Research and Markets: A Probability and Statistics Companion - An accessible and engaging introduction to the study of probability and statistics.DUBLIN -- Research and Markets (http://www.researchandmarkets.com/research/ab82c8/a_probability_and) has announced the addition of John Wiley John Wiley may refer to:
An accessible and engaging introduction to the study of probability and statistics
Utilizing entertaining real-world examples, A Probability and Statistics Companion provides a unique, interesting, and accessible introduction to probability and statistics. This one-of-a-kind book delves into practical topics that are crucial in the analysis of sample surveys and experimentation.
This handy book contains introductory explanations of the major topics in probability and statistics, including hypothesis testing hypothesis testing
In statistics, a method for testing how accurately a mathematical model based on one set of data predicts the nature of other data sets generated by the same process. and regression, while also delving into more advanced topics such as the analysis of sample surveys, analysis of experimental data, and statistical process control. The book recognizes that there are many sampling techniques that can actually improve on simple random sampling, and in addition, an introduction to the design of experiments is provided to reflect recent advances in conducting scientific experiments. This blend of coverage results in the development of a deeper understanding and solid foundation for the study of probability and statistics.
Additional topical coverage includes:
* Probability and sample spaces
* Choosing the best candidate
* Acceptance sampling
* Conditional probability conditional probability
the probability that event A occurs, given that event B has occurred. Written P(AB).
* Random variables and discrete probability distributions In probability theory, a probability distribution is called discrete if it is characterized by a probability mass function. Thus, the distribution of a random variable X is discrete, and X is then called a discrete random variable, if
* Waiting time problems
* Continuous probability distributions
* Statistical inference Inferential statistics or statistical induction comprises the use of statistics to make inferences concerning some unknown aspect of a population. It is distinguished from descriptive statistics.
* Nonparametric methods
* Least squares and medians
* Recursions and probability
Each chapter contains exercises and explorations for readers who wish to conduct independent projects or investigations. The discussion of most methods is complemented with applications to engaging, real-world scenarios such as winning speeds at the Indianapolis 500 and predicting winners of the World Series. In addition, the book enhances the visual nature of the subject with numerous multidimensional graphical representations of the presented examples.
A Probability and Statistics Companion is an excellent book for introductory probability and statistics courses at the undergraduate level. It is also a valuable reference for professionals who use statistical concepts to make informed decisions in their day-to-day work.
Key Topics Covered:
1. Probability and Sample Spaces.
2. Permutations and Combinations permutations and combinations: see probability.
permutations and combinations
Number of ways a subset of objects can be selected from a given set of objects. In a permutation, order is important; in a combination, it is not. : Choosing the Best Candidate; Acceptance Sampling.
3. Conditional Probability.
4. Geometric Probability.
5. Random Variables and Discrete Probability Distributions--Uniform, Binomial binomial (bī'nō`mēəl), polynomial expression (see polynomial) containing two terms, for example, x+y. The binomial theorem, or binomial formula, gives the expansion of the nth power of a binomial (x+ , Hypergeometric, and Geometric Distributions.
6. Seven-Game Series in Sports.
7. Waiting Time Problems.
8. Continuous Probability Distributions: Sums, the Normal Distribution, and the Central Limit Theorem central limit theorem
In statistics, any of several fundamental theorems in probability. Originally known as the law of errors, in its classic form it states that the sum of a set of independent random variables will approach a normal distribution regardless of the ; Bivariate bi·var·i·ate
Mathematics Having two variables: bivariate binomial distribution.
Adj. 1. Random
9. Statistical Inference I.
10. Statistical Inference II: Continuous Probability Distributions II--Comparing Two Samples.
11. Statistical Process Control.
12. Nonparametric Methods.
13. Least Squares, Medians, and the Indy 500.
15. Design of Experiments.
16. Recursions and Probability.
17. Generating Functions and the Central Limit Theorem.
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