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Contents
Front Matter
Introduction
1.
Chapter 1
2.
Introduction to Quantitative Techniques
3.
Introduction, Significance, Scope and Limitations of Statistics
4.
Data classification and Tabulation
5.
Data Presentation: Graphs and Diagrams
6.
Measures of Central Tendency: Mathematical Averages (AM, GM, HM).
7.
Measures of Central Tendency: Averages of Positions (Median, Mode, Quartile, Deciles, Percentile)
8.
Measures of Dispersion: Mean Absolute Deviation, Standard Deviation, Variance, Coefficient of Variation
9.
Measures of Dispersion: Skewness and Kurtosis
10.
Probability Theory: Concept And Enumeration
11.
Conditional Probabilitym Bayes, Theorem
12.
Discrete Probability Distributions: Random variables, Expected Value and Variance:
13.
Discrete Probability Distribution : Binomial Distribution
14.
Discrete Probability Distribution : Poisson Distribution
15.
Continuous distribution – Normal distribution : Normal curve
16.
Continuous distribution – Normal distribution : Standard Normal probability curve.
17.
Sampling and Sampling Distributions: Random Sampling, Non Random Sampling
18.
Sampling and Sampling Distributions: Sampling Distribution of X bar
19.
Sampling and Sampling Distributions: Determining sample size
20.
Estimation: Point Estimation, Interval Estimation, Population mean-(known or unknown)
21.
Hypothesis Testing: Developing null and alternative hypotheses
22.
Hypothesis Testing: Type I and Type II errors, One Tailed and Two Tailed Tests
23.
Hypothesis Testing and Decision Making
24.
Statistical Inference with two populations: Hypothesis Techniques-two sample tests: σ1 and σ2 (known and un-known)
25.
Test of Goodness of Fit and Independence: Chi-Square-test-as a test of independence
26.
Test of Goodness of Fit and Independence
27.
Analysis of Variance and Experimental Design: testing for equality of k population means
28.
Analysis of Variance and Experimental Design: One-Way ANOVA
29.
Analysis of Variance and Experimental Design: Two-Way ANOVA
30.
Analysis of variance and Experimental Design: An Introduction to Experimental, Randomized and Block Design
31.
Analysis of Variance and Experimental Design: Conclusion
32.
Linear Regression: Simple Linear Regression Model with Least Square Method
33.
Correlation: Karl Pearson’s Coefficient of Correlation, Spearman Rank Correlation ar
34.
Correlation: Coefficient of Determination: Testing for Significance
35.
Multiple Regression Model
Back Matter
Appendix
Quntitative Techniques for Management Decisions
4
Data classification and Tabulation