QCAA general maths unit 3 revision set

Bivariate Data

Data with two variables

Categorical data

Data values are labelled (e.g., colours, names)

Numerical data

Data values that are numbered (e.g., height, weight)

Explanatory variable

Explains or predicts changes

Response variable

Responds to or is affected by the explanatory variable

Where are the EV and RV plotted on graphs?

EV plotted on x-axis, RV plotted on y-axis

Two-way frequency tables

Summarises bivariate data; columns labelled with EV values, rows labelled with RV values

Correlation coefficient

r value- measures the strength of a linear relationship between two numerical variables

Coefficient of determination

R²- Indicates the percentage of variation in the RV explained by the EV

Linear regression

Fitting a straight line to data model the relationship between two numerical variables

Least squares method

A technique to find the best-fitting regression line by minimising the sum of the squares of the residuals. Works best when there are no outliers

Residuals

The vertical distance between a data point an the regression line

Residual equation

Residual= data value - predicted value

Interpolation

Inside the given data range

Extrapolation

Anything outside the given data range

Correlation

A relationship between two variables

Causation

One variable directly affects the other

Nominal data

Categorical data with no order or ranking (e.eg colours, types of fruit)

Ordinal data

Categorical data with a natural order or rankings, but no consistent difference between categories (e.eg survey responses, education levels)

Discrete data

Numerical data that can only take specific, separatee values (often whole numbers)

E.g. Number of students in a class, number of cars in a parking lot

Continuous data

Numerical data that can take any value within a range, including decimals
E.g. height, temperature, time