Simple Linear Regression Overview

A set of flashcards that cover key concepts related to Simple Linear Regression, including definitions, variables, assumptions, and analysis methods.

Simple Linear Regression

A statistical technique used to determine the relationship between one predictor variable and a dependent variable.

Dependent Variable (y)

The outcome variable in a regression analysis that is being predicted.

Independent Variable (x)

The predictor variable in a regression analysis that is used to predict the dependent variable.

Regression Line of Best Fit

The line that best represents the data on a graph, minimizing the distance between the line and each data point.

Slope (b)

The measure of how much the dependent variable changes for each unit increase in the independent variable.

y-Intercept (a)

The value of the dependent variable when the independent variable is zero; the point where the regression line intersects the y-axis.

Regression Coefficient

Another term for slope (b); indicates the strength and direction of the relationship between variables.

Normal Distribution

An assumption that the dependent variable follows a bell-shaped curve centered around its mean.

Homoscedasticity

An assumption of regression analysis that states residuals are equally varied across all levels of the independent variable.

Multicollinearity

A phenomenon in statistical analysis where two or more independent variables are highly correlated, affecting the reliability of regression coefficients.

Validity

The degree to which a tool measures what it claims to measure.

Reliability

The consistency of a measure over time and across different instances.

Critical t Value

The threshold value that the calculated t statistic must exceed to indicate significance.

R-squared (R²)

A statistical measure that represents the proportion of variance for the dependent variable explained by the independent variable(s).

Standardized Beta (β)

A statistic that indicates the magnitude of the relationship between the predictor and dependent variable.

Associational Design

A type of research design that examines the relationship or association between two or more variables.

Sample Size (n)

The number of observations or participants included in a study or analysis.

Pearson Correlation

A statistical measure that expresses the extent to which two variables are linearly related.

Statistical Analysis

The process of collecting and interpreting data to uncover patterns and relationships.

Hypothesis Testing

A method used to determine if there is enough statistical evidence in a sample of data to infer that a certain condition holds for the entire population.

Prediction

The act of forecasting the value of a dependent variable based on the values of independent variables.

Interpretation of Results

The process of explaining the meaning and implications of statistical findings.

Patient Health Questionnaire (PHQ-9)

A widely used self-report tool for screening and measuring the severity of depression.

Brief Resilience Scale (BRS)

A scale designed to measure an individual’s ability to bounce back from stress.

Degrees of Freedom (df)

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

Statistical Software

Computer programs used to manage, analyze, and visualize data.

Assumptions of Linear Regression

Conditions that must be met for the results of regression analyses to be valid.

Research Study Design

The framework that outlines how a study will be conducted, including sampling and data collection methods.