Course Catalogue

Module Code and Title:        BMS202 Statistics for Solving Business Problems

Programme:                          Bachelor of Commerce

Credit Value:                         12

Module Tutors:                     Hari Kumar Tiwari, Jigme Tashi, Tshering Pemo

Module Coordinator:            Jigme Tashi

General Objective: This module aims to develop students’ competence in applying statistical techniques in everyday live. Students will be able to collect information, summarize data and analyse quantitative information for decision making in business using statistical tools and techniques.

Learning Outcomes – On completion of the module, students should be able to:

  1. Identify the most appropriate statistical tools to use for each business situation or decision
  2. Explain the limitations of the statistical tools being used, and determine likely bias in the answers arising from misuse of the tools
  3. Prepare graphical representations from data tables to provide analysts a visual representation of a data set
  4. Use statistical functions in spreadsheets to compute the measures of central tendency and dispersion
  5. Use concepts of probability distributions such as binomial, Poisson and normal distributions through spreadsheet statistical functions for properly structuring the analysis to match the business decision to be made which involves uncertainty
  6. Select the correct hypothesis test to use for making the most likely correct decision
  7. Evaluate relationships between variables for making business decisions by using the concept of correlation and simple linear regression
  8. Choose suitable statistical techniques for describing and forecasting time series data.
  9. Apply the most widely-used statistical methods used in a variety of business situations, while recognizing their potential limitations

Teaching and Learning Approach:

Approach

Hours per week

Total credit hours

Lecture

2

30

Discussions, class exercises, case studies, class quizzes, presentations

1

15

Exercises and quizzes in computer lab

1

15

Tutorial & Independent study

4

60

Total

120


Assessment Approach:

A. Group Assignment: Portion of Final Marks-15%

Students in groups of 4 will display two sets of data to illustrate descriptive statistics involving data analysis and presentation from Units I and II (Visualizing & Presenting Data, Data Descriptors) and produce a group report of 1000 words illustrating their results. This will be followed by individual Q&A.

5%       accurate and clear graphic and tabular description of data (group mark)

5%       individual student contribution to the group work (process score)

5%       individual explanation of the meaning of the data as displayed (viva)

B. Project: Portion of Final Marks-10%

Students will work in groups of 4 to collect practical data from specific businesses and forecast results. The project covers unit VI (Time series Analysis), and presenting their findings in a 10-minute presentation.

2%       group work

5%       individual students’ contribution of collecting data & forecasting

3%       clarity of individual student’s part in the presentation

C. Written Class Quizzes: Portion of Final Marks-10%

Coverage: Two written quizzes of 5-10 questions (5% each, 30 min duration) to test students’ knowledge for unit II, III, and VI (Data descriptors, Probability, Time series data & analysis)

5%       accuracy of answers to factual questions

5%       solving practical problems accurately

D. Lab Quizzes: Portion of Final Marks-10%

Two computer lab quizzes (5% each, 45 min duration) of 10-15 problems which cover units IV and V (Probability distributions, and regression & correlation analysis)

E. Class participation: Portion of Final Marks-5%

Each student will contribute to class discussion by answering questions, stating their opinions and listening to others.

2%       coming to class prepared for discussion

2%       sharing their ideas clearly

1%       listening to the ideas of others

F. Midterm Examination: Portion of Final Marks-20%

Students will take a written exam of 2-hour duration covering topics up to the mid-point of the semester.

F. Semester-end Examination: Portion of Final Marks-35%

The module will have a semester-end exam for 2 hours covering the entire syllabus. The question will be divided into two parts – Part A (carrying 40% of the exam weightage) will be mostly of short answer including objective questions. Part-B (carrying almost 60% of the exam weightage) will be mostly of essay type or an extended response to the given question. This part of the question requires students to apply, analyse, and evaluate or construct knowledge and skills.

Areas of assignments

Quantity

Weighting

A.    Group assignment

1

15%

B.    Project

1

10%

C.   Class tests

2

10%

D.   Lab Quizzes

2

10%

E.    Midterm Examination

1

20%

Total Continuous Assessment (CA)

 

65%

F.    Semester-end Examination (SE) 

 

35%

TOTAL

 

100%


Pre-requisites:None

Subject Matter:

  1. Visualizing and Presenting Data Tables
    • Meaning of statistics
    • The different types of data variable
    • Creating a frequency distribution
    • Creating a table using Excel pivot table
    • Principles of table construction
    • Functions used in MS Excel: FREQUENCY, Histogram from Excel Toolbar add-in
  2. Graphical Representation of Data
    • Bar charts
    • Pie charts
    • Histogram
    • Histogram with unequal class intervals
    • Frequency polygon
    • Scatter and time series plots
    • Functions used in MS Excel: Charts
  1. Data Descriptors
    • Measures of Central tendency
      • Mean, median, and mode
      • Percentiles and quartiles
      • Averages from frequency distributions
      • Weighted Averages
      • Functions used in MS Excel: AVERAGE, MEDIAN, MODE, PERCENTILE, QUARTILE, SUM, SUMPRODUCT
    • Measures of dispersion
      • Range,
      • Inter-quartile range and semi- inter-quartile range (SIQR)
      • Standard deviation and Variance
      • Coefficient of variation
      • Measurement of skewness, kurtosis and moments.
      • Functions used in MS Excel: MAX, MIN, VAR, VARP, STDEV, SQRT, COUNT, SKEW, KURT, Box plots and Descriptive statistics from Excel toolbar add-in
  1. Probability
    • Basic ideas and terminologies in probability
      • Probability laws
      • Addition law
      • Multiplication law and conditional probability
      • Bayes’ theorem
      • Functions used in MS Excel: PROB, SUM
  1. Probability Distributions
    • Discrete probability distributions
      • Binomial probability distribution
      • Poisson probability distribution
      • Poisson approximation to the binomial distribution
      • Functions used in MS Excel: BINOMDIST, COMBIN, FACT, POISSON, and SUM
    • Continuous probability distribution
      • The normal distribution
      • The standard normal distribution
      • Checking for normality
      • Other continuous probability distribution
      • Functions used in MS Excel: NORMSDIST, NORMDIST, PRODUCT, SQRT, NORMINV, NORMSINV
  1. Linear Correlation and Regression Analysis
    • Linear correlation analysis
      • Scatter plots
      • Covariance
      • Pearson’s coefficient of correlation and
      • Testing the significance of linear correlation between two variables
      • Spearman’s rank correlation coefficient
      • Testing the significance of Spearman’s rank correlation coefficient, rs
      • Functions used in MS Excel: COVAR, PEARSON, CORREL, STDEV, COUNT, TINV, TDIST, SUM, INTERCEPT, SLOPE, TREND, Correlation from Excel toolbar add-in
    • Linear regression analysis
      • Construct scatter plot
      • Fit line to sample data
      • Test model reliability (Standard Error of Estimate and Coefficient of Determination)
      • Excel data analysis regression analysis
      • Functions used in MS Excel: INTERCEPT, SLOPE, STEYX, Regression from Excel toolbar add-in
  1. Time Series Data and Analysis
    • Introduction to time series data
      • Stationary and non-stationary time series
      • Seasonal time series
      • Univariate and multivariate methods
      • Scaling the time series
    • Index numbers
      • Simple indices
      • Aggregate indices
      • Deflating values
    • Trend extrapolation
      • A trend component
      • Fitting a trend to a time series
      • Types of trends
      • Trend parameters and calculations
    • Forecasting
      • Using a trend chart function to forecast time series
      • Moving averages
      • Exponential smoothing
    • Forecasting errors
      • Types of error measurement
      • Interpreting errors
      • Error inspection
      • Functions used in MS Excel: Charts, Add trend line, INTERCEPT, SLOPE, TREND, GROWTH, AVERAGE, SUM, COUNT, and Exponential Smoothing from Excel toolbar add-in

Reading Lists:

  1. Essential Readings
    • Aczel, A.D. (2012). Complete business statistics (8th ed.). Wohl Publishing.
    • Beri, G.C. (2009). Business statistics (3rd ed.). McGraw Hill Education (India) Private Limited.
    • Davis, G., & Pecar, B. (2013). Business statistics using Excel (1st ed.). Oxford University Press.
    • Levin, R.I, & Rubin, D.S. (2008). Statistics for management (7th ed.). Dorling Kindersley Pvt Ltd.
  2. Additional Readings
    • Thukral, J.K. (2011). Business statistics (3rd ed.). Taxmann publication.
    • Sharma, J.K. (2014). Business statistics (4th ed.). Vikas Publishing House Pvt.
    • Gupta, S.P. & Gupta, M.P. (2014). Business statistics (17th ed.). Sultan Chand and Sons.
    • Levine, D.M., Szabat, K.A., & Stephan, D.F. (2013). Statistics for managers using Microsoft Excel, (7th ed.). India: Phi Learning Private Ltd.

Date: July, 2017
Royal Thimphu College