Statistical Analysis- Intermediate Course

About Course

Overview: This 8-week Statistical Analysis course is designed to equip research scholars and professionals with the necessary skills to conduct thorough and accurate statistical analysis. The course covers a wide range of topics including data collection methods, probability theory, descriptive and inferential statistics, hypothesis testing, regression analysis, ANOVA, multivariate analysis, and the use of SPSS software. Each week will delve into a specific area of statistical analysis, providing both theoretical understanding and practical applications. The course also includes a 1-hour weekly mentoring session to reinforce learning and address individual queries.

Course Topics:

Week 1: Introduction to Statistical Analysis and Data Collection Methods
Week 2: Probability Theory and Distributions
Week 3: Descriptive Statistics: Measures of Central Tendency and Variability
Week 4: Inferential Statistics: Estimation and Hypothesis Testing
Week 5: Correlation and Regression Analysis
Week 6: Analysis of Variance (ANOVA) and Experimental Design
Week 7: Multivariate Analysis Techniques
Week 8: Application of SPSS Software and Final Review

Course Benefits:

Gain a comprehensive understanding of statistical concepts and methodologies.
Learn to apply statistical techniques to real-world research problems.
Develop proficiency in using SPSS for data analysis.
Improve your ability to critically analyze data and draw meaningful conclusions.
Enhance your research capabilities, making your work more rigorous and credible.

Course Objectives:

To provide a strong foundation in statistical theory and its practical applications.
To enable participants to confidently perform statistical analyses and interpret results.
To familiarize participants with the use of SPSS for data analysis.
To foster critical thinking and analytical skills necessary for conducting high-quality research.

Course Outcomes:

Participants will be able to design and execute statistical analyses with confidence.
They will be equipped to handle complex datasets and perform advanced statistical techniques using SPSS.
They will have the skills to effectively communicate statistical findings in research papers and professional reports.
Participants will be better prepared to contribute to academic research or professional projects requiring statistical analysis.

Relevance to Research Scholars and Professionals: This course is particularly beneficial for research scholars and professionals who need to incorporate statistical analysis into their work. Whether you’re conducting original research, analyzing data for business decisions, or validating findings in a scientific study, this course will provide the necessary tools and knowledge. By the end of the course, you’ll be capable of conducting rigorous statistical analyses with SPSS, enhancing the credibility and impact of your work, and making you a more effective researcher or professional in your field.

Fundamental Statistical Concepts: Understand the core principles of statistics, including probability, distributions, and hypothesis testing.
Descriptive and Inferential Statistics: Learn how to summarize and interpret data, and make predictions or inferences based on sample data.
Correlation and Regression Analysis: Gain insights into relationships between variables and how to model these relationships using regression techniques.
Analysis of Variance (ANOVA): Understand how to compare multiple groups and determine if there are significant differences between them.
Multivariate Analysis Techniques: Explore advanced methods to analyze data involving multiple variables simultaneously.
SPSS Software Proficiency: Develop hands-on skills in using SPSS for various statistical analyses, from data input to complex computations.
Data Interpretation and Presentation: Learn how to effectively interpret statistical results and present them in a clear, concise manner.
Application of Statistical Analysis in Research: Apply the statistical techniques learned to real-world research problems, enhancing the credibility and impact of your work.
Critical Thinking and Analytical Skills: Strengthen your ability to critically analyze data and draw meaningful conclusions, essential for high-quality research.
Preparation for Professional and Academic Success: Equip yourself with the statistical tools necessary to excel in research, business, or academic settings.

Course Content

Day 1: Introduction to Statistical Analysis

Day 2: Understanding Data Types and Measurement Scales

Day 3: Data Collection and Sampling Methods

Day 4: Descriptive Statistics: Central Tendency

Day 5: Descriptive Statistics: Variability

Day 6: Training: Descriptive Statistics in Excel

Day 7: Training: Descriptive Statistics in SPSS and Tableau

Day 8: Probability Concepts and Distributions

Day 9: Introduction to Hypothesis Testing

Day 10: One-Sample and Two-Sample Tests

Day 11: Analysis of Variance (ANOVA)

Day 12: Non-parametric Tests

Day 13: Training: Hypothesis Testing in SPSS

Day 14: Training: Hypothesis Testing in Excel and Tableau

Day 15: Correlation and Causation

Day 16: Simple Linear Regression

Day 17: Multiple Regression Analysis

Day 18: Logistic Regression

Day 19: Model Evaluation and Diagnostics

Day 20: Training: Regression Analysis in SPSS

Day 21: Training: Regression Analysis in Excel and Tableau

Day 22: Time Series Analysis: Introduction

Day 23: Moving Averages and Smoothing Techniques

Day 24: ARIMA and Forecasting Models

Day 25: Decomposition of Time Series

Day 26: Model Validation and Evaluation

Day 27: Training: Time Series Analysis in SPSS

Day 28: Training: Time Series Analysis in Excel and Tableau

Day 29: Factor Analysis: Introduction

Day 30: Principal Component Analysis (PCA)

Day 31: Cluster Analysis: Introduction

Day 32: Discriminant Analysis

Day 33: Multivariate Analysis of Variance (MANOVA)

Day 34: Training: Multivariate Analysis in SPSS

Day 35: Training: Multivariate Analysis in Excel and Tableau

Day 36: Statistical Process Control (SPC): Introduction

Day 37: Capability Analysis

Day 38: Six Sigma and Quality Management

Day 39: Reliability Analysis

Day 40: Training: SPC and Quality Management in SPSS

Day 41: Training: Quality Management in Excel and Tableau

Day 42: Introduction to Advanced Statistical Methods

Day 43: Structural Equation Modeling (SEM)

Day 44: Survival Analysis

Day 45: Decision Trees and Random Forests

Day 46: Machine Learning Techniques in Statistics

Day 47: Training: Advanced Statistical Methods in SPSS

Day 48: Training: Advanced Statistical Methods in Excel and Tableau

Day 49: Ethical Considerations in Statistical Analysis

Day 50: Case Studies in Statistical Analysis

Day 51: Communicating Statistical Findings

Day 52: Building Comprehensive Statistical Reports

Day 53: Preparing for Statistical Presentations

Day 54: Final Review and Project Work

Day 55: Training: Final Project in SPSS

Day 56: Training: Final Project in Excel and Tableau