Probability & Statistics with R for Engineers and Scientists (Classic Version)

Michael G. Akritas has been teaching Statistics at Penn State University since 1985. He is the author of approximately 100 research publications dealing with a wide range of statistical topics. He has supervised 18 Ph.D. and 12 M.Sc. students and is currently supervising three Ph.D. students. He is co-Founder of the International Society for Nonparametric Statistics, former Director of the National Statistical Consulting Center for Astronomy and co-Editor of the Journal of Nonparametric Statistics. He held a three year affiliation with the National Technical University of Athens, and visiting appointments at MIT, Texas A&M University, University of Pennsylvania, University of Göttingen, University of Cyprus, Australian National University, and UNICAMP. He has been an elected Fellow of the Institute of Mathematical Statistics and of the American Statistical Association since 2001.

1. Basic Statistical Concepts

1.1 Why Statistics?

1.2 Populations and Samples

            1.2.1 Exercises 

1.3 Some Sampling Concepts

            1.3.1 Representative Samples 

            1.3.2 Simple Random Sampling, and Stratied Sampling 

            1.3.3 Sampling With and Without Replacement

            1.3.4 Non-representative Sampling

            1.3.5 Exercises 

1.4 Random Variables and Statistical Populations         

            1.4.1 Exercises 

1.5 Basic Graphics for Data Visualization 

            1.5.1 Histograms and Stem and Leaf Plots

            1.5.2 Scatterplots

            1.5.3 Pie Charts and Bar Graphs 

            1.5.4 Exercises 

1.6 Proportions, Averages and Variances

            1.6.1 Population Proportion and Sample Proportion       

            1.6.2 Population Average and Sample Average         

            1.6.3 Population Variance and Sample Variance         

            1.6.4 Exercises 

1.7 Medians, Percentiles and Box Plots

            1.7.1 Exercises 

1.8 Comparative Studies 

            1.8.1 Basic Concepts and Comparative Graphics        

            1.8.2 Lurking Variables and Simpson’s Paradox         

            1.8.3 Causation: Experiments and Observational Studies      

            1.8.4 Factorial Experiments: Main E