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QUANT 10) - Coggle Diagram
QUANT 10)
10.2: Analysis of Variance (ANOVA)
Assumptions of Simple Linear Regression
Analysis of Variance (ANOVA) and Its Components
Hypothesis Testing on a Regression Coefficient :pencil2:
Goodness-of-Fit Measures
Testing Significance: the F-Statistic
Hypothesis Testing on a Regression Coefficient :check:
10.1: Linear Regression Basics
Basics
Independent Variable (X)
Dependent Variable (Y)
The Regression Model and Scatter Plot Illustration
Population Regression Equation
3 data types in regression
: Time series, Cross sectional and Panel data
know when to use it, give an example o feach
Time series has to do with 1 stock during a mutliple period
several stocks in several time series
Cross:
Estimating the Regression Line (OLS)
Estimated Regression Equation
Least Squares Criterion
Estimating the Slope Coefficient :check:
Estimating the Intercept Term
Interpreting the Coefficients
Interpreting the Slope :check:
using Dummy variables :pencil2:
Interpreting the Intercept :check:
Special Case: Beta
The beta of an asset is typically estimated by regressing the historical excess returns of the assets versus the historical excess returns of the market
10.3: Predicted Values and Functional Forms of Regression
Predicted Values
Confidence intervals for Predicted Values
Standard Error of the Forecast
Functional Forms of Simple Linear Regression
Log-lin model:
To test whether a one-unit increase in X leads to a constant percentage change in Y
Lin-log model:
would be used to test whether a 1% increase in X leads to a constant absolute change in Y.
Log-log model:
would be used to test whether a 1% increase in X leads to a constant percentage change in Y.
10.4: CAPM Values With Simple Linear Regression