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How to Use SAS JMP Statistical Discovery 9.0.2 for Data Analysis and Visualization

SAS JMP Statistical Discovery is a software that helps you explore and understand your data, perform statistical tests, and create interactive graphs and reports. It is designed for scientists, engineers, and analysts who want to discover insights from their data and communicate them effectively.

In this article, we will show you how to use SAS JMP Statistical Discovery 9.0.2 for some common data analysis and visualization tasks. We will use a sample data set of customer satisfaction ratings for different products and services. You can download the data set from here.

Step 1: Importing and Cleaning the Data

To import the data into SAS JMP Statistical Discovery, go to File > Open and select the file Customer Satisfaction.jmp. You will see a table with 10 columns and 100 rows of data. Each row represents a customer and each column represents a variable.

Before we start analyzing the data, we need to check if there are any missing values or errors in the data. To do this, go to Tables > Summary and select all the columns. You will see a summary table that shows the number of missing values, minimum, maximum, mean, standard deviation, and other statistics for each variable.

From the summary table, we can see that there are no missing values in the data. However, we can also see that some of the variables have values that are outside the expected range. For example, the variable Age has a minimum value of -1 and a maximum value of 99, which are not realistic. Similarly, the variable Satisfaction has a minimum value of 0 and a maximum value of 6, which are not consistent with the scale of 1 to 5 that was used to measure customer satisfaction.

To fix these errors, we can use the Recode command in SAS JMP Statistical Discovery. To recode the variable Age, go to Cols > Recode and select Age. In the Recode window, click on New Formula Column and enter the following formula:

if(Age < 0 | Age > 90,

.,

Age

)

This formula will replace any value of Age that is less than 0 or greater than 90 with a missing value (denoted by .). Click OK to create a new column called Recoded Age with the recoded values.

To recode the variable Satisfaction, go to Cols > Recode and select Satisfaction. In the Recode window, click on New Formula Column and enter the following formula:

if(Satisfaction < 1 | Satisfaction > 5,

.,

Satisfaction

)

This formula will replace any value of Satisfaction that is less than 1 or greater than 5 with a missing value. Click OK to create a new column called Recoded Satisfaction with the recoded values.

Step 2: Exploring and Visualizing the Data

Now that we have cleaned the data, we can start exploring and visualizing it using SAS JMP Statistical Discovery. One of the most powerful features of SAS JMP Statistical Discovery is its Graph Builder tool, which allows you to create interactive graphs by dragging and dropping variables onto different zones.

To launch the Graph Builder tool, go to Graph > Graph Builder. You will see an empty canvas with four zones: X, Y, Color, and Group X. You can drag any variable from the data table onto any zone to create a graph.

For example, if you want to create a scatter plot of Recoded Satisfaction versus Price Paid, you can drag Recoded Satisfaction onto the Y zone and Price Paid onto the X zone. You will see a scatter plot with dots representing each customer.

If you want to add another variable to the graph, you can drag it onto another zone. For example, if you want to color-code the dots by Product Type, you can drag Product Type onto the Color zone. You will see a scatter plot with different colors for different product types.

If you want to create multiple graphs for different groups of customers, you can drag a variable onto the Group X zone. For example, if you want to create separate scatter plots for male and female customers, you can drag Gender onto the Group X aa16f39245