Module Code and Title:       STT101 Descriptive Statistics for Economics      

Programme:                          BA in Development Economics

Credit Value:                         12

Module Tutor:                       Sonal Mehta

General objective: The module introduces some basic concepts and terminology that are fundamental to descriptive statistical analysis and relevant to the study of economics. The main intent is to enable students to develop skills to apply statistical procedures and summarize the information. They will also know how to construct indices.

Learning outcomes – On completion of the module, students will be able to:

1. Identify the sources of biases associated with data collection and reasoning process.
2. Identify the variables 
3. Differentiate between among various data types.
4. Set up sample data for a simple statistical analysis using sampling strategy.
5. Construct frequency table from the raw data.
6. Use spreadsheets to create bar and pie charts.
7. Discuss the significance of common statistical measures.
8. Use spreadsheet to describe the data.
9. Construct histogram, ogives and box and whisker plots using spreadsheets.
10. Present and interpret statistics.
11. Construct and interpret price index.
12. Evaluate the strength and weakness of each price index.

Learning and Teaching Approach: 

Assessment Approach:

The assignment will assess the ability of students to individually plan to collect economics related data using appropriate sampling techniques. Students will write a report on the sampling strategy, its justification, and possible drawbacks. The assignment should have a maximum limit of 500-700 words. 

2    Identification of the variables, their types and subjects

3    Clearly explain the sampling plan 

3    Justification of sampling strategy 

2    Limitations

Two written tests will be conducted (10 marks each), one before midterm, covering Unit I, and one after midterm, covering Unit V. Each test will be of 45 minutes duration, and will contain 5 questions (3 on the conceptual understanding and 2 on problem solving).

The task is for individual student to clean and arrange the given raw data, and provide a descriptive and graphical analysis covering Unit III and IV. The tutor will provide raw data for the work. Students should use spreadsheets for this work. 

2    Data cleaning

3    Arrangement of raw data for analysis

6    Clearly communicate the analysis of results- descriptive statistics

4    Construction of appropriate graphs to represent the analysis 

Students will take a written exam of 1.5-hr duration covering topics up to the mid-point of the semester. The exam will comprise structured questions like MCQ, fill-in-the-blanks, matching, definition, as well as open-ended essay questions.

Students will take a written exam of 2.5-hr duration encompassing all the subject matter covered in the semester. This assessment is comprehensive and summative in nature, and will comprise structured questions like MCQ, fill-in-the-blanks, matching, definition, as well as open-ended essay questions.

Overview of assessment approaches and marks

Pre-requisites: None

Subject matter:

For units II to IV use spreadsheets to create outputs and emphasise on data interpretation.

1. Unit I: Introduction 
   1. Nature and use of statistics
   2. Statistical challenges: imperfect data, practical constraints, ethical standards
   3. Statistical pitfalls: biases, conclusions from small samples, making generalization about individuals from groups, poor survey methods, giving importance to rare observations and assuming a causal link
2. Unit II: Data Collection: 
   1. Definitions: subjects, variables, and data sets
   2. Types of data: qualitative and quantitative
   3. Level of measurement: nominal, ordinal, interval and ratio
   4. Time-series data and cross-sectional data 
   5. Sampling concept: 
      1. Definitions of primary data, secondary data, population, sample and sample frame 
      2. Determination of sample size 
      3. Probability sampling: simple random sample, systematic sampling, stratified sampling, cluster sampling
      4. Non probability sampling: convenience sampling and judgement sampling 
      5. Sampling and non-sampling error
3. Unit III: Nominal and Ordinal Level Data Analysis
   1. Descriptive statistics: frequency distribution tables, binary and Likert scale analysis
   2. Visual Displays: bar chart, pie chart
4. Unit IV: Scale Level Data Analysis
   1. Descriptive statistics:
      1.  Measures of central tendency: mean, median and mode
      2.  Variation: range, standard deviation both absolute and relative measures
      3.  Shape: skewness and kurtosis
   2. Positional averages: quartiles, deciles, quintiles and percentiles
   3. Visual Displays: histograms, frequency polygons, ogives, box and whisker plot
   4. Standardising data: Chebyshev theorem, Empirical rule, unusual observations and outliers
   5. Time series analysis: Trend analysis with line chart 
   6. Weighted mean, Arithmetic Mean and Geometric Mean and the relationship between them
5. Unit V: Index Number 
   1. Definition of Index number: price, quantity and value
   2. Uses of Index Numbers
   3. Problems in construction of index numbers 
   4. Construction of unweight index numbers: simple average of price relatives
   5. Construction of weighted index numbers: Laspeyres, Paasche and Fishers
   6. Cost of living index
   7. Strength and weakness of each index number
   8. Test of consistency: unit test, time reversal and factor reversal
   9. Indices: chain indices, deflating prices and incomes, base shifting, splicing

Reading List:

Essential Reading

Doane, D. & Seward, L. (2019). Applied Statistics in Business and Economics. McGraw-Hills/Irwin.

Newbold, P., Carlson, W. & Thorne, B. (2019). Statistics for Business and Economics. Pearson Education.

Spiegel M.L. & Stephens, L. J. (2007). Statistics, Schaum Outline Series. McGraw Hills. 

Thukral,J.K. (18 December, 2012). Business Statistics. Taxmann Publications Private Limited.

Additional Reading

Larsen, R.J. & Marx, M.L. (2011). An Introduction to Mathematical Statistics and its Applications. Prentice Hall.

Levin, R. R., Rubin, S. D., Siddiqui, H. M., & Rastogi, S. (2021). Statistics for Management. Pearson.

Date: June, 2022