INST314 Final Review Flashcards

Flashcards for reviewing key concepts from the INST314 final review guide.

Nominal Data

Unordered categories (e.g., red, blue, green). Described with proportions and counts using tables or charts.

Ordinal Data

Ordered categories (e.g., small, medium, large). Described with proportions and counts using tables or charts.

Discrete Numerical Data

Whole numbers (e.g., how many people in a class).

Continuous Numerical Data

Infinite number of possible values (e.g., height, weight). Described by distribution shape, center, and spread.

P-value

Probability of observing data as extreme or more extreme than what we got if the null hypothesis is true.

Confidence Interval

A range of values likely to contain the true population parameter with a certain level of confidence (typically 95%).

Type I Error

Rejecting the null hypothesis when it is actually true.

Type II Error

Failing to reject the null hypothesis when it is not true.

Correlation

Measuring the strength and direction of a linear relationship between two variables.

Residual

The difference between an observed value and the value predicted by the regression model.

Linearity (in Simple Linear Regression)

The relationship between the predictor and outcome should be a straight line.

Approximately Normal Residuals

The leftover errors (residuals) should follow a normal distribution.

Constant Variability

The spread of residuals should be roughly the same across all fitted values.

Independence (in Regression)

Residuals should not be related to each other. Plotting residuals in observation order can help detect patterns or autocorrelation.

Sample

A subset of individuals or data points taken from a larger population, giving one estimate of a population parameter.

Sampling Distribution

The distribution of a statistic (like a sample mean) over many possible samples from the same population.

Evidence against the null hypothesis --> reject the null

Low p-value (< 0.05)

Fail to reject the null (not enough evidence)

High p-value (≥ 0.05)

One-way ANOVA

Compares mean across one factor (one independent variable).

Two-way ANOVA

Compares means based on two factors (e.g., teaching method and gender)

Interaction Effects

Occur when the effect of one factor depends on the level of the other factor.

Simple Linear Regression

Predicts one variable (response, y) using one predictor (explanatory, x).

Multiple Regression

Predicts y using two or more predictors.