Heteroscedasticity - Non constant error variance. Hetero = different; scedasticity = tendency to scatter.
Hierarchical Regression Analysis - A multiple regression analysis method in which the researcher, not a computer program, determines the order that the variables are entered into and removed from the regression equation. Perhaps the researcher has experience that leads him/her to believe certain variables should be included in the model and in what order.
Homoscedasticity - Constant error variance. Homo = same; scedasticity = tendency to scatter.
Hypothesis Testing - This is the common approach to determining the statistical significance of findings.
Independent Variable, Explanatory Variable, Predictor Variable, Input Variable - The variable in correlation or regression that can be controlled or manipulated. In math, x frequently represents the independent variable.
Influential Observation - An observation that has a large effect on the regression equation. Note: Outliers and leverage points may be influential observations, but influential observations are usually outliers and leverage points.
Inverse Relationship, Inverse Correlation, Negative Correlation, Negative Relation - Relation between two variables (x, y) such that as x increase, y decreases (or visa versa).
Intercorrelation - Correlation between variables that are all independent (no dependent variables involved).
Least Squares Regression - Regression analysis method which minimizes the sum of the square of the error as the criterion to fit the data. This can refer to linear or curvilinear regression.
Leverages, Leverage Points - An extreme value in the independent (explanatory) variable(s). Compared with an outlier, which is an extreme value in the dependent (response) variable.
Line of Best Fit - See Regression Line.
Linear Correlation- A relationship between the independent and dependent data, that whenever plotted forms a straight line.
Linear Regression - Typically when regression is used without qualification, the type of regression is assumed to be linear regression. This is the method of finding a linear model for the dependent variable based on the independent variable(s).
Linear Trend - The appearance that the data has a linear relationship whenever plotted.
Mean Square Residual, Mean Square Error (MSE) - A measure of variability of the data around the regression line or surface.
Measurement Error (Error, Measurement) - inaccurate results due to flaw(s) in the measuring instrument.
Multicollinearity, Collinearity - The case when two or more independent variables are highly correlated. The occurrence of multicollinearity can cause difficulties in multiple regression. If the independent variables are interrelated, then it may be difficult or impossible to find the specific effect of only one independent variable.
Multiple Correlation Coefficient, R - A measure of the amount of correlation between more than two variables. As in multiple regression, one variable is the dependent variable and the others are independent variables. The positive square root of R-squared.
Multiple
Correlation - Correlation
with one dependent variable and two or more independent variables.
Measures the combined influences of the independent variables on the
dependent.
gives
the proportion of the variance in the dependent variable that can be explained
by the action of all the independent variables taken together.
Multiple Correlation Matrices - A table of correlation coefficients that shows all pairs of correlations of all the parameters with in the sample.
Multiple Correlation Plots - A collection of scatterplots showing the relationship between the variables of interest.
Multiple R - That is the name MS Excel uses for the Multiple Correlation Coefficient, R.
Multiple Regression, Multiple Linear Regression - A method of regression analysis that uses more than one independent (explanatory) variable(s) to predict a single dependent (response) variable. Note: The coefficients for any particular explanatory variable is an estimate of the effect that variable has on the response variable while holding constant the effects of the other predictor variables. “Multiple” means two or more independent variables. Unless specified otherwise, “Multiple Regression” generally refers to “Linear” Multiple Regression.
Multiple Regression Analysis (MRA) - Statistical methods for evaluation the effects of more than one independent variable on one dependent variable.
Negative Correlation- This occurs whenever the independent variable increases and the dependent variable decreases. This is also called a negative relationship.
Nonadditivity - A statement used to describe a relation when the addition of the separate effects do not add up to the total effect.
Nonlinearity - The events are not the same as their causes.
Nonlinear Relationship - A relationship between two variables for which the points in the corresponding scatterplot do not fall in approximately a straight line. Nonlinearity may occur because there is not a defined relationship between the variables as in the first figure below, or because there is a specific curvilinear relationship. See the parabolic relationship shown in the second graph below.
Normality Plot, Normal Probability Plot - A graphical representation of a data set used to determine if the sample represents an approximately normal population. A graph from Minitab is shown below. The sample data is on the x-axis and the probability of the occurrence of that value assuming a normal distribution is on the y-axis. If the resulting graph is approximately a straight line, then the distribution is approximately normal. There are statistical hypothesis tests for normality as well.
Null Hypothesis,
- This is the hypothesis that two or more variables are not related and the
researcher wants to reject.
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