Definitions1. Regression analysis is a statistical process for estimating the

relationships among variables. It includes many techniques for modelingand analyzing several variables, when the focus is on the relationshipbetween a dependent variable and one or more independent variables.

2. "Least squares" means that the overall solution minimizes the sum ofthe squares of the errors made in the results of every single equation.

3. Significance Level The significance level of a statistical hypothesis testis a fixed probability of wrongly rejecting the null hypothesis H0, if it isin fact true.

4. Simple linear regression is the least squares estimator of alinear regression model with a single explanatory variable.

5. Multiple linear regression attempts to model the relationship betweentwo or more explanatory variables and a response variable by fittinglinear equation to observed data. Every value of the independent variablex is associated with a value of the dependent variable y.

• Standard error is used to estimate confidence interval for dependent variable.

• Multicollinearity (also collinearity) is a statistical phenomenon in which two or morepredictor variables in a multiple regression model are highly correlated, meaning that onecan be linearly predicted from the others with a non-trivial degree of accuracy.

• Remedies of Multicolinearity:1. If mild multicollinearity : do nothing

2. Drop one of the variable

3. Transform the variable1. Combination

2. Log or square 1

3. Increase sample size

• The P value or calculated probability is the estimated probabilityof rejecting the null hypothesis (H0) of a study question when thathypothesis is true.

• Degree of Freedom: This refers to a positive whole number that indicates thelack of restrictions in our calculations. The degree of freedom is the numberof values in a calculation that we can vary.

• R-squared is a statistical measure of how close the data are to thefitted regression line. It is also known as the coefficient ofdetermination, or the coefficient of multiple determination formultiple regression.

Definitions

Definitions

• DEFINITION of 'Heteroskedasticity' In statistics, when the standard

deviations of a variable, monitored over a specific amount of time, are

non-constant. Heteroskedasticity often arises in two forms, conditional

and unconditional.