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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
Estimating the Regression Line (OLS)
Estimated Regression Equation
Least Squares Criterion
Estimating the Slope Coefficient
Estimating the Intercept Term
Interpreting the Coefficients
Interpreting the Slope :check:
using Dummy variables :pencil2:
Interpreting the Intercept :check:
Special Case: Beta
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