Practical Econometrics for Managerial Decision Making
| Start Date | End Date | Venue | Fees (US $) | ||
|---|---|---|---|---|---|
| Practical Econometrics for Managerial Decision Making | 31 May 2026 | 04 Jun 2026 | Riyadh, KSA | $ 3,900 | Register |
| Practical Econometrics for Managerial Decision Making | 06 Sept 2026 | 10 Sept 2026 | Kuala Lumpur, Malaysia | $ 4,500 | Register |
| Practical Econometrics for Managerial Decision Making | 15 Nov 2026 | 19 Nov 2026 | Dubai, UAE | $ 3,900 | Register |
Practical Econometrics for Managerial Decision Making
| Start Date | End Date | Venue | Fees (US $) | |
|---|---|---|---|---|
| Practical Econometrics for Managerial Decision Making | 31 May 2026 | 04 Jun 2026 | Riyadh, KSA | $ 3,900 |
| Practical Econometrics for Managerial Decision Making | 06 Sept 2026 | 10 Sept 2026 | Kuala Lumpur, Malaysia | $ 4,500 |
| Practical Econometrics for Managerial Decision Making | 15 Nov 2026 | 19 Nov 2026 | Dubai, UAE | $ 3,900 |
Introduction
This Practical Econometrics for Managerial Decision Making training course focuses on performing high-level multivariate econometric analysis using a range of business and economic data - with an emphasis on design, analysis, and drawing sound inferences to support strategic and operational decision-making. It's no longer specialty PhDs with $10,000 software packages doing high-end econometrics. This training course dispels all the myths and misconceptions about intuitive multivariate modeling and analysis, making it practical-accessible for all managers using inexpensive software applications that work within industry standard Excel spreadsheets.
This Practical Econometrics training course will highlight:
- Working with a range of multivariate models, variables, and statistical output
- Modeling and hypothesis design, variable-selection, and managing big-data
- Developing original research projects, relational hypotheses, and parameters for inferences
- Understanding benefits and costs of primary data vs. secondary data for research
- Use of cross-sectional, time-series, longitudinal, and pooled cross-sectional data sets
- Comparing original research output with published research results on various topics
- Critical review, analysis, and critique of research models, methods, and conclusions drawn
Objectives
- Design-produce an original research study
- Collect and format various types of data
- Perform different models of multivariate econometric analyses
- Analyze detailed statistical output from econometric model-software
- Draw inferences to support high-level managerial decision-making
- Write a detailed, yet succinct, executive summary of research findings
At the end of this Practical Econometrics training course, you will learn to:
Training Methodology
This Practical Econometrics training course will use an inductive reasoning approach for introducing new terms-concepts-models-methods, followed with highly interactive case-discussion, and small-group team case projects applied directly to the attendees’ firms/organizations. The main focus is “hands-on” doing high level econometric modeling, analysis, and interpretation of statistical results.
Who Should Attend?
This Practical Econometrics training course is suitable to a wide range of professionals but will greatly benefit:
- Research and Development / Product Development Teams looking for direct connections
- Business Development Staff looking to proactively open up new opportunities
- Financial Officers looking to design-execute original finance-accounting econometric research studies
- Revenue Officers looking to develop new forms and insights for marketing and competition research
- Board Members looking to fully monetize Big Data for the shareholders / stakeholders
Course Outline
Day 1: Overview of Contemporary Econometrics and Decision Models
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Model Design, Hypotheses, Variables, Structure, Outcomes
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Quantitative and Qualitative Inputs
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Applications: From Wall Street to Marketing to Production to Consumer Behavior
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Software Options
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Linking Models and Confirmation Metrics
Day 2: Understand Different Forms and Types of Research Data
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Cross Sectional Samples
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Time Series Sequences
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Longitudinal Tracking
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Pooled Cross-Sectional Aggregation
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Primary Data Costs and Acquisition
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Secondary Data Costs and Acquisition
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Descriptive Outcomes vs. Predictive Outcomes
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Dummy Variables / Indicators / Surrogates
Day 3: Model and Hypothesis Design as Keys to Managing Big Data
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Targeted Outcomes Determine Input Formations
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Single-Variable vs. Multi-Variable Descriptors and Predictors
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Punctuated Trending vs. Real-time Fluidity
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Static Formations vs. Dynamic-Changing-Active Learning Models
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Correlation and Association vs. Cause-And-Effect
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Building Real Models for Delegates’ Firms, Industries, Markets
Day 4: Designing Original Models for Your Firm, Competitive Market, and Industry
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Categorizing Decision Areas and Coordinating Data Availability
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Micro-economic vs. Macro-economic Decisions
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Indicators, Lagged Variables, Barometers / Bellwethers
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Running Several Rounds of Differing Regressions
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Managing Databases of Targeted Variables
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Problems of Multi-collinearity
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Problems of Autocorrelation
Day 5: Presenting and Evaluating-Critiquing Original Econometric Findings
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Drawing Inferences Rather than Conclusions
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Caveats of Explaining Variance
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Individual and Team Presentations and Discussion-Interaction-Critique
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Confidence Intervals in Econometric Forecasts
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Packaging Analysis-results for Optimum Explanation
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Problems with Overreach from Statistical Outputs
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Personal-Managerial Bias Impacts Interpretation
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Distilling Data Output into Actionable Intel
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Disseminating Data Output for Maximum Decision Making Impact

