Regression Analysis with R - PART ONE (detailed explanation)

R is one of the best or simply the best statistical programming language in the world.
This video lesson is all about REGRESSION ANALYSIS WITH R (LINEAR REGRESSION MODELS).
You'll learn a great deal of lesson and understand what it means to estimate and analyze regression models.
The lesson covers in detail the following:
1. Introduction to ECONOMETRICS
2. Traditional/Classical Methodology of Econometrics
3. Population Regression Function versus Sample Regression Function
4. Method of Ordinary Least Squares
5. Simple and Multiple Linear Regression Models
6. Statistical Significance of Regression Coefficients, and Overall/Joint/Global Hypothesis
You'll also learn important concepts such p-values, significance levels, coefficient of determination (R-squared), etc...
THERE ARE GOING TO BE VERY POWERFUL PRESENTATIONS OF STATISTICAL INFERENCE IN R. THESE ARE THE TOPICS TO BE COVERED AND UPLOADED ON THE CHANNEL:
1. Statistical Inference with R - Concepts and Applications (COVERED)
2. Correlation Analysis (Pearson, SPearman, Kendall Tau, Point biserial, partial correlation, etc....) (COVERED)
3. Regression Analysis (Linear and Logistic Regression)
4. Comparing Two Means
5. Comparing Several Means (GLM1)
6. Analysis of Covariance (ANCOVA) (GLM2) and Factorial ANOVA (GLM3)
7. Repeated Measures Designs (GLM4) and Mixed Designs (GLM5)
8. Non-parametric Tests (Wilcoxon, Kruskal, Friedman's ANOVA, etc...)
9. Multivariate Analysis of Variance (MANOVA)
10. Exploratory Factor Analysis (PCA and Reliability Analysis)
11. Analyzing Categorical Data (Pearson Chi-Square, Fisher's Exact Test, etc...)
12. Multilevel Linear Models
DO YOU WANT TO BE A MASTER STATISTICIAN AND HAVE THESE IMPLEMENTED RIGHT IN R?
Then SUBSCRIBE TO THE CHANNEL FOR MORE OF THESE LESSONS!