Practical Statistical Methods: A SAS Programming Approach

Practical Statistical Methods: A SAS Programming Approachpresents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

Science & Math

Padgett, Lakshmi

Biological Sciences

CRC-Press

Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems.After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife.With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

Practical Statistical Methods: A Sas Programming Approach

CRC Pr I Llc

An introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs. Repeated Measurements and Cross-Over Designs: <ul>
<li>Features the close tie between the design, analysis, and presentation of results</li> <li>Presents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies</li> <li>Includes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one sample repeated measurement designs for psychological studies, and residual effects of different treatments in controlling conditions such as asthma, blood pressure, and diabetes.</li> <li>Utilizes SAS(R) software to draw necessary inferences. All SAS output and data sets are available via the book's related website.</li> </ul>
This book is ideal for a broad audience including statisticians in pre-clinical research, researchers in psychology, sociology, politics, marketing, and engineering.

Repeated Measurements and Cross-Over Designs

Raghavarao, Damaraju

Experiments, Instruments & Measurement

wiley

Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems.
 After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife.
 With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

See the best price to sell, buy, or rent books by Padgett, Lakshmi. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Padgett, Lakshmi

See the best price to sell, buy, or rent books by Padgett, Lakshmi. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Practical Statistical Methods: A SAS Programming Approach

Practical Statistical Methods: A Sas Programming Approach

Repeated Measurements and Cross-Over Designs

Practical Statistical Methods: A SAS Programming Approach