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Business Statistics Made Easy in SAS [Pehme köide]

  • Formaat: Paperback / softback, 384 pages, kõrgus x laius x paksus: 235x191x20 mm, kaal: 658 g, Illustrations, unspecified
  • Ilmumisaeg: 30-Oct-2015
  • Kirjastus: SAS Institute
  • ISBN-10: 1629598410
  • ISBN-13: 9781629598413
  • Formaat: Paperback / softback, 384 pages, kõrgus x laius x paksus: 235x191x20 mm, kaal: 658 g, Illustrations, unspecified
  • Ilmumisaeg: 30-Oct-2015
  • Kirjastus: SAS Institute
  • ISBN-10: 1629598410
  • ISBN-13: 9781629598413
Learn or refresh core statistical methods for business with SAS and approach real business analytics issues and techniques using a practical approach that avoids complex mathematics and instead employs easy-to-follow explanations. Business Statistics Made Easy in SAS is designed as a user-friendly, practice-oriented, introductory text to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS, and basic statistics (descriptive statistics and basic associational statistics). The book also provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing.The book steers away from complex mathematical-based explanations, and it also avoids basing explanations on the traditional build-up of distributions, probability theory and the like, which tend to lose the practice-oriented reader. Instead, it teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors.With no previous SAS experience necessary, Business Statistics Made Easy in SAS is an ideal introduction for beginners. It is suitable for introductory undergraduate classes, postgraduate courses such as MBA refresher classes, and for the business practitioner. It is compatible with SAS University Edition.
Preface ix
About the Author xv
Acknowledgments xvii
Chapter 1 Introduction to the Central Textbook Example 1(8)
Introduction
1(1)
The Company
2(1)
Current Research Needs of the Company
2(3)
Your Brief for the Case Example
5(1)
Extended Analytical Skills Needed in the Project
6(3)
Chapter 2 Introduction to the Statistics Process 9(12)
Introductory Case: Big Data in the Airline Industry
9(2)
Introduction to the Statistics Process
11(1)
Step 1: Your Needs & Requirements
12(1)
Step 2: Getting Data
13(2)
Step 3: Extracting Statistics from the Data
15(2)
Step 4: Understanding & Decision Making
17(1)
Summary: Challenges in the Statistics Process
17(1)
Advice to the Statistically Terrified
18(3)
Chapter 3 Introduction to Data 21(12)
Introductory Case: Royal FrieslandCampina
21(2)
Brief Introduction to Samples, Populations & Data
23(4)
Basic Characteristics of Variables
27(6)
Chapter 4 Data Collection & Capture 33(18)
Introduction
33(1)
Correct Sampling
34(1)
Choose Constructs and Variable Measurements
35(8)
Initial Data Capture: Which Package?
43(1)
Dealing with Data Once It Has Been Captured
43(5)
Database & Data Analysis Software
48(1)
Some Complications in Datasets
48(3)
Chapter 5 Introduction to SAS® 51(18)
Introductory Vignette: SAS On Top of the Analytics World
51(1)
Brief Introduction to SAS
52(1)
Introduction to the Textbook Materials
53(1)
Getting Started with SAS 9 or SAS Studio
53(16)
Chapter 6 Basics of SAS Programs, Data Manipulation, Analysis & Reporting 69(20)
Introduction
70(1)
The Running Data Example
70(2)
The Pre-Analysis Data Cleaning & Preparation Steps
72(1)
Overview of the Three Big Tasks in Business Statistics
73(1)
Basic Introduction to SAS Programming
73(4)
Major Task #1: Data Manipulation in SAS
77(6)
Major Task #2: Data Analysis
83(1)
Major Task #3: SAS Reporting through Output Formats
84(2)
The Visual Programmer Mode in SAS Studio
86(2)
Conclusion
88(1)
Chapter 7 Descriptive Statistics: Understand your Data 89(20)
Introductory Case: 2007 AngloGold Ashanti Look Ahead
90(1)
Introduction
91(1)
End Outcome of a Descriptive Statistics Analysis
91(1)
Getting Descriptive Statistics in SAS
92(2)
Statistics Measuring Centrality
94(3)
Basic Statistics Assessing Variable Spread
97(2)
Assessing Shape of a Variable's Distribution
99(5)
Conclusion on Descriptive Statistics
104(1)
Appendix A to
Chapter 7: Basic Normality Statistics
104(5)
Chapter 8 Basics of Associating Variables 109(14)
Introduction
109(1)
What is Statistical Association?
110(1)
Association Does Not Mean Causation
110(1)
Overview of Associations for Different Variable Types
111(1)
Relating Continuous or Ordinal Data: Correlation & Covariance
112(7)
Relating Categorical Variables
119(4)
Chapter 9 Using Basic Statistics to Check & Fix Data 123(12)
Introduction
123(1)
Inappropriate Data Points
124(2)
Dealing Practically with Missing Data
126(1)
Checking Centrality & Spread
127(1)
Strange Variable Distributions
128(1)
Dealing Practically with Multi-Item Scales
128(7)
Chapter 10 Introduction to Graphing in SAS 135(14)
Introduction
135(1)
Major Graphing Procedures in SAS
136(2)
The PROC SGPLOT Routine in SAS
138(5)
Multiple Plots Simultaneously through PROC SGPANEL
143(1)
Business Dashboards through PROC GKPI
143(2)
Geographical Mapping Using PROC GMAP
145(1)
PROC SGSCATTER for Multiple Scatterplots
146(1)
Conclusion on SAS Graphing
147(2)
Chapter 11 The Statistics Process: Fitting Models to Data 149(22)
Introduction
149(2)
Look for Patterns in the Data (Fit)
151(13)
Step 3: Interpret the Pattern
164(4)
Summary of the Statistics Process
168(3)
Chapter 12 Key Concepts: Size & Accuracy 171(40)
Illustrative Case: Pharmaceuticals I - AstraZeneca's Crestor
172(1)
Introduction
173(1)
Issue # 1: Size of a Statistic
173(4)
Issue # 2: Accuracy of Statistics
177(2)
The Aspects of Inaccuracy
179(21)
Putting Statistical Size and Accuracy Together
200(2)
Conclusion
202(1)
Appendix A to
Chapter 12: More on Accuracy (optional)
203(8)
Chapter 13 Introduction to Linear Regression 211(58)
Illustrative Case: West Point
212(1)
Introduction
213(1)
The Core Textbook Case Example for
Chapter 13
213(2)
Introduction to Linear Regression
215(2)
A Pictorial Walk through Regression
217(9)
Implementing Multiple Regression in SAS
226(1)
Step 1: Collect, Capture and Clean Data
227(4)
Step 2: Run an Initial Regression Analysis
231(2)
Step 3: Assess Fit and Apply Remedies If Necessary
233(24)
Step 4: Interpret the Regression Slopes
257(8)
Step 5: Reporting a Multiple Regression Result
265(1)
Other Statistical Forms
266(1)
Conclusion
267(2)
Chapter 14 Categories Explaining a Continuous Variable: Comparing Two Means 269(16)
Introduction to Comparison of Categories
270(1)
Features of the Continuous Variable to Compare Across Categories
270(1)
Two Types of Categories to Compare
271(1)
Numbers of Categories to Compare: Two vs. More than Two
272(1)
Data Assumptions and Alternatives when Comparing Categories
273(2)
Comparing Two Means: T-Tests
275(9)
Comparing Means for More than Two Categories: ANOVA
284(1)
Chapter 15 Categorical Data Distributions & Associations 285(14)
Introduction
285(1)
Repeat: One-Way Categorical Distributions
286(1)
Repeat: Linking Categorical Variables Together
287(1)
Further Statistical Questions about Categorical Data
287(1)
Assessing One-Way Frequencies
288(5)
Tests of Categorical Variable Association
293(5)
Conclusion on Categorical Data Analysis
298(1)
Chapter 16 Reporting Business Analytics 299(10)
Reminder - Your Brief for the Textbook Case Study
299(1)
Your Tasks in the Analytics and Reporting Stages
300(1)
Background Analyses Versus Displayed Reports for the CEO
300(8)
Conclusion on Business Statistics Reporting
308(1)
Chapter 17 Business Analysis from Statistics: Introduction 309(16)
Case Study: Oracle South Africa
310(1)
Introduction
311(1)
Overall Financial Extrapolation Process
312(1)
Step 1: Statistics Gives Level of or Change in Focal Variables
313(1)
Step 2: Financial Estimates of Revenue or Cost of One Unit
314(4)
Step 3: Combine Statistics with Per-Unit Financial Values
318(1)
Step 4: Include Scope
319(1)
Steps 5 and 6: Net Profitability Calculations
319(2)
Some Simple Examples of Business Extrapolation
321(2)
Conclusion of Statistical Business Extrapolation
323(2)
Chapter 18 Miscellaneous Business Statistics Topics 325(18)
Introduction
326(1)
Big Data
326(4)
Data Warehousing
330(5)
Machine Learning & Algorithms
335(1)
Simulation in Business Situations
336(4)
Bayesian Statistics
340(2)
Conclusion
342(1)
Chapter 19 Bibliography 343(8)
Books and Articles
343(8)
Index 351