Lessons
- CFA L2: Quantitative Methods - Introduction
- Quants: Correlation Analysis
- Quants: Single Variable Linear Regression Analysis
- Standard Error of the Estimate or SEE
- Confidence Intervals (CI) for Dependent Variable Prediction
- Coefficient of Determination (R-Squared)
- Analysis of Variance or ANOVA
- Multiple Regression Analysis
- Multiple Regression and Coefficient of Determination (R-Squared)
- Fcalc – the Global Test for Regression Significance
- Regression Analysis and Assumption Violations
- Qualitative and Dummy Variables in Regression Modeling
- Time Series Analysis: Simple and Log-linear Trend Models
- Auto-Regressive (AR) Time Series Models
- Auto-Regressive Models - Random Walks and Unit Roots
- ARMA Models and ARCH Testing
- How to Select the Most Appropriate Time Series Model?
Standard Error of the Estimate or SEE
- Also called Standard Error of the Regression
- Conceptually SEE helps to measure how imperfect your model is at predicting the value for a dependent variable, Y.
- Mathematically SEE can be seen a measure of deviation(s) around your model’s regression line, a line one deviation above and below the predicted equation line
The following video explains the concept using sample data from Google and yahoo stock quotes.
Membership
Learn the skills required to excel in data science and data analytics covering R, Python, machine learning, and AI.
I WANT TO JOINJOIN 30,000 DATA PROFESSIONALS
Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.