Stat Labs: Mathematical Statistics Through Applications by Deborah Nolan, Terry P. Speed

By Deborah Nolan, Terry P. Speed

Integrating the speculation and perform of data via a chain of case reviews, every one lab introduces an issue, presents a few medical heritage, indicates investigations for the knowledge, and gives a precis of the idea utilized in each one case. aimed toward upper-division scholars.

Show description

Read or Download Stat Labs: Mathematical Statistics Through Applications (Springer Texts in Statistics) PDF

Similar probability & statistics books

Time Series Analysis and Forecasting by Example (Wiley Series in Probability and Statistics)

An intuition-based method lets you grasp time sequence research comfortably Time sequence research and Forecasting by way of instance offers the elemental suggestions in time sequence research utilizing a number of examples. by means of introducing worthy thought via examples that show off the mentioned themes, the authors effectively support readers increase an intuitive knowing of doubtless advanced time sequence versions and their implications.

Understanding Biplots

Biplots are a graphical approach for concurrently showing different types of details; more often than not, the variables and pattern devices defined through a multivariate info matrix or the goods labelling the rows and columns of a two-way desk. This e-book goals to popularize what's now obvious to be an invaluable and trustworthy process for the visualization of multidimensional facts linked to, for instance, crucial part research, canonical variate research, multidimensional scaling, multiplicative interplay and diverse forms of correspondence research.

Adaptive Markov Control Processes (Applied Mathematical Sciences)

This e-book is worried with a category of discrete-time stochastic regulate tactics often called managed Markov methods (CMP's), often referred to as Markov selection strategies or Markov dynamic courses. beginning within the mid-1950swith Richard Bellman, many contributions to CMP's were made, and purposes to engineering, records and operations study, between different components, have additionally been constructed.

Extremes in Random Fields: A Theory and Its Applications

Offers an invaluable new approach for examining the extreme-value behaviour of random fields glossy technology mostly contains the research of more and more advanced information. the intense values that emerge within the statistical research of complicated information are usually of specific curiosity. This ebook specializes in the analytical approximations of the statistical importance of maximum values.

Extra resources for Stat Labs: Mathematical Statistics Through Applications (Springer Texts in Statistics)

Example text

How might the fact that there was an exam in the week prior to the survey affect your previous estimates and this comparison? • Consider making an interval estimate for the average amount of time spent playing video games in the week prior to the survey. Keep in mind the overall shape of the sample distribution. A simulation study may help determine the appropriateness of an interval estimate. • Next consider the “attitude” questions. In general, do you think the students enjoy playing video games?

34 2. Who Plays Video Games? • Population parameter is a summary of the characteristic for all units in the population, such as the average value of the characteristic. The population parameter of interest to us here is the average amount of time students in the class spent playing video games in the week prior to the survey. In parallel, for the sample, we have the following: • Sample units are those members of the population selected for the sample. • Sample size, usually denoted by n, is the number of units chosen for the sample.

3. For a simple random sample of size 5 from a population of 100 subjects, let I (1), I (2), . . , I (5) be the indices of the first, second, third, fourth, and fifth subjects sampled. Compute the following and show your work. a. b. c. d. e. f. g. P(I (1) 100), . . , P(I (5) 100). P (the 100th subject is in the sample). E[I (1)]. P(I (1) 100 and I (2) 2). P(I (1) 10, I (2) 20, I (3) 30, I (4) 40, and I (5) 50). P(the 10th, 20th, 30th, 40th, and 50th subjects are in the sample). P(the 10th and 20th subjects are in the sample).

Download PDF sample

Rated 4.19 of 5 – based on 39 votes