CPSC 375: Introduction to Data Science and Big Data Analytics

These flashcards cover key vocabulary and concepts related to data science, particularly focused on regression analysis and model evaluation.

Data Science Process

A systematic approach for collecting, structuring, and analyzing data to gain insights.

Linear Regression Model

A statistical method to model the relationship between a dependent variable and one or more explanatory variables by fitting a linear equation.

Coefficients

Values that represent the relationship between independent variables and the dependent variable in regression models.

Residuals

The differences between observed and predicted values in a regression model.

Sum of Squares Error (SSE)

A measure of the total deviation of the response values from the fit to the response values, indicating unexplained variation.

Adjusted R-squared

A modified version of R-squared that adjusts for the number of predictors in a model, preventing overfitting.

Dummy Variables

Binary variables created to represent categories of a qualitative variable, used in regression models.

Least Squares Method

A statistical technique used to estimate the parameters of a linear regression model by minimizing the sum of squared residuals.

Prediction Interval

An estimate of the range in which new observations are expected to fall, given a certain probability.

Confidence Interval

An estimate of the range in which the true mean of the dependent variable is expected to fall, given a certain probability.

Outlier

An observation that deviates significantly from the other data points, which may indicate an error or an unusual occurrence.

Nonlinear Regression

A form of regression analysis in which data fit a model described by a nonlinear equation.

Exploratory Data Analysis (EDA)

The process of analyzing data sets to summarize their main characteristics, often visualizing them to gain insights.

Model Fit

A term that refers to how well a regression line approximates the real data points.

Variance Inflation Factor (VIF)

A measure of how much the variance of a regression-coefficient estimate is increased due to multicollinearity.

Homoscedasticity

A characteristic of a dataset in which the variance of the errors is constant across all levels of the independent variable.

Heteroscedasticity

A condition in which the variance of errors differs across levels of the independent variable.

P-value

The probability that the observed results would occur by chance if the null hypothesis were true, helping to determine statistical significance.