Quant Flashcards
Analysis of Variance (ANOVA)
The analysis of the total variability of a dataset (such as observations on the dependent variable in a regression) into components representing different sources of variation; with reference to regression, ANOVA provides the inputs for an F-test of the significance of the regression as a whole.
Dependent Variable
The variable whose variation about its mean is to be explained by the regression; the left-hand-side variable in a regression equation.
Error Term
The portion of the dependent variable that is not explained by the independent variable(s) in the regression
Estimated Parameters
With reference to a regression analysis, the estimated values of the population intercept and population slope coefficient(s) in a regression
Fitted Parameters
With reference to a regression analysis, the estimated values of the population intercept and population coefficient(s) in a regression
Independent Variable
A variable used to explain the dependent variable in a regression; a right-hand-side variable in a regression equations
Linear Regression
Regression that models the straight-line relationship between the dependent and independent variable(s)
Parameter Instability
The problem or issue of population regression parameters that have changed over time
Regression coefficient
The intercept and slope coefficient(s) of a regression
Adjusted R2
A measure of goodness-of-fit of a regression that is adjusted for degrees of freedom and hence does not automatically increase when another independent variable is added to a regression
Breusch-Pagan test
A test for conditional heteroskedasticity in the error term of a regression
Categorical dependent variables
An alternative term for qualitative dependent variables
Common size statements
Financial statements in which all elements (accounts) are stated as a percentage of a revenue for income statement or total assets for balance sheet
Conditional heteroskedasticity
Heteroskedasticity in error variance that is correlated with the values of the independent variable(s) in the regression
Data Mining
The practice of determining a model by extensive searching through a dataset for statistically significant patterns
Discriminate analysis
A multivariate classification technique used to discriminate between groups, such as companies that either will or will not become bankrupt during some time frame
Dummy variable
A type of qualitative variable that takes on a value of 1 if a particular condition is true and 0 if that condition is false
First-Order Serial Correlation
Correlation between adjacent observations in a time series
Generalized least squares
A regression estimation technique that addresses heteroskedasticity of the error term
Assumptions of Linear Regression Model
- Relationship between dep variable and ind variable is linear
- Ind variable is not random
- Expected value of error term = 0
- Variance of the error term is same for all observations
- Error term is not correlated across observations
- Error term is normally distributed
Type I error
Rejecting the null hypothesis when it is true (i.e. null hypothesis should not be rejected)
Type II error
Failing to reject the null hypothesis when it is false (i.e. null should be rejected)
P-value
Smallest level of significance at which the null hypothesis can be rejected
Heteroskedastic
With reference to the error term of regression, having a variance that differs across observations - i.e. non-constant variance
Having consistent standard errors will correct for this