The assumption of homoscedasticity (literally, same variance) is central to linear regression models. Homoscedasticity describes a situation in which the error term (that is, the “noise” or random disturbance in the relationship between the independent variables and the dependent variable) is the same across all values of the independent variables. Heteroscedasticity (the violation of homoscedasticity)
Validity implies precise and exact results acquired from the data collected. In technical terms, a measure can lead to a proper and correct conclusions to be drawn from the sample that are generalizable to the entire population. Four Major Types: 1.Internal validity: When the relationship between variables is causal. This type refers to the relationship
Two-Stage least squares (2SLS) regression analysis is a statistical technique that is used in the analysis of structural equations. This technique is the extension of the OLS method. It is used when the dependent variable’s error terms are correlated with the independent variables. Additionally, it is useful when there are feedback loops in the model.
There are 3 major areas of questions that the regression analysis answers – (1) causal analysis, (2) forecasting an effect, (3) trend forecasting. The first category establishes a causal relationship between two variables, where the dependent variable is continuous and the predictors are either categorical (dummy coded), dichotomous, or continuous.. In contrast to correlation analysis
Nonlinear regression is a regression in which the dependent or criterion variables are modeled as a non-linear function of model parameters and one or more independent variables. There are several common models, such as Asymptotic Regression/Growth Model, which is given by: b1 + b2 * exp(b3 * x) Logistic Population Growth Model, which is given