|
|
Business Statistics and Data Analysis |
|
3 Days |
|
|
|
|
|
|
|
|
|
|
|
Course Description and Audience:
BSDA is for Marketing, Sales, HR, Business Analysts and Managers who routinely
analyze data for business application. Areas of focus are foundation statistics,
distribution analysis, capability assessment, graphing, prediction, forecasting,
comparison tests, sample size selection and model fitting. |
|
|
|
|
|
|
|
|
|
Course Objectives:
Upon completion of the course the participants will be able to:
- Understand the ideas associated with sampling and data collection.
- Demonstrate the ability to evaluate distributions.
- Select appropriate sample sizes for performance evaluation.
- Conduct comparative tests using data.
- Use regression techniques in order to analyze the results and make performance improvements.
- Select an appropriate analysis technique based on the type of data.
|
|
|
|
|
|
|
|
|
|
Software: JMP
Prerequisites: None |
|
|
|
|
|
|
|
|
|
Detailed Course Outline:
Introduction to (JMP)
Table commands
Column commands
Row commands
Subset, Stack and Join commands
Saving data and graphs
Statistics Foundations & Distribution Analysis
Measures of center and spread
Standard error and central limit theorem
Normal distribution, t distribution and confidence intervals
Test for normality
Data and tolerance intervals (normal)
Process capability (normal) and non-normal distribution fitting
Nominal X, Continuous Y
Contour plots, Components of Variance, REML and POV
Sample size for the mean and standard deviation
t test – one sample, two sample and paired
Test for differences in variances
One-way ANOVA and N way ANOVA
Continuous X, Continuous Y
Simple linear regression, correlation
Multiple Regression and ANCOVA
Forcasting and time series analysis
Nominal X, Nominal Y
Mean and Sigma for proportion defective
Sample size and statistical tests for proportion defective
Mean and Sigma for defect per unit
Chi-square test for defects and proportion defective
Pareto graphs and cross tabs analysis
Continuous X, Nominal Y
Logistic regression
|
|
|
|
|
|
|
|
|
|
|