## Introduction to probability and statistics: principles and applications for engineering and the computing sciencesThis well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis. |

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### Contents

Introduction to Probability and Counting | 1 |

Some Probability Laws | 25 |

Discrete Distributions | 45 |

Copyright | |

21 other sections not shown

### Other editions - View all

Introduction to Probability and Statistics: Principles and Applications for ... J. Susan Milton,Jesse C Arnold No preview available - 2002 |

Introduction to Probability and Statistics: Principles and Applications for ... Janet Susan Milton,Jesse C. Arnold No preview available - 2002 |

### Common terms and phrases

05 level ANOVA approximate Assume assumption block chi-squared chi-squared distribution conducted confidence interval continuous random variable control chart cumulative distribution cumulative distribution function data are obtained data set defined Definition degrees of freedom denote the number difference discrete discrete random variable distributed with mean effect equation error Example Explain factor factorial experiment Find a 95 Find the probability function independent interaction joint density Let X denote matrix median method method of moments normally distributed Note null hypothesis number of defective observed value outliers parameters point estimate Poisson predict predictor variables problem procedure proportion random sample randomly selected regression line reject H0 residual sample mean Section shown in Fig simple linear regression standard deviation standard normal stem-and-leaf diagram sum of squares temperature test H0 test statistic Theorem tion treatment combinations Type I error unbiased estimator verify