Introduction To Quantitative Methods Ii
- Social Sciences
- 100 level
- 2 credit units
- 260 pages
- 20 units
This course introduces students to quantitative methods in economics. It covers basic statistical definitions, data collection, measures of central tendency and dispersion. Students will learn to compute and interpret moments, skewness, kurtosis, and probability distributions. The course also explores index numbers and their applications in economic analysis, business, and finance. Emphasis is placed on applying statistical techniques to solve real-world economic problems.
About this course
- Difficulty
- Intermediate
- Study hours
- 156 hours
- Maths
- Intermediate
- Content
- Theoretical, practical, problem solving
- Practical work
- Yes
- Assignments
- Tutor Marked Assignments
- Final Examination
What you'll read
The real module and unit structure of ECO154, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
ECO154 · UNIT 1: MEANING AND RELEVANCE OF STATISTICS
This unit being the first of the introductory has been able to expose you to the meaning and scope of statistics, steps involved in carrying out statistical inquiry as well as the relevance of statistics to different fields of study and the day to day activities.
What you should be able to do
- Define and explain basic statistical concepts.
- Compute and interpret measures of central tendency and dispersion.
- Apply probability theory to solve real-world problems.
- Calculate and interpret index numbers.
- Understand the properties of moments, skewness, and kurtosis.
- Organize and present data using tables, graphs, and charts.
What it prepares you for
- Data Analyst
- Market Researcher
- Economist
- Financial Analyst
- Business Analyst
- Economics
- Business
- Finance
- Marketing
- Government
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 3: Basic Statistical Measures of Estimates
Unit 3: Measures of Dispersion
Requires strong understanding of algebraic manipulation and formula application.
- Module 4: Moment, Skewness and Kurtosis
Unit 1: Moments
Involves complex calculations and interpretation of statistical shapes.
- Module 5: Basic Statistical Measures of Estimates
Unit 3: Experimental Probability Rules
Requires understanding of conditional probability and application of Bayes' theorem.
A suggested way through it
13 weeks, about 57 hours in total. Yours will differ.
- Week 1Module 1: Basic Introduction
Unit 1: Meaning and Relevance of Statistics · 2 hours
Read Unit 1: Meaning and Relevance of Statistics.. Define statistics and its relevance.. Outline the steps in statistical inquiry.. Discuss the uses of statistics in various fields..
Unit 2: Types/Branches of Statistics · 2 hours
Read Unit 2: Types/Branches of Statistics.. Identify and explain descriptive and inferential statistics.. Understand the differences between inductive and deductive statistics..
- Week 2Module 1: Basic Introduction
Unit 3: Basic Concepts in Statistics · 2 hours
Read Unit 3: Basic Concepts in Statistics.. Define data, array, variable, sample, and population.. Distinguish between primary and secondary data.. Understand sampling techniques and sampling errors..
Unit 4: Collection of Data · 2 hours
Read Unit 4: Collection of Data.. Identify and explain various methods of data collection.. Discuss the problems associated with data collection..
- Week 3Module 1: Basic Introduction
Unit 5: Organization of Data · 3 hours
Read Unit 5: Organization of Data.. Distinguish between grouped and ungrouped data.. Construct frequency distribution tables for grouped and ungrouped data..
- Week 4Module 2: Representation o Data
Unit 1: Tables · 2 hours
Read Unit 1: Tables.. Define table and its components.. Explain the properties of a good table.. Understand the importance of tables in statistics..
Unit 2: Graphs · 2 hours
Read Unit 2: Graphs.. Define graphs and their features.. Outline the importance of graphs in statistics.. Construct line graphs..
- Week 5Module 2: Representation o Data
Unit 3: Charts · 3 hours
Read Unit 3: Charts.. Define different forms of charts.. Present data in bar charts, pie charts, and Z-charts..
Unit 4: Histogram and Curves · 3 hours
Read Unit 4: Histogram and Curves.. Construct histograms and frequency polygons.. Construct cumulative frequency curves.. Use Lorenz curves and pictograms to represent data..
- Week 6Module 3: Basic Statistical Measures of Estimates
Unit 1: Measures of Central Tendency Ungrouped Data · 3 hours
Read Unit 1: Measures of Central Tendency Ungrouped Data.. Explain the meaning and scope of measures of central tendency.. Calculate mean, median, and mode for ungrouped data..
Unit 2: Measures of Central Tendency of Grouped Data · 3 hours
Read Unit 2: Measures of Central Tendency of Grouped Data.. Compute measures of central tendency for grouped data.. Estimate measures of central tendency from curves and histograms..
- Week 7Module 3: Basic Statistical Measures of Estimates
Unit 3: Measures of Dispersion · 3 hours
Read Unit 3: Measures of Dispersion.. Define range, mean deviation, standard deviation, and variance.. Compute and interpret measures of dispersion for different forms of data..
Unit 4: Measures of Partition · 3 hours
Read Unit 4: Measures of Partition.. Define quartiles, deciles, and percentiles.. Calculate measures of partition for both grouped and ungrouped data..
- Week 8Module 4: Moment, Skewness and Kurtosis
Unit 1: Moments · 3 hours
Read Unit 1: Moments.. Define the concept of moments.. Compute first, second, third, and fourth moments for ungrouped and grouped data.. Apply Charlier's check and Sheppard's correction..
- Week 9Module 4: Moment, Skewness and Kurtosis
Unit 2: Skewness · 3 hours
Read Unit 2: Skewness.. Define skewness.. Identify features of skewness.. Describe, compute, and interpret measures of skewness..
- Week 10Module 4: Moment, Skewness and Kurtosis
Unit 3: Kurtosis · 3 hours
Read Unit 3: Kurtosis.. Define kurtosis.. State and explain types of kurtosis.. Identify measures of kurtosis and their interpretations..
- Week 11Module 5: Basic Statistical Measures of Estimates
Unit 1: Basic Concept in Probability · 3 hours
Read Unit 1: Basic Concept in Probability.. Distinguish between events, experiments, sample space, and probability.. Differentiate mutually exclusive, conditional, and independent events..
- Week 12Module 5: Basic Statistical Measures of Estimates
Unit 2: Use of Diagram in Probability · 3 hours
Read Unit 2: Use of Diagram in Probability.. Solve probability problems using tree diagrams.. Solve probability problems using Venn diagrams..
Unit 3: Experimental Probability Rules · 3 hours
Read Unit 3: Experimental Probability Rules.. State and apply basic probability rules.. Demonstrate the practicability of probability rules..
- Week 13Module 5: Basic Statistical Measures of Estimates
Unit 4: Experimental Probability · 3 hours
Read Unit 4: Experimental Probability.. Compute probability values with different forms of selection.. Use tabular data to solve probability problems..
Unit 5: Random Variable and Mathematics of Expectation · 3 hours
Read Unit 5: Random Variable and Mathematics of Expectation.. Define and explain the concept of random variables.. Describe random variable distribution with respect to probabilities, mean, variance, and standard deviation.. Carry out mathematical problems involving expectation..
Preparing for the exam
- Review all definitions and formulas from Units 1-5 in Modules 3 and 5.
- Practice calculating measures of central tendency and dispersion for both grouped and ungrouped data.
- Focus on understanding the differences between various sampling techniques.
- Create diagrams illustrating probability concepts like mutually exclusive and independent events.
- Work through all examples in the study units and attempt similar problems from textbooks.
- Pay special attention to the application of Bayes' theorem and index number calculations.
- Review all Tutor-Marked Assignments (TMAs) and address any areas of weakness identified by your tutor.
Questions students ask about this course
What is ECO154 about?
This course introduces students to quantitative methods in economics. It covers basic statistical definitions, data collection, measures of central tendency and dispersion. Students will learn to compute and interpret moments, skewness, kurtosis, and probability distributions. The course also explores index numbers and their applications in economic analysis, business, and finance. Emphasis is placed on applying statistical techniques to solve real-world economic problems.
How many units does ECO154 have?
ECO154, Introduction To Quantitative Methods Ii, has 20 units across 5 modules, over 260 pages of course material. You can read it one unit at a time.
How many credit units is ECO154?
ECO154 carries 2 credit units, at 100 level in Social Sciences.
Is ECO154 hard?
ECO154 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 ECO154 take to study?
About 156 hours of study, spread across its 20 units.
How is ECO154 assessed?
ECO154 is assessed by Assignments, Tutor Marked Assignments and Final Examination.
What can I do with ECO154?
Data Analyst, Market Researcher, Economist, Financial Analyst and Business Analyst.