Detailed Study Notes on Quantitative Methods and Statistics
Introduction to Quantitative Methods DPM 101 Lecture 1
Presenter: Mary Ann De-Graft (Mrs.)
Topics Covered:
Statistics
Collecting
Organising
Presenting
Analyzing
Interpreting
Probability
Introduction to Statistics
History of statistics
Definition of Statistics:
Singular Definition: Statistics is the science of collecting, organizing, presenting, analyzing data, and interpreting results for effective decision making.
Plural Definition: Statistics refers to data collected on characteristics of items.
Statistic: A value obtained from a sample.
Statistics: Values or estimates derived from statistical analysis.
Branches of Statistics:
Descriptive Statistics: Deals with describing essential characteristics of data without drawing conclusions.
Inferential Statistics: Involves making generalizations about a population based on a sample by drawing conclusions.
Importance of Statistics:
General Importance:
Effective decision making
Planning and development
Formulation and implementation of policies
Judicious allocation of resources
Forecasting and predictions
Specific Importance in Business:
Relief and assignment on how statistics are important in Procurement, Marketing, Accounting, Banking.
Limitations of Statistics:
Statistics does not deal with individual numbers.
Statistical conclusions are not universally true.
Cannot deal with qualitative characteristics.
Requires a high degree of skill and understanding for proper interpretation.
Collection of Data
Types of Data:
Sources of data
Survey methods
Sampling methods
Methods of data collection
Presentation of Data
Organization of data:
Editing
Coding
Classification
Tabulation of data:
Use of tables; frequency tables
Diagrams:
Charts
Histogram
Frequency polygon
Summary Measures (Descriptive Statistics)
Measures of Central Tendency:
Mean
Median
Mode
Measures of Dispersion:
Standard Deviation
Analysis Through Inference
Correlation Analysis:
Scatter plots
Types of correlation
Interpretation
Correlation coefficients
Probability Theory
Introduction to Probability:
Definition of probability:
A quantitative measure of the likelihood of an event occurring.
Importance of Probability:
Fundamental for statistics, helps in making inferences.
Range of Probability:
Between 0 (impossible event) and 1 (certain event).
Interpretation of Probability:
Understanding and interpreting probability is critical for statistical analysis.
Ways of Finding Probability (Single Events)
Classical Approach
Frequency Approach
Subjective Approach
Rules of Probability (Compound Events)
Addition Rule:
For mutually exclusive events: P(A or B) = P(A) + P(B)
For non-mutually exclusive events: P(A or B) = P(A) + P(B) - P(A and B)
Multiplicative Rule:
For independent events: P(A and B) = P(A) * P(B)
For conditional events: P(A | B) = P(A and B) / P(B)
Probability Distributions
Types of Probability Distributions:
Binomial Distribution
Poisson Distribution
Normal Distribution
Characteristics:
Mean and standard deviation of these distributions.
References
Linde, W. (2024). Probability Theory: A First Course in Probability Theory and Statistics. Walter de Gruyter GmbH & Co KG.
Mavrakakis, M. C., & Penzer, J. (2021). Probability and Statistical Inference: From Basic Principles to Advanced Models. Chapman and Hall/CRC.
Sheldon M. Ross. (2020). Introduction to Probability and Statistics for Engineers and Scientists, 6th Edition. Academic Press.
Giri, N. C. (2019). Introduction to Probability and Statistics. 2nd Edition. CRC Press.