## A First Course in Probability Models and Statistical InferenceThis textbook provides an introductory course in probability and statistical inference. Its emphasis in the probability portion of the text is on developing a clear and concrete understanding of probability distributions as models for real-world situations. This understanding of probability distributions is then used to develop the basic principles of statistical inference and to apply these ideas in a wide variety of applications. A particular feature of the book is the author's use of exercises to develop the reader's understanding of important concepts. Each exercise comes with two levels of solutions: the first level consists of hints, clarifications, and references to relevant discussions in the text; while the second level provides detailed and complete solutions. The author presupposes no previous knowledge on the half of the reader and carefully discusses each of the main concepts from probability and statistics as they are introduced. As a result, this book makes an excellent introduction to this central component of any curriculum which includes quantitative methods. |

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### Contents

Introduction to Probability Models of the Real World | 3 |

3 | 33 |

Chapter 2 Understanding Observed Data | 38 |

Copyright | |

8 other sections not shown

### Other editions - View all

A First Course in Probability Models and Statistical Inference James H.C. Creighton Limited preview - 2012 |

A First Course in Probability Models and Statistical Inference James H.C. Creighton No preview available - 2012 |

### Common terms and phrases

answer appropriate approximation assume assumption average binomial calculate called cards chance Chapter characteristic coin complete compute conclusion confidence interval course determined endpoints equally error estimate exactly example expected experiment Explain fact fair five formula give given Hand heads hypothesis independent interest it's least less linear look mean measure normally distributed Note observed obtain outcomes p-value parameter percent persons picture population possible possible values prediction probability probability distribution problem proportion question random experiment random variable real-world reasonable reject replacement result roll rule sample mean scores Show significance simple random sample situation specific squared standard deviation standard error statistical Suppose sure theoretical there's toss true unknown variance weight What's zero