UNIVERSITY OF THE CUMBERLANDS • DSRT 734

DSRT 734: Estimating Pearson’s Correlation From a Scatterplot

This question is related to DSRT 734 Inferential Statistics in Decision-Making at University of the Cumberlands.

Get DSRT 734 Help

DSRT 734 Question

Given the scatterplot below, choose the value for Pearson's Correlation
below that most accurately approximates the correct correlation.

[SCATTERPLOT IMAGE]

A. r = 0.04

B. r = 0.74

C. r = 0.47

D. r = -0.74

The scatterplot referenced in this question is not included.

What this question assesses

This item assesses visual estimation of a Pearson correlation from a scatterplot. The required judgment combines direction, how tightly points follow a straight-line pattern, possible outliers, and whether a linear correlation is a sensible summary.

How to approach this type of question

A valid estimate requires the omitted scatterplot. When the image is available, begin with direction: an upward cloud supports a positive sign and a downward cloud supports a negative sign. Next assess form. Pearson’s r summarizes linear association, so curvature or separated clusters can make a single coefficient misleading even when a pattern is strong. Then judge strength by how narrowly the points cluster around an imagined straight line, not by the steepness of that line. A nearly patternless cloud suggests a value near zero; a clearer linear band suggests a magnitude farther from zero. Inspect unusual points because one high-leverage observation can change the visual impression substantially. Finally compare those features with every offered value, checking sign and magnitude separately. Because the scatterplot is missing, none of these observations can be made from the supplied source, and this page does not select an option or infer the unseen pattern.

A useful inspection record would list the plot’s horizontal and vertical variables, observed sign, apparent form, strength, outliers, and clusters. Estimate magnitude only after those features are documented. Do not use axis slope as strength because rescaling an axis changes steepness without changing correlation. If the plot is curved, note that a coefficient near zero can coexist with a strong nonlinear pattern. Those checks prevent a visually appealing answer choice from replacing evidence that only the missing image can provide.

Get Help With DSRT 734 Inferential Statistics in Decision-Making at University of the Cumberlands

Get targeted DSRT 734 help and improve your grades.

Get DSRT 734 Help

Sources & updates

Published by Domyclass • Updated August 2026