Statistics for Management Sciences I
- Sciences
- 200 level
- 3 credit units
- 189 pages
- 21 units
This course introduces students to the fundamental principles of statistics with a focus on applications in management sciences. It covers data collection methods, descriptive statistics, measures of central tendency and dispersion, probability theory, and statistical distributions. Students will learn how to summarize data, perform basic statistical inference, and apply these techniques to decision-making processes. The course also includes an introduction to nonparametric methods and index numbers.
About this course
- Difficulty
- Intermediate
- Study hours
- 156 hours
- Maths
- Intermediate
- Content
- Theoretical, practical, problem solving
- Practical work
- Yes
- Assignments
- Mini projects
- Final examination
What you'll read
The real module and unit structure of STT205, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
STT205 · UNIT 2 SOURCES AND METHODS OF STATISTICAL DATA
Random Experiment is an experiment whose outcome may not be the same even though the condition of the experiment may be the same. The experiment can be conducted repeatedly under the same conditions at different times. Example: Student’s score in an examination.
What you should be able to do
- Apply statistical techniques to analyze data
- Interpret statistical results in a management context
- Formulate and test hypotheses
- Estimate population parameters
- Use statistical software for data analysis
What it prepares you for
- Data Analyst
- Market Research Analyst
- Business Intelligence Analyst
- Financial Analyst
- Management Consultant
- Finance
- Marketing
- Healthcare
- Manufacturing
- Consulting
- SPSS
- Excel
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 4: Statistical Probability (Set Theory and Basic Concepts of Probability)
Unit 2: Bayes's Theorem and Counting Techniques
Bayes's Theorem requires a strong understanding of conditional probability and can be challenging to apply in complex scenarios.
- Module 6: Estimation and Hypothesis Testing
Unit 3: Statistical Hypotheses' Dimensions
Statistical Hypotheses' Dimensions requires a deep understanding of statistical inference and the ability to formulate appropriate hypotheses for different research questions.
A suggested way through it
13 weeks, about 120 hours in total. Yours will differ.
- Week 1Module 1: Nature of Statistics, Statistical Inquiries, Forms and Design
Unit 1: Introduction to Statistics · 5 hours
Read Unit 1: Definition of Statistical Terms, Sample, Population, Data, Variable, Parameter.. Solve self-assessment exercises on statistical terms.. Differentiate between Elementary and Composite Events..
Unit 2: Sources and Methods of Statistical Data · 5 hours
Read Unit 2: Overview of Statistical data, Definition of terms in Data Sets, Types of Data and Variables.. Differentiate between Numerical and Non-Numerical Data.. Solve self-assessment exercises on sources and methods of statistical data..
Unit 3: Statistical Inquiries, Forms and Design: Questionnaire · 5 hours
Read Unit 3: Definition of a Questionnaire, Qualities of a Good Questionnaire, Types of Questionnaire.. Design a sample questionnaire.. Solve self-assessment exercises on questionnaire design..
- Week 2Module 2: Presentation of Statistical Data
Unit 1: Descriptive Handling of Statistical Data · 5 hours
Read Unit 1: Statistical Tabulation, Kinds of Tabulation, Types of Tables.. Prepare a statistical table from a given dataset.. Solve self-assessment exercises on statistical tabulation..
Unit 2: Frequency · 5 hours
Read Unit 2: Frequency Distribution, Group Frequency Distribution, Class Interval and Class Limits.. Construct a frequency distribution table.. Solve self-assessment exercises on frequency distribution..
- Week 3Module 2: Presentation of Statistical Data
Unit 3: Diagrammatic Representation · 5 hours
Read Unit 3: Pictorial Diagram, Pictogram, Block Diagram, Scattered Diagram.. Create a pictogram to represent a given dataset.. Solve self-assessment exercises on diagrammatic representation..
Unit 4: Ratios, Percentages and Random Numbers · 5 hours
Read Unit 4: Ratios, Percentages and Random Numbers.. Calculate ratios and percentages from a given dataset.. Solve self-assessment exercises on ratios and percentages..
- Week 4Module 3: Measures of Central Tendency, Location and Dispersion
Unit 1: Measure of Central Tendency/ Location · 5 hours
Read Unit 1: Measures of Location, Arithmetic Mean, Mean of Group Data.. Calculate the arithmetic mean for grouped and ungrouped data.. Solve self-assessment exercises on measures of central tendency..
Unit 2: Fractiles - Measures of Partition and Dispersion · 5 hours
Read Unit 2: Fractiles - Measures of Partition and Dispersion.. Calculate quartiles, deciles, and percentiles.. Solve self-assessment exercises on measures of partition..
- Week 5Module 3: Measures of Central Tendency, Location and Dispersion
Unit 3: Measures of Dispersion/Spread · 5 hours
Read Unit 3: Measures of Dispersion/Spread.. Calculate measures of dispersion such as range, variance, and standard deviation.. Solve self-assessment exercises on measures of dispersion..
- Week 6Module 4: Statistical Probability (Set Theory and Basic Concepts of Probability)
Unit 1: Set theory · 5 hours
Read Unit 1: Set theory.. Perform set operations such as union, intersection, and complement.. Solve self-assessment exercises on set theory..
Unit 2: Bayes's Theorem and Counting Techniques · 5 hours
Read Unit 2: Bayes's Theorem and Counting Techniques.. Apply Bayes's theorem to solve probability problems.. Solve self-assessment exercises on counting techniques..
- Week 7Module 4: Statistical Probability (Set Theory and Basic Concepts of Probability)
Unit 3: Permutations and Combinations · 5 hours
Read Unit 3: Permutations and Combinations.. Calculate permutations and combinations.. Solve self-assessment exercises on permutations and combinations..
Unit 4: Basic Concepts of Probability · 5 hours
Read Unit 4: Basic Concepts of Probability.. Apply basic probability concepts to solve problems.. Solve self-assessment exercises on basic probability..
- Week 8Module 5: Statistical Distributions
Unit 1: Normal Distribution and Students (T) Distribution · 5 hours
Read Unit 1: Normal Distribution and Students (T) Distribution.. Apply normal and t-distributions to solve problems.. Solve self-assessment exercises on normal and t-distributions..
- Week 9Module 5: Statistical Distributions
Unit 2: Binomial Distribution · 5 hours
Read Unit 2: Binomial Distribution.. Apply binomial distribution to solve problems.. Solve self-assessment exercises on binomial distribution..
Unit 3: Poisson, Geometric and Hyper-geometric distributions · 5 hours
Read Unit 3: Poisson, Geometric and Hyper-geometric distributions.. Apply Poisson, geometric, and hyper-geometric distributions to solve problems.. Solve self-assessment exercises on these distributions..
- Week 10Module 6: Estimation and Hypothesis Testing
Unit 1: Estimation · 5 hours
Read Unit 1: Estimation.. Apply estimation techniques to solve problems.. Solve self-assessment exercises on estimation..
Unit 2: Principle of Hypothesis Testing · 5 hours
Read Unit 2: Principle of Hypothesis Testing.. Apply hypothesis testing principles to solve problems.. Solve self-assessment exercises on hypothesis testing..
- Week 11Module 6: Estimation and Hypothesis Testing
Unit 3: Statistical Hypotheses' Dimensions · 5 hours
Read Unit 3: Statistical Hypotheses' Dimensions.. Apply statistical hypotheses to solve problems.. Solve self-assessment exercises on statistical hypotheses..
- Week 12Module 7: Progressive Statistical Methods
Unit 1: Introduction to nonparametric (Methods and test based on Runs) · 5 hours
Read Unit 1: Introduction to nonparametric (Methods and test based on Runs).. Apply nonparametric methods to solve problems.. Solve self-assessment exercises on nonparametric methods..
Unit 2: Fundamentals of Index Number · 5 hours
Read Unit 2: Fundamentals of Index Number.. Apply index number techniques to solve problems.. Solve self-assessment exercises on index numbers..
- Week 13Final Revision
Final Revision · 10 hours
Review all modules and units.. Work on assignments and mini-projects.. Prepare for final examination..
Preparing for the exam
- Create flashcards for key statistical terms and formulas from Units 1-5
- Practice hypothesis testing problems from Units 6-7 weekly
- Review descriptive statistics concepts from Module 2
- Focus on understanding the assumptions and limitations of each statistical test
- Work through all self-assessment exercises and TMAs
Questions students ask about this course
What is STT205 about?
This course introduces students to the fundamental principles of statistics with a focus on applications in management sciences. It covers data collection methods, descriptive statistics, measures of central tendency and dispersion, probability theory, and statistical distributions. Students will learn how to summarize data, perform basic statistical inference, and apply these techniques to decision-making processes. The course also includes an introduction to nonparametric methods and index numbers.
How many units does STT205 have?
STT205, Statistics for Management Sciences I, has 21 units across 7 modules, over 189 pages of course material. You can read it one unit at a time.
How many credit units is STT205?
STT205 carries 3 credit units, at 200 level in Sciences.
Is STT205 hard?
STT205 is rated intermediate level, with intermediate mathematical content. It is mostly theoretical, practical and problem solving work, and it has a practical component.
How long does STT205 take to study?
About 156 hours of study, spread across its 21 units.
How is STT205 assessed?
STT205 is assessed by assignments, mini projects and final examination.
What can I do with STT205?
Data Analyst, Market Research Analyst, Business Intelligence Analyst, Financial Analyst and Management Consultant.