Introductory business statistics with microcomputer applications
Author(s)
Bibliographic Information
Introductory business statistics with microcomputer applications
(The Duxbury series in statistics and decision sciences)
PWS-Kent Pub. Co., 1990
- Other Title
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Business statistics
Available at 5 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Relating statistics to business-studies, this book makes use of cited statistics from research and general literature. Over 1600 end-of-section and end-of-chapter exercises provide students with a variety of types and sources, and with thorough practice. Bivariate data analysis is presented early - important for students who cover business statistics in one term and need to be exposed to bivariate data. Sampling is also presented early so that students know where data comes from and how it is gathered. Quality control and control charts are discussed earlier than is usual because of its emphasis in the international market place and its timeliness. This book should be of interest to business, accounting, finance, management, marketing, and economics students studying statistics.
Table of Contents
Introduction. Organizing and graphing data. Numerical descriptive measures. Organizing and describing bivariate data. Probability and probability distributions. Discrete probability models. Continuous probability models. The sampling distribution of the mean. Introduction to statistical inference. Large sample inferences. Inferences for small samples. Inferences about and comparing two populations. Analysis of variance. Simple regression and correlation. Multiple regression. Developing regression models. The series analysis. Chi-square and other nonparametric procedures.
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