Chapter 16 Multiple Regression

These vocabulary flashcards cover key terms and definitions related to multiple regression analysis, aiding in understanding concepts needed for the exam.

Multiple Regression

A method of regression analysis that uses multiple independent variables to predict the outcome of a dependent variable.

Independent Variables

Variables that are manipulated or selected to observe their effect on a dependent variable.

Dependent Variable

The outcome variable that researchers are trying to predict or explain.

R Package

A collection of functions and datasets that can be used for statistical analysis in R.

stargazer

An R package used to produce well-organized and easy-to-read summary tables for regression output.

Life Expectancy

The average number of years a person is expected to live, often used as a measure of a population's health.

Fertility Rate

The average number of children born to a woman over her lifetime.

Mean Years of Education

The average number of years of education received by individuals in a population.

Cross-national Analysis

Analytical comparisons conducted across different countries.

R-squared (R²)

A statistical measure that represents the proportion of variance for a dependent variable that's explained by independent variables.

Adjusted R-squared (Adjusted R²)

A modified version of R² that adjusts for the number of predictors in a model.

Residual Standard Error

The standard deviation of the residuals, representing the average amount that the observed values differ from the predicted values.

F Statistic

A ratio used to compare the fits of multiple models to understand if at least one regression coefficient is different from zero.

Significance Level

A threshold for determining if a statistical result is unlikely to have occurred by chance.

Linear Equation

An algebraic equation in which each term is either a constant or the product of a constant and a single variable.

Statistically Significant

A result that is unlikely to occur by random chance alone, typically measured by a p-value.

Null Hypothesis

A general statement that there is no effect or no difference, used as a default position in statistical testing.

P-value

A measure that helps determine the significance of results in hypothesis testing.

Outliers

Observations that fall far from the other data points, possibly indicating variability in measurement or a different population.

Coefficient

A numeric value that represents the strength and direction of a relationship between an independent variable and the dependent variable.

Prediction

An estimation of an outcome based on statistical analyses.

Null Variable

A variable not significantly contributing to the model's prediction capability.

Correlation

A statistical measure that describes the extent to which two variables change together.

Bivariate Regression

A regression analysis that involves two variables.

Model Fit

The degree to which a statistical model accurately represents the observed data.

Predictive Capacity

The ability of a model to accurately predict outcomes based on input variables.

Statistical Output

The results of statistical analysis, typically including estimates, significance levels, and model fit statistics.

Control Variables

Variables that are held constant to accurately assess the effect of independent variables on a dependent variable.

Root Mean Squared Error (RMSE)

A measure of the differences between predicted and observed values; lower values indicate better model fit.

Effect Size

A quantitative measure of the magnitude of the experimental effect.

Descriptive Labels

Labels that clearly describe the variables being analyzed in statistical output or tables.

Dummy Variables

Numerical variables used in regression analysis to represent categories or groups.

Sample Size (N)

The number of observations included in a statistical study.

Degrees of Freedom

The number of independent values or quantities which can be assigned to a statistical distribution.

Scatterplot

A graphical representation of the relationship between two quantitative variables.

Predictor Variables

Another term for independent variables, used to predict the outcome.

Regression Line

A line that best fits the data points in a scatterplot of the actual versus predicted values.

Statistical Significance

A determination that a relationship or effect in data is not due to random chance.

Model Assessment

The process of evaluating the performance and accuracy of a statistical model.

Linear Model

A statistical model that assumes a linear relationship between independent and dependent variables.

Variance

A measure of the dispersion of numbers in a data set.

Statistical Independence

A situation where the occurrence of one event does not affect the probability of the occurrence of another.

Analyzing Residuals

Evaluating the differences between observed and predicted values to assess model fit.

Multicollinearity

A situation in which two or more independent variables are highly correlated.

Path Diagram

A visual representation of the relationships between variables in a regression model.

Hypothesis Testing

The method of making statistical decisions using experimental data.

Linear Regression Coefficients

Numerical values that indicate the relationship strength and direction between independent and dependent variables.

Transformations

Statistical techniques used to alter variables for improved model fitting.

Analysis of Variance (ANOVA)

A collection of statistical models used to analyze the differences among group means.

Model Specification

The process of determining the functional form of the relationship between variables.