Topic 4.4: Cumulative Frequency and Box Plots Questions

Describe how to find the equation of the regression line of y on x from bivariate data on a GDC.

For data with x ranging from 10 to 50, state whether using the regression line to predict y at x = 30 is interpolation or extrapolation.

A scatter diagram shows points rising from lower-left to upper-right in a tight band. Describe the correlation.

State what a Pearson correlation coefficient of r = −0.88 tells you about two variables.

Two variables have r = 0.15. Describe the correlation.

State the range of possible values of the Pearson correlation coefficient r.

A GDC gives r = 0.97 for the relationship between two quantities. State whether a line of best fit would be a good model, and why.

A regression line is y = 1.14x + 0.58. Use it to estimate y when x = 6.

A study finds a strong positive correlation between ice-cream sales and drowning incidents. Explain why ice-cream sales do not cause drownings.

A regression line of height (y cm) on age (x years) is y = 5.2x + 80. Interpret the y-intercept 80 in context.

Order these correlation coefficients from weakest to strongest linear relationship: r = 0.45, r = −0.91, r = 0.10, r = −0.6.

For data with x from 10 to 50, classify a prediction at x = 70 and comment on its reliability.

A regression line of y on x is y = −2.5x + 40. Interpret the gradient in context (x = price in $, y = units sold).

Explain why predictions from a regression line are more trustworthy when r is close to ±1.

A coach records the number of training hours per week (x) and the distance jumped (y, in metres) for 8 athletes. Entering the data into a GDC gives the Pearson correlation coefficient r = 0.92.

A researcher studies how the temperature x (in °C) affects the number of cold drinks y sold at a kiosk. The recorded data, for temperatures from 18 °C to 32 °C, gives the regression line of y on x as y = 8.5x − 96 (from a GDC), with r = 0.96.