
Explore JMP, a powerful data analysis tool for visuals and statistics, covering data cleaning, graph builder, distributions, dashboards, and inferential methods from regression to ANOVA.
Here is the link to the 30 day free trial:
https://www.jmp.com/en_us/download-jmp-free-trial.html
Import CSV and Google Sheets into JMP, and clean imported data for analysis. Convert columns to numeric and currency, fix formatting, and align titles to ensure accuracy.
Learn to clean data in JMP by importing a sheet, recoding misspelled days, and creating formula columns to compare mean vs. advanced purchase days, then prepare for jump visuals.
To follow along, open the .JMP file in the resources file originally from the Sample Data.
Car Physical Data
Build and compare pie charts using the quality issues data across day, swing, and night shifts, adding labels by value or percent to reveal holistically and by shift.
Explore heat maps with the graph builder to visualize confirmed Covid cases by location, adjust color themes, and interpret the distribution across the United States.
Explore graphing options in JMP, including contour vs scatter plots, bubble plots, heat maps, and 3D scatter plots, using the travel cost data set to visualize net costs.
Use JMP's graph builder to visualize odometer distributions with histograms, comparing make types A, B, and C, and switching between count, percent, and kernel density overlays.
Import updated Google Sheets into JMP to refresh automated reports, adjust the data table to the new row count, and re-run saved distribution report with the refreshed dataset.
Learn the basics of hypothesis testing, defining H0 and Ha with examples like mu equals 800 g and four-foot height, and when to reject or fail to reject.
Visualize z test with a water bottle weight: compare 803 g and 809 g x-bar (n=16) to mu 800 g using a 95% confidence interval to reject or not reject.
Perform z tests in JMP to compare population means of 97 and 96 with known standard deviation, after checking normality via Shapiro‑Wilk; conclude reject 97, fail to reject 96.
Perform two-sample t tests in JMP to compare means between independent samples, test equal vs unequal variances with Levene, and verify the null mean difference is zero.
Sample data available on JMP:
Datasets -> Help -> Sample Data -> "Typing Data"
Sample data available on JMP:
Datasets -> Help -> Sample Data -> "Typing Data"
Learn Statistics, Analytics and Data Visualization with JMP 15 to solve problems, reveal opportunities and inform decisions. Create opportunities for you or key decision-makers to discover data patterns such as customer purchase behavior, sales trends, quality defects, or production bottlenecks.
What You'll Learn:
Here is a summary of topics covered in this course:
Hypothesis Testing
Normal Distributions
Shapiro Wilk Test
Z Test & T Test
2 Sample T Test
ANOVA
One Way ANOVA
T Test & Tukey Test
Data Visualization
Descriptive Statistics
Quality Control Charts (Pareto, X Bar & R, & IMR)
Distributions
Linear Regression
Fit Y by X
(Pearsons) Correlation Coefficient
Data Clean Up
Publish sharable Analysis & Dashboards
Section 2: Data Types, Column, Data Clean Up
Import data from a variety of sources: Excel, Google Sheets, CSV, etc. Learn how to format specific columns and how to clean data before creating graphs / distributions / analysis.
Section 3: JMP Visuals & Graphing
Learn how to create individual value plots (scatter plots), bar charts, pie charts, parallel plots, heat maps, and more.
Section 4: Descriptive Statistics & Quality Control Charts
Learn and create tables of descriptive statistics on JMP. Create control charts such as Pareto Charts, X Bar & R Charts and IMR Charts.
Section 5: Distributions Overview
Learn about Box Plots and Histograms in detail. Then learn how to create distributions and what analysis you can take from it.
Section 6: Publish Dashboards
Post your finished analysis to the web in dashboard form to share with others. Save and automate reports for changing data.
Section 7: Linear Regression (Fit Y by X):
Learn about linear regression and dependent and independent variables. Learn how correlation coefficient can help you analyze future trends of big data. Create your own fitted lines on JMP using Fit Y by X tool.
Section 8: Hypothesis Testing:
Learn an introduction to hypothesis testing and what it means. Learn the assumptions for hypothesis testing. Understand confidence intervals and significance (alphas). Learn how to test to see if your data comes from a normally distributed population using the Shapiro Wilk Test. Learn Z Test and T Test through visual explanation and through JMP. Learn 2 Sample T Test.
Section 9: ANOVA
Dive into analysis of variance (ANOVA) and understand the basics. Learn how to perform a one way ANOVA test on multiple examples. Expand your knowledge through learning how to perform ANOVA T Tests & Tukey tests.
Are there any course requirements or prerequisites?
Willingness to learn
A part of this course on the use of JMP 15. You will need to have the software to practice. Please note that you can download 30 days trial version of this software from JMP's website.
Who this course is for:
You should take this course if want to learn JMP completely from scratch
You should take this course if you know some JMP skills but want to get better
You should take this course if you are good with JMP and want to take your skills to the next level and truly leverage the full potential of JMP