivery Head etc.

He has trained over 1500 professionals across the globe on Business Analytics, Agile, PMP, Lean Six Sigma, Business analytics and the likes.

He has 8 years of extensive experience in corporate, open house and online training.

He is a thorough implementer with abilities in Business Analytics and Agile projects.

He worked in Delivery management focusing on maximizing business value articulation.

He has a comprehensive experience in leading teams and multiple projects.

Quality Management: A thorough implementer with abilities in Quality management focusing on maximizing customer satisfaction, process compliance and business value articulation; comprehensive experience in leading teams & multiple projects. A result-oriented leader with expertise in devising strategies aimed at enhancing overall organizational growth, sustained profitability of operations and improved business performance.

Project Management: Project Management Professional involved in Initiation, Planning, Execution, Monitoring & Controlling and Closing phases of project activities. Devising and implementing project plans within preset budgets and deadlines and managing the projects towards successful delivery of project deliverables and meeting project objectives.

Training: Close to 8 years training experience and conducted multiple trainings in PMP, Agile, Six Sigma, Business Analytics and Process Excellence across the globe. Understands the individual differences of the attendees and possesses excellent training skills and considered as one of the best trainers in his areas of expertise.