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Statistical Analysis 

Using R  


Price: 1500 INR

About Course

SPSS (Social Packages for Social Sciences) is the statistical software by IBM. It is having a wide range of statistical analysis which is required to be done for research work. It can handle complex survey data very efficiently and then can be used for analysis purposes.

Looking at the applicability of the SPSS in the Research area, Stat Modeller comes up with the Certificate Course on SPSS specially designed for Faculties, Researchers and Students.

All our videos are recordings of our live training. We have intentionally kept discussions with participants in the videos to give you a feeling of the live training session.

What will You Learn?

 Introduction to Statistics

 Understanding Data

 Descriptive Statistics

 Introduction to SPSS

 Creating Files in SPSS

Univariate and Bivariate Frequency Analysis 

Descriptive Analysis using SPSS 

 Importance of Data Visualization

 Data Visualization Techniques (Bar Chart, Cluster Bar Chart, Histogram, Boxplot, Pie Chart, Scatter Plot)

 Introduction to Hypothesis Testing

One Sample t-test, Two Sample t-test, Paired Sample t-test

ANOVA

Chi-Square Test

Simple Linear Regression

Multiple Linear Regression


Material Includes

  •   10+ Hours of Videos
  •   Exercise Files
  •  Handouts
  •  Unlimited Watch Time for 1 Year
  •  Verifiable e-Certificate

Requirements

 Anyone can Join

 Computer/ Laptop

 Devote time to perform tasks given during the sessions

Benefits

 Suitable for both beginners

 Experienced faculty

 Hands-on experience

 Email support

Meet your Instructor

Hiren Kakkad

CEO & Founder

Stat Modeller

  •  More than 12 years of industrial experience
  •  Awardee of the Training Quality Excellence Award by IQAC and ISTD
  •  Certified Trainer by NSDC & Skill India
  •  Certified by Google Analytics Academy for Data Studio
  •  Certified Lean Six Sigma Black Belt
  •  Trained 12000+ participants
  •  Guided 150+ Improvement projects
  •  Assisted 50+ Research Projects
  •  Trainer for in Data Analysis, R, Python, SPSS, Minitab, Power BI, Excel, Advanced Excel, Six Sigma, TPM, Kaizen, 5-S, Kanban etc.

Get your Verifiable Certificate

Strengthen Your Professional Profile with a Verifiable Certificate

Course Curriculum

Introduction to R
  • Trainer Introduction
  • Course Content
  • Why Use R?
  • Introduction to R
  • Install R and R studio
  • Navigating the R Console and R studio Interface
  • Exploring Help Functions and Managing Packages in R
  • Understanding the Global Environment in R
  • Generic Functions (mean, hist.)
  • R Language Fundamentals
  • Unique Features
  • Understanding Data Types in R
  • Data Structures in R
  • Creating Vectors in R Studio: Practical
  • Creating Matrix in R Studio: Practical
  • Creating Array in R Studio: Practical
  • Creating List in R Studio: Practical
  • Case Study: Practical in R Studio: Part 2
  • Creating Data Frame in R Studio: Practical
Importing Data into R from Various Sources
  • What We’ve Learned: A Recap
  • Set a Working Directory and Read Text Files in R: Practical
  • Understanding Functions in R
  • Using Arguments to Filter Data in R
  • Filter Data with Conditional Operators in R
  • Read CSV File in R: Practical
  • Benefits of a Working Directory in R
  • Install and Understand Excel Packages in R
  • Read Excel FIle in R: Practical
Descriptive Statistics
  • Introduction to Statistics: Concepts and Examples
  • Understanding Scales of Measurement in Statistics
  • Understanding Scales of Measurement with Examples and Their Importance: Part 1
  • Understanding Scales of Measurement with Examples: Part 2
  • Basic Concepts in Statistics (Mean, Median, Mode): Part 1
  • Basic Concepts in Statistics (Mean, Median, Mode): Part 2
  • Basic Concept in Statistics (Mean, Median, Mode): Part 3
  • Measurement of Spread (Range, Std. Dev): Part 1
  • Measurement of Spread (Coefficent of Variation): Part 2
  • Understanding Descriptive Statistics with Practical Examples
  • Case Study: Practical in R Studio: Part 1
  • Case Study: Practical in R Studio: Part 2
Data Visualization in R
  • Data Visualization: Turning Data into Insights
  • Types of Charts
  • Data Visualization (Bar Chart)
  • Data Visualization (Clustered Bar Chart)
  • Data Visualization (Pie Chart)
  • Data Visualization (Histogram)
  • Data Visualization (Box Plot)
  • Understanding Shapes of Histrogram
  • Data Visualization (Scatter Plot)
  • Introduction to GGPLOT 2 Package
  • Getting Started with GGplot2 Package: Installation
  • Data Visualization with GGplot2 (Histogram)
  • Installation of plotly Package and Data Visualization (Histogram)
  • Data Visualization using GGPLOT ( Bar Chart)
  • Data Visualization using GGPLOT (Cluster Bar Chart)
  • Data Visualization using GGPLOT (Box Plot)
  • Data Visualization using GGPLOT (Scatter Plot)
Hypothesis Testing
  • Introduction to Testing of Hypothesis
  • Normal Distribution
  • Types of Error in Testing
  • Explaintion of Type I and Type II Error with Example
  • p-Value
  • Types of Test
  • Chi-Square test for Association
  • Test for means: Parametric Tests and Types
  • Assumption of One Sample t - test
  • Case Study: One Sample t-test
  • Assumption and Case study: Independent Sample t-test
  • Assumption and Case Study: Paired Sample t-test
  • ANOVA Test and Assumption
  • Case Study: ANOVA Assumption (Part 1)
  • Case Study: ANOVA Assumption (Equality of Variance) (Part 2)
  • Case Study: Performed ANOVA
  • Case Study: Pairwise Comparision (Part 1)
  • Case Study: Pairwise Comparision (Part 2)
  • Case Study: Pairwise Comparision (Part 3)
Non-Parametric Hypothesis Tests
  • Non-Parametric Tests: When to Use and Types
  • Wilcoxon Signed Rank Test
  • Wilcoxon Signed Rank Test for Two Sample
  • Wilcoxon Signed Rank Test for Paired Sample
  • Kruskal Wallis Test
Linear Regression
  • Understanding Regression Analysis
  • Simple Linear Regression
  • Understanding Regression with an example
  • Assumption of Linear Regression
  • Case Study: Simple Linear Regression
  • Fit the Model
  • Create Plots
  • Multiple Linear Regression: Part 1
  • Multiple Linear Regression: Part 2
  • Logistic Linear Regression
  • Case Study: Logistic Linear Regression
  • Factor Analysis
  • Puropse of Factor Analysis
  • Important Terminology
  • 6 Steps to Perform Factor Analysis: 1. Formulate the Problem
  • 2. Construct Correlation Matrix
  • 3. Determine the Method of Factor Analysis
  • 4. Determine the number of Factor
  • 5. Rotate Factors
  • 6. Interpret the Factor
  • Export analysis as a Report
Final Exam
  • Exam Instruction
  • Final Exam
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