Course : Business Statistics

Course Code : BMKT 31223

Credit Value : 3

Status : Compulsory

Level : Level 3

Semester : Semester I

Overall Learning Outcomes : At the end of the course unit students should be able to;

  1. recommend appropriate statistical tools and techniques that can be used for decision making for a given business context
  2. propose univariate, bivariate and multivariate statistical tools appropriately to analyze data to assist decision making in a given business context
  3. calculate probabilities in assessing the uncertainty of a given context
  4. justify the selection of tools available for forecasting in business decision making
  5. build an awareness of the importance and availability of various statistical tools to assist decision making in organizations
  6. produce statistical inferences and estimations for a given business context
  7. calculate probabilities by identifying the relevant probability distribution for a given scenario
  8. select appropriate quantitative data analysis techniques in decision making
  9. internalize the practice of statistical decision making


Course Content         :

Topic No

Topic

Learning Outcomes

Teaching and Learning Method

Method of Assessment

1

Introduction to business statistics and data

At the end of the session students should be able to;

  1. describe the importance of statistics in business decision making
  2. compare different types of statistics and apply appropriate type of statistics for a given context
  3. justify the selection of a sample in statistical inquiry for a given context
  4. differentiate types of data and scales of data available in the business context
  5. compare different data collection methods
  6. internalize the habit of decision making using appropriate type of statistics


Lectures Group assignment

Group Assignment Report

Mid/End Semester Examination

2

Descriptive statistics to describe data

At the end of the session students should be able to;

  1. select appropriate tools and techniques of organizing data for a given context
  2. discuss the appropriateness of data presentation techniques for a given context
  3. calculate measures of central tendency for a collected set of data
  4. decide and calculate appropriate measures of dispersion for a given context
  5. compute measures of skewness and kurtosis for a given data set
  6. develop sound interpretation abilities of univariate statistics for a given marketing context


Lectures Group assignment

Group Assignment Report

Mid/End Semester Examination

3

Bivariate Analysis

At the end of the session students  should be able to;

  1. differentiate parametric and non-parametric tests available for decision making
  2. justify the selection of bivariate analysis method for any context  by checking assumptions
  3. develop a simple linear regression model for a given context
  4. determine regression estimators by using the least square method
  5. calculate and interpret error variance, standard errors of regression estimators
  6. calculate confidence intervals for regression parameters
  7. apply concepts of correlation for a given context
  8. assess the viability of the constructed model
  9. apply parametric and/or non-parametric bivariate analysis for identified two variables
  10. display sound interpretation ability of bivariate statistics for a given marketing context


Lectures Tutorials

Mid/End Semester Examination

4

Basic Probability Concepts in decision making

At the end of the session students should be able to;

  1. demonstrate an understanding of basic probability concepts (including: events, sample space, and probabilities)
  2. calculate probabilities using appropriate rules of probability
  3. calculate conditional probabilities for a given scenario
  4. differentiate independent and dependent events
  5. discuss the law of total probability
  6. calculate probabilities using Bayes’ theorem
  7. apply the concepts of probability for a given context


Lectures

Class room discussions Tutorials

Mid/End Semester Examination

5

Probability Distributions

At the end of the session students should be able to;

  1. differentiate discrete and continuous random variables
  2. calculate expected value, variance and standard deviation of discrete random variable
  3. review conditions for a binomial/ poison/ hypergeomatric random variables
  4. solve problems using binomial/ poison/ hypergeomatric distribution formulas
  5. solve problems using uniform or normal probability distribution formulas
  6. calculate probabilities by identifying the relevant probability distribution for a given scenario


Lectures Tutorials

Mid/End Semester Examination

6

Sampling Distributions

At the end of the session students should be able to;

  1. differentiate random and non-random samples
  2. determine the sampling distribution of a statistic and a proportion

Lectures Case study based class room discussions

Mid/End Semester Examination

7

Statistical Inferences and Estimations

At the end of the session students should be able to;

  1. assess sample statistics as estimators of population parameters
  2. calculate confidence intervals for population mean, population proportion for single sample and two samples
  3. determine the sample size for a given study
  4. develop an understanding of the concepts of hypothesis testing
  5. determine two types of errors in hypothesis testing
  6. recommend appropriate hypothesis testing methods for a given scenario
  7. explain assumptions of analysis of variance
  8. apply hypothesis testing in analysis of variance
  9. discuss the theory of ANOVA and computation of ANOVA


Lectures

Case study based class room discussions








Mid/End Semester Examination

8

Multivariate Analysis

At the end of the session students should be able to;

  1. assess the suitability of different multivariate statistics for different marketing related decisions
  2. review the assumptions pertaining to the recommended analysis techniques
  3. recommend suitable multivariate analysis techniques for a given marketing context
  4. build the k variable multiple regression model
  5. assess the viability of regression model using appropriate tools
  6. display sound interpretation ability of multivariate statistics for a given marketing context


Lectures Case study based class room discussions

Class room presentation

Mid/End Semester Examination

9

Forecasting, Time Series and Random Numbers

At the end of the session students should be able to;

  1. demonstrate an awareness of different types of forecasting
  2. evaluate a given data set using the trend analysis techniques
  3. review the seasonality and cyclical behaviour for a given set of data
  4. recommend smoothing techniques for a given set of data
  5. discuss index numbers


Lectures Case study based class room discussions

Class room presentation

Mid/End Semester Examination

Recommended Reading       :

  1. Levine, D.M, Krehbiel, T.C., and Berenson, M.L, (2012), “Business Statistics: A First Course”, 3rd edition, Pearson Education.
  2. Chandan, J.S, (2009), “Statistics for Business and Economics”, 2nd edition, Vikas Publishing House.
  3. Srivastava, S, Srivastava, S.C., and Pradhan, N.C., (2006), “Fundamentals of Statistics”, 1st edition, Anmol Publications.
  4. Levin, R.I. and Rubin, D.S., (2008),” Statistics for Managers”, 7th edition, , Prentice Hall.
  5. Gupta, S.C and Gupta, I., (2005), “Business Statistics”, 5th edition, Himalaya Publishing House.
 

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