Techniques and Tools for Data-Driven
Business Decisions in the Face of Uncertainty
In today’s digital economy, managers need to make decisions in the face of uncertainty. Data has become ubiquitous. And so has the
potential to turn data into revenue-producing insights and actions. But how do you make that happen?
Being able to make decisions informed by data is not optional—it is essential. Organisations that make decisions based on data
simply perform better. During the workshop, you will learn to develop data-driven models and analysis using statistical methods.
Important tools in the business statistics toolbox are hypothesis tests, confidence intervals and regression analysis. A mastery
over hypothesis tests and confidence intervals allows you to understand and measure the extent of risk and uncertainty in various business processes. Regression analysis is the engine behind a multitude of data analytics applications used for many forms of
forecasting and prediction. A mastery of these techniques and tools will improve your business decision-making process.
The purpose of the course is the understanding and application of business analytics, rather than detailed mathematical derivations.
The techniques are explained using easy to understand examples in MS Excel.
By the end of the course, participants will:
- Learn techniques towards a principled approach for data-driven decision-making
- Quantitative modelling of dynamic nature of decision problems using historical data
- Learn various approaches for decision-making in the face of uncertainty
- Business applications of hypothesis testing and confidence interval estimation
- Master the various calculations to constructing confidence intervals and to conduct different kinds of hypothesis tests.
Why you should attend?
- Become proactive in understanding and leveraging business data, and data-driven decision making, to gain a competitive edge
- Understand best-practice methods to collect data
- Employ real-world business analytics utilised at top firms
- Formulate data-driven recommendations for strategic business decisions
- Understand modelling tools and best practices
Clear explanation of theories coupled with hands-on exercises for a firm grasp of Business Analytics through:
- Practical Examples
- Case Studies
- Short Exercises
- Group Discussions
Who Should Attend
Business professionals in operations, management, finance, sales, marketing, information technology, supply chain logistics and human resources. This course is for any business professional who wants to learn the tools and techniques in business analytics to drive key business results.
The course is demonstrated
using Microsoft Excel 2016.
Session 1: ANALYTICS
- What is analytics?
- Hypothesis-driven analytics strategy
- Ask questions:
- What is happening?
- Why is it happening?
- Propose, analyse and test hypotheses
Session 2: HYPOTHESIS TESTING AND CONFIDENCE INTERVAL
- What is confidence interval?
- How is confidence interval constructed?
- t-distribution, t-statistic and z-statistic
- T.DIST and T.INV excel functions
- Construct confidence intervals using z-statistic and t-statistic
Session 3: BUSINESS APPLICATIONS OF CONFIDENCE INTERVAL
- Various business applications of the confidence interval
- Use the confidence interval to calculate an appropriate sample size
- Confidence interval for a population proportion
Session 4: HYPOTHESIS TESTING
- The logic of hypothesis testing
- The four steps for conducting a hypothesis test
- Hypothesis tests for a population mean
- Hypothesis tests for a population proportion
- Guidelines, Formulas and an Application of Hypothesis Test
- Difference between single tail hypothesis tests and two tail hypothesis tests
- Type I and Type II errors in a hypothesis and ways to reduce such errors.
Session 5: HYPOTHESIS TEST - DIFFERENCES IN MEAN
- Apply hypothesis tests to test the difference between two different data - difference-in-means hypothesis test
- Business applications of the three kinds of difference-inmeans hypothesis test
- Excel dialog box to conduct hypothesis tests
- The equal & unequal variance assumption and the paired t-test for difference in means
Session 6: REGRESSION ANALYSIS
- What is linear regression?
- Build a regression model and estimating it using Excel
- Making inferences of relationships between various variables using the estimated model
- Using the regression model to make predictions
- Errors, Residuals and R-square
Session 7: REGRESSION ANALYSIS: HYPOTHESIS TESTING AND GOODNESS OF FIT
- Hypothesis testing in a linear regression
- ‘Goodness of Fit’ measures (R-square, adjusted R-square)
Session 8: REGRESSION ANALYSIS: DUMMY VARIABLES, MULTICOLLINEARITY
- Dummy variable regression (using categorical variables in a regression)
- Interpretation of coefficients and p-values in the presence of Dummy variables
- Multicollinearity in regression models and how to deal with it
Session 9: REGRESSION ANALYSIS: VARIOUS EXTENSIONS
- Mean centering of variables in a regression model
- Building confidence bounds for predictions using a regression model
- Interaction effects in a regression
- Transformation of variables in a regression
- The log-log and semi-log regression models
Session 10: CASE STUDIES AND BUSINESS APPLICATIONS OF HYPOTHESIS TESTING AND REGRESSION ANALYSIS