Far-western University Faculty of Management
Course Title: Business Statistics and Mathematics
Full Marks: 100
Course No.: STAT 1001
Pass Marks: 45
Nature of the Course: Theory/Practical
Time per Period: 1 hour
Year: First
Total Periods: 120
Level: BBS 1. Course Description
This course deals with the application of statistical tools in business decision making and covers data collection, tabulation and presentation, techniques of summarizing and describing numerical data, basic concepts of probability, correlation & regression, time series & forecasting. Moreover, it provides knowledge of system of linear equations, matrices, determinant and their applications in the business and economics. 2. Course Objectives
The general objectives of the course are as follows:
To familiarize the students with descriptive statistical tools. To familiarize the students with the concept of probability, establishment of relationship between variables and forecasting.
To provide students with a sound understanding und erstanding of matrix algebra for business decisions.
To provide the basic knowledge of differentiation and integration.
To provide the sound knowledge of excel program to handle various problems of statistics
3. Specific Objectives and Contents Unit objectives
Describe the applications of statistics Unit 1: Introduction in business 1.1 An Overview of Statistics Explain the various terminologies of 1.2 Statistics for Managers statistics 1.3 Basic Vocabulary of Statistics Distinguish between quantitative and 1.4 Types of Variables categorical variables 1.5 Scales of Measurement Explain with measurement scales 1.6 Microsoft Excel Worksheets Demonstrate with excel and excel graphical and statistical functions
After completing this unit, students will be able to:
Contents
Explain the difference between primary and secondary data
(5 hrs.)
Unit 2: Data Collection, Presenting Data in Tables and Charts (14 hrs.)
2.1 Tables and Charts for Categorical Data 2.1.1
The Summary Table
2.7
Develop tables categorical data
and
charts
for
2.1.2 2.1.3
The Bar Chart The Pie Chart
Develop tables numerical data
and
charts
for
2.1.4
The Pareto Diagram
2.2 Organizing Numerical Data
Construct a frequency distribution and a histogram Construct relative and frequency distributions
cumulative
Construct a stem-and-leaf display represent data
to
Visually represent data by using graphs and charts Construct a scatter diagram
2.2.1
The Ordered Array
2.2.2
The Stem-and-Leaf Display
2.3 Tables and Charts for Numerical Data 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5
The Frequency Distribution The Relative Frequency Distribution and the Percentage Distribution The Cumulative Distribution The Histogram The Polygon
2.3.6
The Cumulative Frequency Curve (Ogive)
2.4 Cross Tabulations 2.4.1
The Contingency Table
2.4.2
The Side-by-Side Bar Chart
2.5 Scatter Plots and Time-Series Plots 2.5.1
The Scatter Plot
2.5.2
The Time-Series Plot
2.6 Microsoft Excel Graphs After completing this unit, students will be able to:
Describe the applications and uses of central tendency, variation, and shape in numerical data Identify descriptive summary measures for a population Construct and interpret a box-andwhisker plot and shape of the distribution
Unit 3: Numerical Descriptive Measures (17 hrs.)
3.1 Measures of Central Tendency 3.1.1 The Mean 3.1.2 The Median 3.1.3 The Mode 3.1.4 Partition values( Quartiles, Deciles and Percentiles) 3.1.5 The Geometric Mean 3.2 Variation and Shape (Measurement of Dispersion and Shape) 3.2.1 The Range 3.2.2 The Inter quartile Range 3.2.3 The Variance and the Standard Deviation 3.2.4 The Coefficient of Variation 3.2.5 Shape 3.2.6 Visual Explorations: Exploring Descriptive Statistics 3.2.7 Microsoft Excel Descriptive Statistics Results 3.3 Numerical Descriptive Measures for a Population 3.3.1 The Population Mean 3.3.2 The Population Variance and Standard Deviation
3
3.4 Exploratory Data Analysis 3.4.1 The Five-Number Summary 3.4.2 The Box-and-Whisker Plot After completing this unit, students will be able to:
Unit 4: Basic Probability
(12 hrs.)
4.1 Sets & Set Operations
Understand basic probability concepts
4.2 Introduction to Permutation & Combination
Understand conditional probability
4.3 Basic Probability Concepts
use Bayes’ probabilities
theorem
to
revise
Determine the number of combinations and the number of possible permutations of n objects r at a time. Use concept of probability in business decision environment
4.3.1 Events and Sample Spaces 4.3.2 4.3.3 4.3.4
Contingency Tables Simple (Marginal) Probability Joint Probability
4.4 Conditional Probability 4.4.1 Computing Conditional Probabilities 4.4.2 Decision Trees 4.4.3 Statistical Independence 4.4.4 Multiplication Rules 4.4.5 Marginal Probability Using the General Multiplication Rule 4.5 Bayes’ Theorem
After completing this unit, students will be able to:
Identify independent and dependent variables
5.1 Simple correlation coefficient Method measurement of simple correlation coefficient
Understand types of relationships
5.2 Scatter Diagram method and Karl Parsons coefficient of correlation method
Use regression analysis to predict the value of a dependent variable based on an independent variable The meaning of the coefficients b0 and b1
regression
5.3.1 Using Least-Squares Method 5.3.2 Using of Excel 5.4 Coefficient of Determination
Use excel to identify correlation coefficient and regression equation
5.5 Correlation and Causation
Describe the trend, cyclical, seasonal, and irregular components of the time series model Fit a linear or a polynomial trend equation to a time series Smooth a time series with the centered moving average and exponential smoothing techniques
of
5.3 Determining the Simple Linear Regression Equation
Compute correlation coefficient
After completing this unit, students will be able to:
5.1
Unit 5: Correlation and Linear Regression (11 hrs.)
5.6 Determining the Multiple Linear Regression Equation using Excel Unit 6: Models for Time Series and Forecasting (8 hrs.)
6.1 Time Series 6.1.1 Components of a Time Series 6.1.2 Fitting a Linear Trend Equation 6.1.3 Fitting a Quadratic Trend Equation 6.2 Smoothing Techniques 6.2.1 The Moving Average 6.2.2 Exponential Smoothing
6.1
Determine seasonal indexes and use them to compensate for the seasonal effects in a time series Use the trend extrapolation and the exponential smoothing forecast methods to estimate a future value Use the mean absolute deviation ( MAD) and mean squared error ( MSE ) criteria to compare how well fitted equations or curves fit a time series
After completing this unit, students will be able to:
Use index numbers to compare business or economic measurements from one period to the next
6.3 Seasonal Indexes 6.3.1 The Ratio to Moving Average Method 6.3.2 Deseasonalizing the Time Series 6.4 Forecasting 6.4.1 Forecasts Using the Trend Equation 6.4.2 Forecasting with Exponential Smoothing 6.4.3 Seasonal Indexes in Forecasting 6.5 Evaluating the forecast accuracy: MAD and MSE
Unit 7: Index Numbers
(8 hrs)
7.1 Types of index number 7.2 Notation and terminology 7.3 Method of constructing index number 7.4 Un-weighted method a. Simple average of price relative b. Simple aggregative method 7.5 Weighted Method a. Laspeyre's Index number b. Paasches's Index number c. Fisher’s index number 7.6 Cost of living index 7.6.1 Method of constructing cost of living index numbers: a. Aggregative expenditure method b. Family Budget method
After completing this unit, students will be able to:
Identify independent and dependent variables Understand types of relationships Use regression analysis to predict the value of a dependent variable based on an independent variable The meaning of the coefficients b0 and b1
regression
Compute correlation coefficient
Unit 8: Linear Programming
(10 hrs.)
8.1 Introduction 8.2 system of linear inequalities 8.3 Construction of LP models 8.4 Graphical LP Solution 8.5 Solution using LINDO program 8.6 Special cases 8.5.1
Unbounded Solution
8.5.2
Infeasible Solution
8.5.3
Alternative optima
Use excel to identify correlation coefficient and regression equation
After completing this unit, students will be able to:
Unit 9: Systems of Linear Equations and Matrices (13 hrs.)
9.8
Understand the concept of matrix
9.1 Basic Matrix Operations, Matrix Products
Apply matrix operations in business problem solving
9.2 Systems of Two Linear Equations in Two Variables
Represent system of linear equations in matrix form
9.3 Larger Systems of Linear Equations
Compute the value of determinant
9.5 Determinant
Identify inverse of a matrix
9.6 Matrix Inverses
Solve Systems of Linear Equations by using Crammer’s rule
9.7 Applications of Matrices in Business and Economics
After completing this unit, students will be able to:
9.4 Applications of Systems of Linear Equations
Unit 10: Differentiation and Integration
(22 hrs.)
10.1 Functions 10.1.1 Introduction
Gain familiarity with various functions
10.1.2 Polynomial Functions (Linear and Quadratic Functions)
Understand the meaning of derivative
10.1.3 Exponential and Logarithmic Functions 10.2 Limit and continuity
Identify derivative of various functions
10.3 Derivatives 10.3.1 Introduction
Understand the concept of integration
10.3.2 Rules of Differentiation 10.3.3 Derivative as rate of change
Use different rules and techniques of integration
10.3.4 Higher order derivative 10.4Application of derivative on business 10.5 Integration 10.5.1 Introduction 10.5.2 Techniques of integration 10.5.3 Definite Integral 10.6 Application of Integration
Text Book
Levine, D. M., Stephan, D. F., Krehbiel, T. C. & Berenson, M. L. (2008). STATISTICS FOR MANAGERS USING Microsoft Excel. United States of America: Prentice-Hall Reference Books Weiers, R. M. (2008). Introduction to Business Statistics. United States of America: Thomson South-Western Larson, R. & Farber, B. (2012). ELEMENTARY STATISTICS picturing the world . United States of America: Prentice-Hall Levin, R. I., Rubin, D. S., Stinson. J.P. & Gardner, E. S. (Jr): Quantitative Approaches to Management, McGraw-Hill.
