Advanced Business Statistics and Data Analysis
Statistical and Analytical Courses
This course is for all Marketing, Sales, HR, Business Analysts and Managers who routinely analyze data for business application. Areas of focus are analysis of data for business planning, forecasting, data mining, variation analysis and multiple factor modeling. This course is designed for 16 hours of presentation.

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Download the full curriculum for Statistical and Analytical courses in PDF format.

Biotech, Pharmaceutical & Medical Device Courses

Systematic product development, Quality by Design courses, consulting services and analytical training for biotechnology, pharmaceutical and medical device industries. QbD provides guidance to facilitate design of products and processes that maximize the product’s efficacy and safety profile while enhancing product manufacturability and control.

Lean Six Sigma

Complete curriculum for new product development, manufacturing and business process performance optimization.

Tools & Templates

Development tools and templates created by Thomas A. Little Consulting have been used by numerous companies to aid and support various aspects of product development, problem solving, data analysis and risk assessment.

This course is required for all Marketing, Sales, HR, Business Analysts and Managers who routinely analyze data for business application.
Business Statistics and Data Analysis
Course Objectives
  1. Use data to solve business and transactional problems
  2. Select appropriate analysis technique based on type of data.
  3. Analyze complex multifactor data sets.
  4. Estimate the effect size from the data relative to the business case of interest.
  5. Generate prediction equations to predict business behavior based on critical inputs
  6. Use multiple regression techniques in order to analyze data and make business process improvements.
Detailed Course Outline
Section I: Advanced Graphs
Advanced Pareto Plots
Confidence Intervals and Tests
Stacked Bar Graphs
Graph Builder
Section II: Variation Analysis
POV Analysis
MSA for Attributes
Section III: Data Mining
Recursive Partitioning
Section IV: Time Series and Forecasting
Seasonal ARIMA
Learning Curves
Nonlinear Regression
Section V: Multiple Factor Analysis
Multiple Regression