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- 1. Chapter 1 Basic Concepts of Scale of Measurement, Central Tendencies and Dispersion Prof. Suresh suresh.suralkar@gmail.com Phone: 40434399, 25783850, 9969982986
- 2. Chapter 1.1 Basic Concepts of Scale of Measurement 2 / 18
- 3. 3 / 18 Evaluation Pattern • Class Participation & Attendance : 10% • Group Case Study Presentation & Report : 15% • Tests (written) : 15% • Solve & Submit one Q. paper of any prev. year : 10% • Final Exam : 50%
- 4. 4 / 18 Course Content - Syllabus Contents 1. Basic Concepts of scale of measurement, Central tendencies and dispersion 2. Probability and Probability Distributions 3. Sampling and sampling distributions 4. Estimation 5. Testing of Hypotheses 6. Chi-square 7. Analysis of Variance (One Way Anova) 8. Bivariate Analysis 9. Time series analysis 10. Decision Trees 11. Linear Programming, Transportation and Assignment Problems Cases Exercises using SPSS / Excel
- 5. 5 / 18 Quantitative Methods • Quantitative Methods • Other names to this subject: • Statistical Methods for Management • Statistics for Managers • Business Statistics • Statistics is a very ancient science, advanced to modern level
- 6. 6 / 18 Quantitative Methods • Definition: Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation and presentation of data. • Data Types: Quantitative & Qualitative
- 7. Population vs. Sample a b c d ef gh i jk l m n o p q rs t u v w x y z Population Sample b c g i n o r u y Measures used to describe a population are called parameters Measures computed from sample data are called statistics 7 / 18
- 8. Key Definitions • A population (universe) is the collection of all members of a group • A sample is a portion of the population selected for analysis • A parameter is a numerical measure that describes a characteristic of a population • A statistic is a numerical measure that describes a characteristic of a sample 8 / 18
- 9. Two Branches of Statistics • Descriptive statistics – Collecting, summarizing, and presenting data • Inferential statistics – Drawing conclusions about a population based only on sample data 9 / 18
- 10. Descriptive Statistics • Collect data – e.g., Survey • Present data – e.g., Tables and graphs • Characterize data – e.g., Sample mean = iX n 10 / 18
- 11. Inferential Statistics Drawing conclusions about a population based on sample results. • Estimation – e.g., Estimate the population mean weight using the sample mean weight • Hypothesis testing – e.g., Test the claim that the population mean weight is 120 pounds 11 / 18
- 12. Collecting Data Secondary Sources Data Compilation Observation Experimentation Print or Electronic Survey Primary Sources Data Collection 12 / 18
- 13. Types of Data Data Categorical Numerical Discrete Continuous Examples: Marital Status Political Party Eye Color (Defined categories) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics) 13 / 18
- 14. 14 / 18 Scales of Measurement • Scales of Measurement are of four types Nominal scale Ordinal scale Interval scale Ratio scale • Weakest to strongest scale
- 15. 15 / 18 Nominal Scale In Nominal Scale, numbers are used simply as labels for groups, classes or categories e.g. Blue, Green and Red numbered as 1, 2 and 3 resp. Male, Female Nominal stands for name of category Nominal scale is used for Qualitative Data
- 16. 16 / 18 Ordinal Scale In Ordinal Scale, data elements are ordered according to their relative size or quality e.g. Four products ranked as 1, 2, 3 and 4: worst to best
- 17. 17 / 18 Interval Scale In the interval scale, the value of zero is assigned arbitrarily and therefore we can not take ratio of two measurements. But we can take ratio of intervals e.g. 8am and 4am. We can not take ratio of two. But we can take ratio of duration: 8am – 4am and 8am – 6am as 4/2. Similarly Temp 20° C, 30° C and 50° C
- 18. 18 / 18 Ratio Scale Used when the measurements are in Ratio Scale e.g. 1, 2, 3, 4, 5… Rs. 100 is twice of Rs. 50 Rs. 0 is absence of any money Measurement of duration (but not time of day)

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