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?
Confidence Intervals (CI) for Dependent Variable Prediction
- In all likelihood, your model will not perfectly predict Y.
- The SEE can be extended to determine the confidence interval for a predicted Y value. A common CI to test for a predicted value is 95%.
- Your regression parameters, the y-intercept (b0) and slope coefficient (b1) will need to be tested for significance before you can generate a confidence interval around your model’s project Y value around an expected X value.
- H0 = 0 is the null hypothesis when testing either parameter and you will look to reject this in significance, (note: typically the greater emphasis is on the slope coefficient, as b1 value not statistically different from zero indicates no relationship between Y and X).
- tcalc = the standard script for the output of your significance test on the regression model’s parameters and its absolute value must exceed the designated tcritical on a two tailed significance test.
Data Science in Finance: 9-Book Bundle
Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.
What's Included:
- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python
Each book includes PDFs, explanations, instructions, data files, and R code for all examples.
Get the Bundle for $29 (Regular $57)JOIN 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.
