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This is the complete lecture note for ECO 206 STATISTICS OF ECONOMICS. You can read it online below, open it in the full viewer, or download the PDF for offline study. It pairs with the course's video lectures, so you can read and watch together.
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Course code: ECO 206 Course title: Statistics for Economist Course credit load: 2 Units Series 1: Foundations of Descriptive Statistics Series 2: Probability and Probability Distributions Series 3: Sampling and Estimation Techniques Series 4: Hypothesis Testing and Statistical Inference Series 5: Advanced Statistical Methods and Applications Series outline • Part 1: Frequency Distribution and Data Presentation • Sub-Part 1.1: Types of Data and Classification • Sub-Part 1.2: Construction of Frequency Tables • Sub-Part 1.3: Graphical Representation of Data • Part 2: Measures of Central Tendency • Sub-Part 2.1: Mean • Sub-Part 2.2: Median • Sub-Part 2.3: Mode • Part 3: Measures of Dispersion • Sub-Part 3.1: Range and Quartile Deviation • Sub-Part 3.2: Variance and Standard Deviation • Sub-Part 3.3: Coefficient of Variation • Part 4: Shapes of Frequency Distribution • Sub-Part 4.1: Skewness • Sub-Part 4.2: Kurtosis Introduction Statistics is often described as the language of data, and in economics it plays a vital role in helping us make sense of the numbers that surround business, management, and socio-political decisions. Imagine a government trying to decide whether to increase spending on healthcare, or a company evaluating sales performance across regions. In both cases, raw figures alone are not enough. We need tools to organise, summarise, and interpret these numbers so that meaningful conclusions can be drawn. This Series introduces you to those foundational tools, known as descriptive statistics, which provide the first step in transforming data into useful knowledge. The aim of this Series is to equip you with the ability to organise and summarise data effectively, using measures and techniques that reveal patterns, central tendencies, variability, and
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS distributional shapes. By the end of this Series, you will be able to present data clearly and interpret its basic features with confidence. We shall commence this Series by examining how data can be classified and presented through frequency distributions and graphical methods. Next, we will explore measures of central tendency, focusing on the mean, median, and mode as ways of identifying the centre of a dataset. Following that, we will consider measures of dispersion, such as range, variance, and standard deviation, which highlight the spread of data around the centre. Finally, we will wrap up by studying the shapes of frequency distributions, including skewness and kurtosis, which provide deeper insights into the nature of data patterns. This sequential journey will give you a strong foundation for the more advanced statistical techniques that follow in later Series. Series Learning Outcomes Upon completing this Series, you should be able to: • SLO 1.1 define different types of data and demonstrate how to classify them for statistical analysis, • SLO 1.2 construct frequency tables and illustrate data using appropriate graphical methods, • SLO 1.3 explain and compute measures of central tendency including mean, median, and mode, • SLO 1.4 calculate measures of dispersion such as range, variance, and standard deviation, and evaluate their significance in describing data spread, • SLO 1.5 analyse the shapes of frequency distributions by identifying skewness and kurtosis and interpreting their implications for data behaviour. 1.1 Frequency Distribution and Data Presentation 1.1.1 Types of data and classification When working with statistics, the first step is to identify the type of data you are dealing with. Data refers to facts, figures, or information collected for analysis. Data can be broadly classified into qualitative data, which describes attributes or categories (such as gender or occupation), and quantitative data, which represents numerical values (such as income or age). Quantitative data can further be divided into discrete data, which takes whole number values, and continuous data, which can take any value within a range. Fill in the blank: Quantitative data that can take any value within a given interval is called __________.
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS [Diagram showing classification of data into qualitative and quantitative, with subdivisions. Caption: Figure 1.1 Classification of data 1.1.2 Construction of frequency tables A frequency distribution is a summary that shows how often each value or group of values occurs in a dataset. To construct a frequency table, data is organised into classes or intervals, and the number of observations falling into each class is recorded. This helps to simplify large datasets and makes patterns easier to identify. For example, if you collect data on the ages of 100 students, instead of listing each age, you can group them into intervals such as 10–14, 15–19, and so on, and record the frequency of students in each interval. Fill in the blank: A summary that shows how often each value occurs in a dataset is called a __________.
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS [Table 1.1 Example of a frequency distribution of student ages Short description: A table showing age intervals and corresponding frequencies. 1.1.3 Graphical representation of data Data can be presented visually using graphs and charts, which make interpretation easier. Common methods include: • Histogram: A bar chart showing the frequency of data within intervals. • Bar chart: A chart with rectangular bars representing categories and their frequencies. • Pie chart: A circular chart divided into slices to show proportions. • Line graph: A graph that uses points connected by lines to show trends over time. Each graphical method has its strengths. For example, histograms are useful for continuous data, while pie charts are effective for showing proportions. Fill in the blank: A circular chart divided into slices to show proportions is called a __________.
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS [Example of a histogram showing student age distribution. Caption: Figure 1.2 Histogram of student ages Pilot questions 1.1 1. Which type of data describes attributes such as gender or occupation? 2. What is the difference between discrete and continuous data? 3. What is the main purpose of a frequency table? 4. Which graphical method is most suitable for showing proportions? 5. In a histogram, what does the height of each bar represent? 1.2 Measures of Central Tendency 1.2.1 Mean The mean is the most commonly used measure of central tendency. It is calculated by summing all the values in a dataset and dividing by the number of observations. The mean provides a single value that represents the average of the dataset. However, it can be influenced by extreme values, known as outliers, which may distort the overall picture. Fill in the blank: The mean is obtained by dividing the __________ of all values by the number of observations.
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS [Diagram showing calculation of mean with a dataset of student scores. Caption: Figure 1.3 Calculation of mean 1.2.2 Median The median is the middle value of a dataset when the values are arranged in order. If the dataset has an odd number of observations, the median is the exact middle value. If it has an even number of observations, the median is the average of the two middle values. The median is particularly useful when dealing with skewed data, as it is less affected by outliers. Fill in the blank: When a dataset has an even number of observations, the median is the __________ of the two middle values.
LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS [Illustration showing how to find the median in both odd and even datasets. Caption: Figure 1.4 Determining the median 1.2.3 Mode The mode is the value that occurs most frequently in a dataset. A dataset may have one mode (unimodal), two modes (bimodal), or more than two modes (multimodal). The mode is especially useful for categorical data, where the mean and median may not be meaningful. For example, in a survey of favourite colours, the mode would be the colour chosen most often. Fill in the blank: A dataset with more than two modes is described as __________.
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