2nd ed. 2012, XII, 845p. 237 illus.. With online files/update.
Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
An extensive range of applications that will appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and business, economics, and quantitative social science students
Nearly 1,500 exercises to help students master the material and better understand sophisticated concepts and arguments
An emphasis on the importance of statistical software, including output from the statistical software packages Minitab, R, and SAS.
Many mathematical statistics texts are heavily oriented toward a rigorous mathematical development of probability and statistics, without much attention paid to how statistics is actually used.. In contrast, Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data.
The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The next six chapters develop the probability material that bridges the gap between descriptive and inferential statistics. Point estimation, inferences based on statistical intervals, and hypothesis testing are then introduced in the next three chapters. The remainder of the book explores the use of this methodology in a variety of more complex settings.
This edition includes a plethora of new exercises, a number of which are similar to what would be encountered on the actuarial exams that cover probability and statistics. Representative applications include investigating whether the average tip percentage in a particular restaurant exceeds the standard 15%, considering whether the flavor and aroma of Champagne are affected by bottle temperature or type of pour, modeling the relationship between college graduation rate and average SAT score, and assessing the likelihood of O-ring failure in space shuttle launches as related to launch temperature.
Other features include:
- An extensive range of applications that will appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and business, economics, and quantitative social science students.
- Nearly 1,500 exercises to help students master the material and better understand sophisticated concepts and arguments.
- An emphasis on the importance of statistical software, including output from the statistical software packages Minitab, R, and SAS.
Content Level »Upper undergraduate
Keywords »Descriptive statistics - Inference - Point estimation - Probability - Regression and correlation
Overview and Descriptive Statistics.- Probability.- Discrete Random Variables and Probability Distributions.- Continuous Random Variables and Probability Distributions.- Joint Probability Distributions.- Statistics and Sampling Distributions.- Point Estimation.- Statistical Intervals Based on a Single Sample.- Tests of Hypotheses Based on a Single Sample.- Inferences Based on Two Samples.- The Analysis of Variance.- Regression and Correlation.- Goodness-of-Fit Tests and Categorical Data Analysis.- Alternative Approaches to Inference.- Appendix Tables.