Data Basics Types of Variables Numerical (Quantitative) Continuous: Can take any value within a range (e.g., height, weight). Discrete: Can only take

Continuous Variable

A numerical variable that can take any value within a range, such as height or weight.

Discrete Variable

A numerical variable that can only take distinct values, such as the number of students in a class.

Categorical Variable

A variable that can be divided into categories, which can be regular (no inherent order) or ordinal (meaningful order).

Sampling Bias

A systematic error that occurs when certain individuals are more likely to be included in a sample than others.

Non-response Bias

A type of bias that occurs when only a small fraction of sampled individuals respond to the survey.

Randomize

The process of randomly assigning subjects to treatments in an experiment.

Control Group

A group in an experiment that does not receive the treatment and is used for comparison.

Placebo Effect

A psychological response in which participants show improvement after receiving a non-active treatment.

Blinding

A technique used to prevent bias by concealing treatment assignment from participants.

Mean

The average of a dataset, calculated by summing all values and dividing by the count of values.

Median

The middle value of a dataset when the values are ranked in order.

Variance

The average of the squared deviations from the mean, indicating how spread out the values are.

Standard Deviation

The square root of the variance, representing the average distance of values from the mean.

Interquartile Range (IQR)

The difference between the third and first quartiles (Q3 - Q1), indicating the spread of the middle 50% of data.

Correlation Coefficient (r)

A numerical measure of the strength and direction of a linear relationship between two variables, ranging from -1 to +1.

Least Squares Regression Line

A line that minimizes the sum of the squared residuals between observed and predicted values, used for prediction.

Residuals

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

R² (Coefficient of Determination)

A statistic that indicates the proportion of variability in the dependent variable explained by the independent variable(s).

Extrapolation

The process of predicting values outside the observed data range, which is often unreliable.