Statistical Methods and Computer Applications by P.N. AroraSECTION A: BIO-STATISTICS 1 Significant Digits and Rounding of Numbers 2 Classification of Data 3 Diagrammatic Representation of Data 4 Measures of Standard Deviation 5 Sampling and Estimation 6 Probability Introducing Computer Systems and Bayes Theorem 7 Probability Distributions [BINOMIAL, POISSON AND NORMAL DISTRIBUTIONS) 8 Skewness and Kurtosis 9 Correlation Analysis 10 Regression Analysis 11 Tests of Significance - Parametric Tests 12 Chi-Square Test - A Non-Parametric Test 13 F-Distribution and ANOVA Table 14 Design of Experiments 15 Statistical Quality Control Charts SECTION B: COMPUTER APPLICATIONS 1 Introducing Computer Systems 2 Algorithms and Flow Charts 3 Introduction to C 4 Constants, Variables and Data Types 5 Operators and Expressions 6 Decision Control Structure 7 Loop Control Structure 8 Functions 9 Arrays 10 File Handling 11 Computer Applications in Pharmaceutical and Clinical Studies
Why you should love statistics - Alan Smith
Statistical methods and computer applications
Journal of the Italian Statistical Society. Editor: Tommaso Proietti. Journal no. This international journal fosters the development of statistical methodology and its applications in biological, demographic, economic, health, physical, social, and other scientific domains. In particular, the journal emphasizes investigations of methodological foundations and methods that have broad applications. SMA includes two sections. The first is devoted to statistical methodology, publishing original contributions in all fields of statistics.
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Marie, Ontario, Canada. Conventional statistical approaches rely heavily on the properties of the central limit theorem to bridge the gap between the characteristics of a sample and some theoretical sampling distribution. Problems associated with nonrandom sampling, unknown population distributions, heterogeneous variances, small sample sizes, and missing data jeopardize the assumptions of such approaches and cast skepticism on conclusions. Conventional nonparametric alternatives offer freedom from distribution assumptions, but design limitations and loss of power can be serious drawbacks. With the data-processing capacity of today's computers, a new dimension of distribution-free statistical methods has evolved that addresses many of the limitations of conventional parametric and nonparametric methods.
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