2.10:

2.10: Graphique en nuage de points

JoVE Core
Statistics

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JoVE Core Statistics

Scatter Plot

2.11: Time-Series Graph

6,778 Views

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01:15 min

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April 30, 2023

Overview

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

High values of one variable occurring with high values of the other variable or low values of one variable occurring with low values of the other variable.
High values of one variable occurring with low values of the other variable.

One can determine the strength of the relationship by looking at the scatter plot and seeing how close the points are to a line, a power function, an exponential function, or to some other type of function. For a linear relationship, there is an exception. Consider a scatter plot where all the points fall on a horizontal line providing a "perfect fit." The horizontal line would, in fact, show no relationship.

When looking at a scatterplot, one must notice the overall pattern and any deviations, if any.

Transcript

Considérez des données quantitatives sur le prix des maisons et leur surface au sol correspondante. De telles données quantitatives à deux variables sont appelées données bivariées.

La variable qui agit en tant que cause est appelée variable indépendante, tandis qu’une autre variable qui montre la réponse est appelée variable dépendante.

Cette dépendance d’une variable par rapport à l’autre peut être visualisée à l’aide du nuage de points. Ici, la variable indépendante (la surface au sol) est représentée le long de l’axe des abscisses, et la variable dépendante (le prix des maisons) est représentée le long de l’axe des ordonnées.

Marquez les prix correspondant à la surface du sol. Ensuite, tracez la ligne d’ajustement optimale de sorte qu’un nombre presque égal de points soient présents au-dessus et en dessous de cette ligne. Ensemble, ces points forment le modèle permettant d’identifier la corrélation entre les deux variables.

Notez que l’augmentation de la surface au sol entraîne une hausse du prix des maisons. Une telle tendance à la hausse dénote une corrélation positive.

À l’inverse, si l’on observe une tendance à la baisse, cela indique une corrélation négative. Pas de tendance signifie pas de corrélation.

Key Terms and definitions

Scatter Plot - A graph showing the relationship between two variables, x and y.
Direction - Highs and lows of variables in a scatter plot.
Perfect Correlation - All points in a scatter plot fall on a single line.
No Trend - Scatter plot showing no clear relationship between variables.
Linear Relationship - Scatter plot where data points fall along a line, but not horizontally.

Learning Objectives

Define Scatter Plot - Visual representation of the relationship between two variables (e.g., scatter plot).
Contrast Perfect Correlation vs No Trend - Distinguish between clear and unclear relationships (e.g., perfect correlation scatter plot vs no trend scatter plot).
Explore Examples - Look at variations of scatter plots (e.g., linear relationship scatter plot).
Explain Scatter Plot Direction - Describe how the direction of a scatter plot is determined.
Apply in Context - Understand how scatter plots are used in psychology.

Questions that this video will help you answer

What is a scatter plot and how to read one?
What distinguishes perfect correlation from no trend in scatter plots?
How to determine the direction of a scatter plot?

This video is also useful for

Students - Understanding of scatter plots aids in comprehending variable relationships.
Educators - Scatter plots provide a visual tool for teaching relationship between variables.
Researchers - Scatter plots can serve as a fundamental tool in data analysis.
Science Enthusiasts - Scatter plots offer a simple way to see relationships between variables.

Tags

Nuage de points relation variables X et Y valeurs élevées valeurs faibles force de la relation relation linéaire proximité de points ajustement parfait modèle global écarts