Practical Statistical Methods

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A01=Lakshmi Padgett
advanced SAS statistical programming applications
Aft Model
Age Group_Uncategorized
Age Group_Uncategorized
analysis
ANOVA Table
Author_Lakshmi Padgett
automatic-update
biomedical statistics
categorical data techniques
Category1=Non-Fiction
Category=PBT
Category=UFM
COP=United Kingdom
CORR Procedure
Delivery_Delivery within 10-20 working days
Dummy Variables
educational statistics
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Estimate Std Error
freq
GLM Procedure
hypothesis
interim
Interim Analysis
Jonckheere Test
Language_English
Mantel Haenszel Chi Square
Method Num DF Den DF
Missing Data
odds
PA=Available
Partial Null Hypotheses
PHREG Procedure
Price_€20 to €50
print
proc
Proc Freq
Proc Print
propensity score methodology
Proportional Odds Assumption
PS=Active
quantitative data analysis
ratio
Ridit Analysis
Roc Curve
softlaunch
sort
Source DF
Source DF Sum
Statistic Alternative Hypothesis DF
Std Dev Sum Minimum Maximum
survival analysis methods
Test Chi Square DF Pr
Welch's ANOVA
Welch’s ANOVA
Wilcoxon Mann Whitney Test

Product details

  • ISBN 9780367382834
  • Weight: 474g
  • Dimensions: 156 x 234mm
  • Publication Date: 23 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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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.

Lakshmi V. Padgett is a senior manager at Centocor. She has more than 15 years of industrial experience and has published several papers in various leading journals. She also co-authored Block Designs: Analysis, Combinatorics and Applications. She earned a Ph.D. from Temple University.

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