Scatter plot curvilinear relationship example

Psychological Statistics

scatter plot curvilinear relationship example

Psychology definition for Curvilinear Relationship in normal everyday If you were to graph this kind of curvilinear relationship, you will come up with an. 0 sample re su lt. Curvilinear Correlation. Chapter 5 # 4. Correlation . What can we conclude simply from the scatter diagram? Chapter 5 #. Use a scatterplot to display the relationship between two quantitative variables. Curvilinear form: The data points appear scattered about a smooth curve. Let's look, for example, at the following two scatterplots displaying positive, linear.

scatter plot curvilinear relationship example

Each point on the plot shows the X and Y scores for a single subject. This is what we mean by "bivariate" plot -- each point represents two variables.

A bivariate plot of two scores self-esteem and Interpersonal Avoidance from our class dataset is shown below.

scatter plot curvilinear relationship example

The red line on the graph shows a perfect linear relationship between the two variables. As can be seen, the points on this graph do not follow a perfect straight line.

Overview of Correlation

The distance of the points to the line is called "scatter". A large amount of scatter around the line indicates a weak relationship. Little scatter represents a strong relationship. If all points fall directly on a straight line, we have a perfect linear relationship between our two variables.

Curvilinear Relationship

We also look at the graph to determine the direction of the linear relationship. A line that begins in the upper left corner of the plot and ends in the lower right corner like the relationship shown above is called a negative relationship.

scatter plot curvilinear relationship example

The direction of the relationship between two variables is identified by the sign of the correlation coefficient for the variables. Postive relationships have a "plus" sign, whereas negative relationships have a "minus" sign.

Curvilinear Relationship definition | Psychology Glossary | corrosion-corrintel.info

The Form Shape of a Relationship: The form or shape of a relationship refers to whether the relationship is straight or curved.

A straight relationship is called linear, because it approximates a straight line. A curved relationship is called curvilinear, because it approximates a curved line.

An example of the relationship between the Miles-per-gallon and engine displacement of various automobiles sold in the USA in is shown below.

scatter plot curvilinear relationship example

This is curvilinear and negative. In this course we only deal with correlation coefficients that measure linear relationship. There are other correlation coefficients that measure curvilinear relationship, but they are beyond the introductory level.

The Degree Strength of a Relationship Finally, a correlation coefficient measures the degree strength of the relationship between two variables.

The mesures we discuss only measure the strength of the linear relationship between two variables. Two specific strengths are: They are said to be perfectly linearly related, either positively or negatively.

scatter plot curvilinear relationship example

When two variables have no relationship at all, their correlation is 0. There are strengths in between Here are three examples: Weight and Horsepower The relationship between Weight and Horsepower is strong, linear, and positive, though not perfect.

Drive Ratio and Horsepower The relationship between drive ratio and Horsepower is weekly negative, though not zero.

The Pearson correlation coefficient is.

  • Describing scatterplots (form, direction, strength, outliers)

The Pearson correlation coefficient is. Correlations can be used to help make predictions. If two variables have been known in the past to correlate, then we can assume they will continue to correlate in the future.

Describing scatterplots (form, direction, strength, outliers) (article) | Khan Academy

We can use the value of one variable that is known now to predict the value that the other variable will take on in the future. For example, we require high school students to take the SAT exam because we know that in the past SAT scores correlated well with the GPA scores that the students get when they are in college. Suppose we have developed a new test of intelligence.