Introductory Statistics
- 2nd Edition - March 18, 2005
- Author: Sheldon M. Ross
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 5 9 7 1 3 2 - 4
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 5 4 7 3 4 - 3
This text is written for the introductory non-calculus based statistics course offered in mathematics and/or statistics departments for undergraduate students of any major who take a semester course in basic Statistics or a year course in Probability and Statistics.
Introduction to Statistics
Describing Data Sets
Using Statistics to Summarize Data Sets
Probability
Discrete Random Variables
Normal Random Variables
Distributions of Sampling Statistics
Estimation
Testing Statistical Hypotheses
Hypothesis Tests Concerning Two Populations
Analysis of Variance
Linear Regression
Chi-Squared Goodness of Fit Tests
Nonparametric Hypotheses Tests
Quality Control
Describing Data Sets
Using Statistics to Summarize Data Sets
Probability
Discrete Random Variables
Normal Random Variables
Distributions of Sampling Statistics
Estimation
Testing Statistical Hypotheses
Hypothesis Tests Concerning Two Populations
Analysis of Variance
Linear Regression
Chi-Squared Goodness of Fit Tests
Nonparametric Hypotheses Tests
Quality Control
- Edition: 2
- Published: March 18, 2005
- Language: English
SR
Sheldon M. Ross
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
Affiliations and expertise
Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA