Data Analysis and Regression Techniques

These flashcards cover essential vocabulary and definitions related to data analysis and regression techniques.

Data Analysis

The process of examining, organizing, transforming, and interpreting data to extract useful insights and support decision-making.

Bivariate Analysis

A statistical method that examines the relationship between two variables.

Difference in Means Test (t-test)

A statistical test used to compare the average (mean) values of two groups to determine if they are significantly different.

t-test Types

Independent t-test compares means between two separate groups; paired t-test compares means within the same group before and after a treatment.

p-value

The probability that the observed difference or relationship happened by chance.

Correlation Coefficient (r)

A value between -1 and +1 that measures the strength and direction of a relationship between two variables.

Null Hypothesis (H₀)

A statement that there is no effect or no relationship between variables.

Magnitude of Effects

It refers to the importance of the difference; it indicates how meaningful or impactful the difference is.

Bivariate Regression

Examines the relationship between one independent variable and one dependent variable.

Multivariate Regression

Examines the relationship between multiple independent variables and one dependent variable.

Regression Equation

The formula used to predict outcomes: yᵢ=α+βxᵢ+e.

Constant (α)

The predicted value of y when x is zero.

Coefficient (β)

The change in y for each unit change in x.

Standard Error of the Coefficient

Measures how much the coefficient is likely to vary if the study were repeated.

Confidence Interval

A range of values within which we are 95% confident the true coefficient falls.

Adjusted R²

Measures how well the model explains variability in y while adjusting for the number of predictors.

3 S's in Regression Interpretation

Consider Sign, Size, and Significance to interpret regression results.