Basics Steps of Correlation Analysis in JMP Software.pdf

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1. Basics of
Correlation
Analysis in JMP
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2. Introduction
Correlation analysis is one of the basic statistical
procedures used to examine the relationship between
two or more variables. Knowing how variables are
related can yield valuable insights applicable in
various disciplines starting from economics to biology
and engineering.
JMP : It is one of the most commonly used statistical
analysis software designed by SAS company and is
very effective for performing correlation analysis. In
this presentation, we will guide you through the basic
steps of performing correlation analysis in JMP using
“Auto MPG” dataset which is used to analyze the
performance of a car depending on different
attributes.

3. What is Correlation ?
Before exploring the steps involved in JMP, let’s define
what correlation is. In simple terms, correlation measures
is the extent (in terms of direction and strength) to which
2 continuous variables are related linearly. The
correlation coefficient, denoted as r, ranges from -1 to +1.
qr = +1: A perfect positive linear relationship between
variables. As one variable increases, the other increases
as well.
qr = -1: A perfect negative linear relationship. As one
variable increases, the other decreases.
qr = 0: No linear relationship between the variables.
The closer the correlation coefficient is to +1 or -1, the
stronger the linear relationship.

4. Getting Started
with JMP
Software

5. JMP has friendly user interface for both beginners as well as professional users. For performing
correlation analysis, let us consider using the ‘Auto MPG’ dataset containing features such as MPG,
number of cylinders, displacement and horsepower, weight, acceleration, year of manufacture and
country of origin. The data set can be either imported from JMP integrated data library or maybe
downloaded from a data base such as the UCI Machine Learning Repository.
Step 1: Load the Dataset
1.Open JMP and start a new project.
2.Go to File > Open and load the "Auto MPG" dataset, which should be in
.jmp, .csv, or .xls format. Once the dataset is loaded, you will see all the
variables in the data table.

6. Step 2: Overview of
the Data
After loading the data, spend some time to study the
variables. The key attributes we will focus on for
correlation analysis are:
qMPG: Miles per gallon, a measure of fuel efficiency.
qHorsepower: The power output of the vehicle.
qWeight: The weight of the car.
qAcceleration: The time taken for the car to accelerate
from 0 to 60 mph.

7. Step 3: Exploring the Data with
Graph Builder
Step 4: Launching
the Correlation
Platform
Before performing the correlation, visualizing the data
using JMP’s Graph Builder is helpful. See the steps
below:
•Click on Graph in the toolbar and select Graph
Builder.
•Drag and drop variables such as MPG, Horsepower,
and Weight into the graph. This allows you to
visually inspect potential relationships between
variables.
•For example, if you plot MPG against Horsepower,
you might see an inverse relationship, indicating
that as horsepower increases, MPG decreases.
•Visualizing relationships give you some clarity on
of how the correlation coefficients will behave.
Now, let us perform the actual
correlation analysis:
•Go to Analyze > Multivariate
Methods > Multivariate.
•In the dialog box, select the
variables you want to include
in the correlation analysis. For
this example, we will select
MPG, Horsepower, Weight,
and Acceleration.
•Click OK to generate the
correlation matrix.

8. Interpreting the Correlation Matrix
Once the correlation matrix is generated, a table gets displayed that shows the
correlation coefficients between all selected variables. Here's how to interpret the
results:
MPG and Horsepower: You might see a negative correlation, indicating that as
horsepower increases, MPG decreases. This implies that cars having more powerful
engines are less fuel-efficient.
MPG and Weight: There will likely be a strong negative correlation, implying that heavier
cars are less fule-efficient.
Horsepower and Weight: These two variables may show a positive correlation, indicating
that heavier cars usually have more powerful engines.
Acceleration and MPG: There might be a weak positive or negative correlation, depending
on the characteristics of the dataset.
STEP 5

9. Step 6: Visualizing
Correlation with
Pairwise Plots
JMP also facilitates you to view these relationships with the help
of pairwise plots, which provide scatterplots for each pair of
variables:
1.In the Multivariate dialog box, click on the red triangle next to
the correlation matrix and select Pairwise Plots.
2.For each pair of variables, JMP will show scatterplots in order
to visualize the relationship. For example, the scatterplot of MPG
vs Weight will likely show a downward trend, consistent with
the negative correlation observed in the matrix.

10. Advanced Options
for Correlation in
JMP
•Partial Correlation: To make adjustments on the
effect of one or more variables, a partial
correlation test is usually done. It can be
accessed under the red triangle present in the
basic window of Multivariate platform.
•Non-linear Relationships: Although correlation
analysis describes linear relations, JMP offers
additional tools for analyzing non-linear relations
as well. using the Fit Y by X feature you can fit
different type of models such as polynomial and
exponential fits.

11. Practical Example:
Correlation Analysis
Interpretation

12. Variables Correlation Coefficient (r)
MPG vs Weight -0.85
MPG vs Horsepower -0.78
MPG vs Acceleration 0.42
Horsepower vs Weight 0.79
let’s interpret a hypothetical outcome from the correlation
matrix:

13. from this matrix:
•The negative correlation coefficient of -0.85 of MPG and Weight indicates that
vehicles with higher weight have lower miles per gallon.
•The negative coefficient calculated for MPG and Horsepower (-0. 78) corroborates the
hypothesis that cars with physically powerful engines are less fuel efficient.
•A moderate positive relationship between MPG and Acceleration (0. 42) could imply
that cars with better acceleration or faster 0-60 times tend to get better MPG.
•Lastly, the coefficients calculated for Horsepower and Weight wherein the coefficient
of 0.79 shows that, in general, the weight does correlate with the power of the cars.

14. Benefits of JMP
Assignment Help
for Students

15. Benefits of JMP Assignment
Help for Students
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16. Applied Multivariate Statistical
Analysis by Richard A. Johnson and
Dean W. Wichern – This is a
comprehensive book on
multivariate statistics, with detailed
coverage of correlation and other
related topics.
Statistics for Business and Economics
by Paul Newbold, William L. Carlson,
and Betty Thorne – A great resource
for students looking to apply
statistical methods in business
contexts, including correlation
analysis.
Recommended Textbooks

17. Thank
you very
much!
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