Data Mining & Statistical Analysis Using SQL

By: Robert Trueblood, John N. Jr. Lovett

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Paperback | 1 August 2001

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436 Pages

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22.86 x 18.42 x 3.18

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This book is not just another theoretical text on statistics or data mining. Instead, it's designed for DBAs, database administrators, who want to buttress their understanding of statistics to support data mining and customer relationship management analytics and who want to use SQL, Structured Query Language. Each chapter is independent and self-contained with examples tailored to business applications. Each analysis technique is expressed in a mathematical format that lends itself to coding either as a database query or as a Visual Basic procedure using SQL.Each chapter includes: formulas (how to perform the required analysis, numerical example using data from a database, data visualization and presentation options (graphs, charts, tables), SQL procedures for extracting the desired results, and data mining techniques.About the authors:Robert P. Trueblood is an analyst at QuantiTech, Inc. He has a Ph.D. in computer science and applications and taught at the university level for 18 years before going into private industry. He enjoys designing and implementing systems in Visual Basic.John N. Lovett, Jr. is a senior engineering consultant at QuantiTech, Inc. and co-owner, with his anthropologist/archaeologist wife Jane, of Falls Mill and Museum in Belvedere, TN. He has a Ph.D. in industrial engineering, with a BS in mathematics and MS in operations research.

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Dedication	p. iii
About the Authors	p. iv
Introduction	p. xi
Acknowledgments	p. xvi
Basic Statistical Principles and Diagnostic Tree	p. 1
Categories of Data	p. 2
Sampling Methods	p. 2
Diagnostic Tree	p. 5
SQL Data Extraction Examples	p. 7
Measures of Central Tendency and Dispersion	p. 9
Measures of Central Tendency	p. 10
Mean	p. 10
Median	p. 12
Mode	p. 16
Geometric Mean	p. 17
Weighted Mean	p. 20
Measures of Dispersion	p. 22
Histogram Construction	p. 22
Range	p. 32
Standard Deviation	p. 33
Conclusion	p. 40
Goodness of Fit	p. 41
Tests of Hypothesis	p. 43
Goodness of Fit Test	p. 46
Fitting a Normal Distribution to Observed Data	p. 47
Fitting a Poisson Distribution to Observed Data	p. 62
Fitting an Exponential Distribution to Observed Data	p. 68
Conclusion	p. 71
T-SQL Source Code	p. 72
Make_Intervals	p. 73
Combine_Intervals	p. 74
Compare_Observed_And_Expected	p. 80
Procedure Calls	p. 82
Additional Tests of Hypothesis	p. 85
Comparing a Single Mean to a Specified Value	p. 88
Comparing Means and Variances of Two Samples	p. 94
Comparisons of More Than Two Samples	p. 101
Conclusion	p. 104
T-SQL Source Code	p. 105
Calculate_T_Statistic	p. 105
Calculate_Z_Statistic	p. 107
Compare_Means_2_Samples	p. 108
Contingency_Test	p. 113
Procedure Calls	p. 117
Curve Fitting	p. 119
Linear Regression in Two Variables	p. 121
Linear Correlation in Two Variables	p. 127
Polynomial Regression in Two Variables	p. 130
Other Nonlinear Regression Models	p. 136
Linear Regression in More Than Two Variables	p. 141
Conclusion	p. 147
T-SQL Source Code	p. 147
Linear_Regression_2_Variables	p. 148
Gaussian_Elimination	p. 150
Array_2D	p. 158
Polynomial_Regression	p. 160
Exponential_Model	p. 169
Multiple_Linear_Regression	p. 172
Procedure Calls	p. 179
Control Charting	p. 181
Common and Special Causes of Variation	p. 183
Dissecting the Control Chart	p. 193
Control Chorts for Sample Range and Mean Values	p. 195
Control Chart for Fraction Nonconforming	p. 206
Control Chart for Number of Nonconformities	p. 213
Conclusion	p. 215
T-SQL Source Code	p. 216
Sample_Range_and_Mean_Charts	p. 216
Standard_P_Chart	p. 219
Stabilized_P_Chart	p. 222
C_Chart	p. 224
Procedure Calls	p. 227
Analysis of Experimental Designs	p. 229
One-Way ANOVA	p. 231
Two-Way ANOVA	p. 238
ANOVA Involving Three Factors	p. 245
Conclusion	p. 260
T-SQL Source Code	p. 261
ANOVA	p. 261
Procedure Calls	p. 275
Time Series Analysis	p. 277
Simple Moving Average	p. 278
Single Exponential Smoothing	p. 286
Double Exponential Smoothing	p. 292
Incorporating Seasonal Influences	p. 300
Criteria for Selecting the Most Appropriate Forecasting Technique	p. 308
Conclusion	p. 311
T-SQL Source Code	p. 311
Simple Moving Average	p. 312
Weighted Moving Average	p. 315
Single Exponential Smoothing	p. 318
Double Exponential Smoothing	p. 322
Seasonal Adjustment	p. 327
Procedure Calls	p. 332
Overview of Relational Database Structure and SQL	p. 337
Statistical Tables	p. 359
Tables of Statistical Distributions and Their Characteristics	p. 373
Visual Basic Routines	p. 381
Bibliography	p. 397
Index	p. 399
Table of Contents provided by Syndetics. All Rights Reserved.

Data Mining & Statistical Analysis Using SQL

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Paperback

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