Learning Outcomes
On completion of this course, the learner should be able to:
Demonstrate an understanding of statistical methods used in decision-making.
Demonstrate an understanding and application of statistical and mathematical models for estimation and forecasting.
Demonstrate an understanding and application of techniques used in solving optimization problems in management.
Course Overview (Video)
Course Curriculum
Unit 1 - Data Collection
Unit 2 - Sampling
Unit 3 - Data Classification
Unit 4 - Measures of Location
Unit 5 - Measures of Dispersion
Unit 6 - Measures of Skewness
Unit 7 - Regression Analysis
Unit 8 - Correlation Analysis
Unit 9 - Index Numbers
Unit 10 - Network Analysis
Unit 11 - Decision Theory
Unit 12 - Forecasting
Unit 13 - Linear Programming
Unit 14 - Probability
Unit 15 - Discrete Distributions
Unit 16 - Binomial Distributions
Unit 17 - Poisson Distribution
Unit 18 - Normal Distributions
Unit 19 - Statistical Inference
Unit 20 - Estimation Theory
Unit 21 - Hypothesis Testing
Unit 22 - Non-Parametric Tests
Unit 23 - Linear Algebra & Calculus
Unit 24 - Statistical Quality Control
Unit 1 - Data Collection
Lecture 1 - Data Collection Techniques
Unit 2 - Sampling
Lecture 1 - Sampling Techniques
Unit 3 - Classification And Presentation of Data
Lecture 1 - Classification of Data
Lecture 2 - Frequency Distribution Table
Lecture 3 - The Histogram
Lecture 4 - The Lorenz Curve
Lecture 5 - Constructing the Lorenz curve
Lecture 6 - Lorenz curve for ungrouped data
Unit 4 - Measures of Location
Lecture 1 - The Mode
Lecture 2 - The Median
Lecture 3 - The Mean
Unit 5 - Measures of Dispersion
Lecture 1 - Measures of Dispersion Explained
Unit 6 - Measures of Skewness
Lecture 1 - Measures of Skewness Explained
Unit 7 - Regression Analysis
Lecture 1 - Regression Analysis Explained
Lecture 2 - Least-Squares Regression Y on X
Lecture 3 - Least Squares Regression X on Y
Unit 8 - Correlation Analysis
Lecture 1 - Correlation Analysis Explained
Lecture 2 - Product moment Correlation Coefficient
Unit 9 - Index Numbers
Lecture 1 - Introduction to Index Numbers
Lecture 2 - Laspeyres and Paasche Index
Unit 10 - Network Analysis
Lecture 1 - Introduction to Network Analysis
Lecture 2 - Drawing the Activity Network
Lecture 3 - Identifying the CRITICAL PATH
Lecture 4 - Identifying CRITICAL ACTIVITIES
Unit 11 - Decision Theory
Lecture 1 - Decision Theory Explained
Lecture 2 - Expected Monetary Value Approach
Unit 12 - Forecasting & Time-Series Analysis
Lecture 1 - The Forecasting Techniques
Lecture 2 - The Moving Average Method
Lecture 3 - The Z-chart
Lecture 4 - Least-squares Method
Lecture 5 - Exponential Smoothening Method
Unit 13(1) - Linear Programming - Graphical Method
Lecture 1 - Linear Programming Procedures
Lecture 2 - Formulating the LP Model
Lecture 3 - Obtaining Coordinates for Plotting
Lecture 4 - Graphing the LP Model
Unit 13(2) - Linear Programming - Simplex Method
Lecture 1 - Simplex Method Explained
Lecture 2 - Formulating the LP Model
Lecture 3 - The Initial Tableau
Lecture 4 - The Pivot Number
Lecture 5 - Row Operations
Lecture 6 - Final Tableau & Interpretation
Unit 14 - Probability
Lecture 1- Probability Explained
Unit 15 - Discrete Distributors
Lecture 1 - The Probability of a Discrete
Lecture 2 - The Mean of a Discrete
Lecture 3 - The Variance of a Discrete
Lecture 4 - The Standard Deviation
Unit 16 - Binormal Distributors
Lecture 1 - Mean and Variance of Binomial
Lecture 2 - Probability of a Binomial
Lecture 3 - Binomial Approximation to Normal
Unit 17 - Poisson Distributors
Unit 18 - Normal Distributors
Lecture 1- Normal Distributions Explained
Unit 19 - Statistical Inference
Lecture 1- Statistical Inference Explained
Unit 20 - Estimation Theory
Lecture 1- Estimation Theory Explained
Unit 21(1) - Hypothesis Testing (Large Samples)
Lecture 1 - Steps in a Hypothesis Test
Lecture 2 - Testing Large Samples (Z-tests)
Lecture 3 - Types of Hypothesis Tests
Lecture 4 - One Sided Test of Significance
Unit 22 - Non-Parametric Tests
Lecture 1 - Steps in Chi-Square Tests
Lecture 2 - Testing Large Samples (Z-tests)
Lecture 3 - Types of Hypothesis Tests
Lecture 4 - One Sided Test of Significance
Unit 23 - Linear Algebra & Calculus
Lecture 1 - Rules of Finding Derivatives
Lecture 2 - Application of Differentiation
Lecture 3 - Point of Profit Maximization
Lecture 4 - Turning Points Explained
Lecture 5 - Determining the Turning Points
Unit 24 - Statistical Quality Control
Lecture 1 - Statistical Quality Control Explained
Lecture 2 - Plotting Control Charts
Course Resources
Pricing Plans
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Frequently Asked Questions
The course starts now and never ends! It is a completely self-paced online course. You decide when you start and when you finish.
If you pay for a Tutor support plan, you will get to attend weekly live interactive sessions with your Tutor in addition to following your own study plan.
How does life-time access sound? After enrolling you have unlimited access to this course for as long as you like across any and all devices you own.
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You will get a custom certificate signed by your lecturer for every short course and master class. Students undertaking professional courses such as CPA(U), ATD, and CTA will get their certificates and result slips from the ICPAU.
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Target Group
The course has been designed for students undertaking Quantitative Techniques under CPA(U) professional course and has been built around the official CPA(U) syllabus. The course is also designed for students undertaking Quantitative Methods and statistics at an institution of higher learning.
CPA Innocent Mugisha is a Professor of Finance and Accounting with over 10 years' experience in teaching Accounting, Finance, Taxation, Auditing and Statistics related courses including hands-on courses on common accounting system such as tally, quickbooks and sunsystems both at University and Professional level. His qualifications are; PhD (candidate), MBA(Finance), CPA(U), FCCA, CIPS, CTA and BCOM (Accounting). Innocent has also published various books on most topics in Accounting and Finance for Business and Professional Studies.