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Practical Econometrics Course for Effective Managerial Decision Making » MAL76

Practical Econometrics Course for Effective Managerial Decision Making

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Why select this training course?

The Rcademy Practical Econometrics Course for Effective Managerial Decision-Making emphasizes design, analysis, and how to draw sound inferences to support strategic and operational decision-making. Participants are exposed to how to perform high-level multivariate econometric analysis using a variety of business and economic data. The training course integrates advanced multivariate econometrics modeling ideas, variable analysis, interpretation, and applications to forecasting and managerial decision-making. In addition to performing fundamental econometric analysis, the course teaches participants how to interpret and apply econometrics outputs to various quantitative and qualitative problem-solving situations. It covers all aspects of hypothesis testing, variable selection, data types, statistical analysis, error tolerances, inferences, insights, correlations, and trends. The course begins with the basics of econometrics, an overview of managerial decision-making and economics, and ends with critiquing original econometric findings.

What is an effective managerial decision-making process?

Decisions determine how a business will develop. While making, the correct choices can boost profitability, foster growth, and sustain stability, making the wrong ones can have the opposite effect and even endanger the health of a firm. Decisions made by managers, therefore, have a significant impact on businesses. The goal of an efficient managerial decision-making process is to enable the effective and efficient performance of corporate tasks while also resolving recognized problems. It requires being able to comprehend data, interpret it, and use it.

What is the relevance of statistics in effective managerial decision-making?

The mathematical and statistical examination of economic relationships is frequently used as a foundation for financial forecasting. Governments occasionally utilize such data to formulate monetary policy, while private businesses use it to make choices regarding prices, inventories, and output. Despite the abundance of data available, statistics give managers greater confidence in handling uncertainty, allowing them to act more quickly and wisely while also giving their workforce, who depend on them, stronger leadership.

Who should attend?

The following personnel should take The Practical Econometrics Course for Effective Managerial Decision-Making Training Course by Rcademy:

  • Product Developers are charged with effectively overseeing the process of creating new products or improving old ones to meet client expectations
  • Researchers are tasked with organizing, collecting, and evaluating opinions and data to solve issues, explore issues and predict trends
  • Business Developers are responsible for working with businesses in a range of industries to aid them in achieving their corporate objectives
  • Financial Officers tasked with generating cheques for tax, payroll, cheques, and other bills of the company, keeping and balancing financial records
  • Revenue Officers working on behalf of the government to collect funds such as taxes and duties
  • Board Members in charge of identifying and monitoring the organization’s programs, services, and products
  • Financial analysts are responsible for giving corporations or individuals sound advice on business investment decisions
  • Chief executive officers are accountable for carrying out obligations as the top executive within a company to direct company practices and procedures
  • Strategy managers are charged with assisting the senior executive leadership in establishing the goals and objectives of the company
  • Every other person interested in data science, statistics, and hypothesis testing

What are the course objectives?

The Practical Econometrics for Managerial Decision-Making Training Course by Rcademy is designed to help participants achieve the following objectives:

  • Recognize the fundamental concepts, terminology, and modeling used in modern economics
  • Develop a new managerial outlook on the best methods for doing applied business research
  • Understand the use of formal, unbiased economic models and statistical results
  • Able to evaluate the data, results, and conclusions of existing econometric studies with objectivity
  • Discover proactive, forward-looking methods for handling massive data for research design
  • Bring sensible, real-world econometrics to internal and external enterprises
  • Recognize techniques for obtaining information to aid in making decisions
  • Learn to evaluate and rate alternatives to find the best solutions

How will this course be presented?

This course is designed to fulfill the needs of participants while also improving their knowledge and skills in the field. This course will be presented using a variety of practical ways to ensure attendees’ active and continuous learning. Renowned experts and professionals with years of work and experience will teach the course. The course modules are also based on thorough research into the subject.

The Rcademy Practical Econometrics Course for Effective Managerial Decision-Making integrates practical and theoretical learning by providing attendees with cases, studies, lectures, slides on the concepts, and real-life scenarios. Participants will also engage in presentations, seminar workshops, quizzes, and regular feedback on lessons learned to confirm their optimum satisfaction.

What are the topics covered in this course?

Module 1: Basics of Econometrics

  • Introduction to econometrics
  • Introduction to sense-making
  • Types of variables
  • Types of economic data
  • The goals of econometrics
  • The stages of econometrics
  • Residuals and regression types
  • Scales of measurement

Module 2: Overview of Managerial Decision-Making

  • Introduction to managerial decision-making
  • Strategic analysis
  • Programmed and non-programmed decisions
  • Strategic management process
  • Decision-making process
    – Decision recognition
    – Alternative generation
    – Implementation and evaluation
    – Alternative analysis and selection

Module 3: Managerial Economics

  • Introduction
  • Nature of managerial economics
  • Scope of managerial economics
  • Principles of managerial economics
  • Consumer demand
  • Price elasticity of supply
  • Managerial and microeconomics

Module 4: Econometrics: Methods and Applications

  • Introduction
  • Simple regression
  • Model specification
  • Multiple regression
  • Endogeneity
  • Binary choice
  • Time series

Module 5: Overview of Contemporary Econometrics and Decision Models 

  • Introduction
  • Confirmation metrics
  • Model design and outcomes
  • Structure, hypotheses, and variables
  • Qualitative inputs
  • Quantitative inputs
  • Software Options
  • Linking models

Module 6: Observational Decision-Making with Econometrics

  • Introduction
  • Non-central location measures
  • Central location measures
  • Quantifying dispersion in sample data
  • Transformation of numeric descriptors to profile numeric sample data
  • Breakdown analysis of numeric measures
  • Numeric measures distribution (Bimodal and skewness)
  • Relationships between numeric descriptors

Module 7: Understand Different Types and Forms of Research Data

  • Longitudinal tracking
  • Pooled cross-sectional aggregation
  • Cross-sectional samples
  • Surrogates and indicators
  • Time series sequences
  • Primary acquisition and data costs
  • Secondary acquisition and data costs
  • Descriptive outcomes
  • Dummy variables
  • Predictive outcomes

Module 8: Keys to Managing Big Data: Model and Hypothesis Design

  • Targeted outcomes input formations
  • Single-variable descriptors and predictors
  • Multi-variable descriptors and predictors
  • Punctuated Trending
  • Real-time Fluidity
  • Static figures, active learning models
  • Dynamic-changing active learning models
  • Association and correlation cause-and-effect
  • Accurate models for delegates’ markets, industries, and firms

Module 9: Models for Firm, Industry, and Competitive Market 

  • Coordinating data availability
  • Managing databases of targeted variables
  • Micro-economic decisions
  • Macro-economic decisions
  • Barometers and bellwethers
  • Categorizing decision areas
  • Indicators and lagged variables
  • Rounds of differing regressions
  • Problems of multi-collinearity
  • Problems of autocorrelation

Module 10: Presenting and Evaluating: Critiquing Original Econometric Findings

  • Packaging analysis results for optimum explanation
  • Caveats in explaining variance
  • Drawing inferences rather than conclusions
  • Confidence intervals in econometric forecasts
  • Problems with overreach from statistical outputs
  • Discussion, critique, and interaction
  • Personal managerial bias impacts the interpretation
  • Data output distillation
  • Data output dissemination

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