English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 40 lectures (5h 12m) | 4.71 GB
Unlock the Power of Statistical Analysis to Uncover Relationships and Make Informed Decisions
Take your data analysis skills to new heights as you dive into the realm of statistical inference and relationships. In this “Mastering Statistics: Fundamentals to Data Analysis” course, you’ll learn advanced techniques to uncover hidden relationships within your data, enabling you to make informed decisions that drive tangible results.
Statistical analysis is the key to unlocking the potential insights hidden within complex datasets. Building upon foundational knowledge, this course will equip you with the tools and techniques to analyze and interpret relationships between variables. Through practical examples, real-world scenarios, and hands-on exercises, you’ll gain a deep understanding of how to navigate advanced statistical concepts with confidence.
From comparing samples and assessing relationships to understanding confidence intervals and significance testing, you’ll develop a comprehensive toolkit for robust data analysis. You’ll explore techniques for handling binary and categorical data, delve into correlation and regression analysis, and master ANOVA for advanced statistical inference.
By avoiding common pitfalls and understanding the dangers of data dredging, you’ll emerge with the skills needed to draw accurate conclusions and make data-driven decisions. Whether you’re working in business, research, or any data-centric field, this course will empower you to extract valuable insights that can shape your organization’s success.
By the end of this course, you’ll be equipped with advanced statistical techniques that will transform the way you analyze data. Uncover hidden relationships, make data-driven decisions, and unlock new opportunities for growth and success.
Enroll now and embark on a journey of mastery, as you harness the power of statistical inference and relationships to drive informed decision-making.
What you’ll learn
- Gain expertise in advanced statistical techniques to uncover meaningful relationships within data.
- Develop the skills to make informed decisions based on robust statistical inference.
- Master the application of statistical tools for analyzing categorical and quantitative variables.
- Avoid common pitfalls and confidently draw accurate conclusions from complex data analysis.
Table of Contents
Foundations of Statistical Analysis and Confidence Intervals
1 Course Overview and Welcome
2 Reproducing Work in R Setting up Your Environment
3 Understanding the Challenge of Inference
4 Addressing Bias and Variability in Data Analysis
5 Introduction to Confidence Intervals Concepts and Importance
6 Calculating Confidence Intervals Step-by-Step Guide
7 Interpreting Confidence Intervals Practical Examples and Applications
8 Foundations of Statistical Analysis and Confidence Intervals – Review
Significance Testing and Proportional Analysis Techniques
9 Introduction to Significance Testing Fundamentals and Hypothesis Testing
10 Common Errors in Significance Testing Type I and Type II Errors
11 Practice with Significance Testing Case Studies and Exercises
12 Understanding Statistical Usage Avoiding Misuse and Abuse
13 Confidence Intervals for Proportion Estimating and Interpreting Proportions
14 Significance Testing for Proportions Hypothesis Testing with Categorical Data
15 Proportions Practice Applying Proportional Analysis Techniques
16 Significance Testing and Proportional Analysis Techniques – Review
Goodness of Fit, Sample Size, and Two-Sample Analysis
17 Understanding Goodness of Fit Assessing Model Fit and Distributional Assumption
18 Goodness of Fit Practice Analyzing and Interpreting Model Fit
19 Sample Size and Power Determining Sample Size Requirements and Power Analysis
20 Fundamentals of Statistics Key Concepts
21 Introduction to Two-Sample Testing
22 Confidence Intervals for Two-Sample Comparison
23 Significance Testing for Two-Sample Comparison
24 Goodness of Fit, Sample Size, and Two-Sample Analysis – Review
Two-Sample Analysis Binary and Categorical Data
25 Practice Performing the Welch Test
26 Analyzing Two-Sample Binary Data
27 Pitfalls to Avoid The Dangers of Data Dredging
28 Analyzing Two-Sample Categorical Data
29 Practice Analyzing Categorical Data
30 Two-Sample Analysis Binary and Categorical Data – Review
Statistical Relationships and Analysis
31 Introduction to Sample Correlation
32 Hypothesis Testing and Significance of Correlation
33 Linear Regression Modeling Relationships
34 Practice Correlation and Regression Analysis
35 Introduction to ANOVA (Analysis of Variance)
36 Advanced ANOVA Techniques
37 Independence Testing of Categorical Variables
38 Practice Analyzing Categorical Variables
39 Statistical Relationships and Analysis – Review
40 Congratulations and Next Steps
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