Basic JMP is vital to develop analytical skills. Better decisions are outcome of such skills and it‘s for both researchers and business managers.

Basic JMP Training 

What you will learn:
1. Quantitative data collection,
2. Data entry, data cleaning, analysis and decision making using JMP
3. From problem statement to research questions and frameworks (models) and/or hypothesis testing using JMP
4. Differentiate and use different statistical tests to solve problems using JMP
5. Writing Interpretation and report. 

The focus of this class is on using SPSS to solve problems.

Subjects that we cover:
- Basic Statistics using JMP
- Inferential Statistics using JMP
- Type of variables and measurement levels in JMP
- Sample and Population
- Data with JMP
- Entering and Cleaning Data using JMP
- Descriptive Statistics using JMP
- Frequency tables, Pie chart, Bar chart, Histogram using JMP
- Mean, Mode, Median using JMP
- Skewness, Standard Deviation, Variance using JMP 
- Hypothesis Testing for differences
- t-tests using JMP( 1-sample test , 2-smaple test and paired t-test)
- Analysis of Variance (ANOVA) using JMP
- Hypothesis Testing for Relations using JMP
- Graphing scatter diagram using JMP
- Correlations and Regression Analysis using JMP(Simple linear regression)
- graphing results using JMP and data visualization basics
Decision Analysis using the results of JMP
Interpretation, sensitivity analysis, writing report, conclusion and more.


- Review of Descriptive Statistics, 

- Scale of Measurement
- Tests & Assumptions
- Multivariate Statistics using JMP
     Why Study Multivariate Statistics using JMP
     Univariate and Bivariate Statistics using JMP
- Reliability using JMP

- Data Appropriate for Multivariate Statistics using JMP
     Correlation and Partial Correlation using JMP
     Hypothesis Testing using JMP
- Multiple Regression using JMP (1)
     General Purpose and Description using JMP
     Theoretical Issues: Assumptions using JMP
     Data Analysis and Interpretation using JMP
- Multiple Regression using JMP (2)
     Factor Analysis using JMP(Exp. & Com.)
     Basics of Logistic Regression using JMP(LR)
     Interpretation of LR
- Statistical technics to compare groups using JMP(1)
      Non Parametric statistics using JMP
      T test using JMP
      One way ANOVA using JMP
- Statistical technics to compare groups using JMP(2)
      Two ways ANOVA using JMP
      MANOVA using JMP
      Analysis of Covariance using JMP(ANCOVA)
      Introduction to SEM using JMP (Structural Equation Modelling)

Link to : Basic Excel training   -   Excel training  -  SPSS training  -  DEASolver training  -   Microsoft Word Training  -  Microsoft PowerPoint Training

Why JMP?

JMP is an integrated set of analysis tools and modules for the whole analysis process from questionnaire design and improvement, data collection and data cleaning to organizing, data visualization, graphing, presenting and analyzing data. JMP is very user friendly and more informative than many other softwares. In addition, JMP can be connected to SAS and you can create SAS codes using JMP.

Using SPSS , you can analyze, find relations among factors, predict effect of factors on each other with confidence and based on the results you can make better decisions.

SPSS provides a professional tool for instructors to teach statistics and decision making effectively.

SPSS provides professional analytics tools for a wide range of subjects in Colleges and Universities such as social sciences, management, economics, mathematics and statistics, tourism, education, engineering and many others.

Why SPSS is better than Microsoft Excel

It is much easier to use SPSS because it has been designed for statistical analysis.

SPSS is more powerful than Excel because of more analytics tools available in the package.

SPSS has better design when it comes to data entry, organizing data and analytical graphs.

Pivot tables is SPSS are more flexible and more powerful than in Excel.

SPSS has almost 99% of statistical tests in the packages built in the software plus some management tools such as factor reduction, quality control tools, categorical data analysis and random data creator based on required probability distributions.

SPSS has better presentation of results and that helps a lot in interpretation and report writing.