Learn how to perform a one way ANOVA test on multiple examples. Learn 2 Sample T Test.ĭive into analysis of variance (ANOVA) and understand the basics. Learn Z Test and T Test through visual explanation and through JMP. Learn how to test to see if your data comes from a normally distributed population using the Shapiro Wilk Test. Understand confidence intervals and significance (alphas). Learn the assumptions for hypothesis testing. Learn an introduction to hypothesis testing and what it means. Create your own fitted lines on JMP using Fit Y by X tool. Learn how correlation coefficient can help you analyze future trends of big data. Learn about linear regression and dependent and independent variables. Section 7: Linear Regression (Fit Y by X): Save and automate reports for changing data. Post your finished analysis to the web in dashboard form to share with others. Then learn how to create distributions and what analysis you can take from it. Learn about Box Plots and Histograms in detail. Create control charts such as Pareto Charts, X Bar & R Charts and IMR Charts. Learn and create tables of descriptive statistics on JMP. Section 4: Descriptive Statistics & Quality Control Charts Learn how to create individual value plots (scatter plots), bar charts, pie charts, parallel plots, heat maps, and more. Learn how to format specific columns and how to clean data before creating graphs / distributions / analysis. Import data from a variety of sources: Excel, Google Sheets, CSV, etc. Section 2: Data Types, Column, Data Clean Up Quality Control Charts (Pareto, X Bar & R, & IMR) Here is a summary of topics covered in this course: Create opportunities for you or key decision-makers to discover data patterns such as customer purchase behavior, sales trends, quality defects, or production bottlenecks. All four data sets can be found inside JMP as sample data.Learn Statistics, Analytics and Data Visualization with JMP 15 to solve problems, reveal opportunities and inform decisions. The last one is from a study of how fast the body can absorb and use up oxygen, which contains 31 observation and 9 variables. The third one “car physical data” has information about 8 variables on 116 car models. Another consists of 50 samples from each of three species of Iris with four different features measured (see plot). One contains 408 students’ records with 19 variables related to their SAT scores. We will supplement computer lab components with lecture components.įour data sets will be used. This short course starts with basic data manipulation, and moves to some advanced features, including importing data, interactive GUI, descriptive statistics such as numerical summary, inferential statistics such as t-tests, ANOVA, and regression. This wealth of available options and analyses can be overwhelming, so this course is designed to provide some guidance for users who are new to JMP. JMP is different from other statistical programming packages such as SAS and R since a great number of sophisticated analyses are readily available without the need to write computer code.
#Jmp data analysis software
JMP is a user friendly statistical software package that puts advanced analytic capabilities within an easy-to-use graphical interface.
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