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http://rapidshare.com/files/78033408/Data.Analysis.Using.SQL.and.Excel.Oct.2007.rar

Title:

Data Analysis Using SQL and Excel

Gordon S. Linoff

Size: 18.8MB(Only the downloading URL is provided)

Format: PDF

Content:

Foreword xxvii
Acknowledgments xxxi
Introduction xxxiii
Chapter 1 A Data Miner Looks at SQL 1
Picturing the Structure of the Data 2
What Is a Data Model? 3
What Is a Table? 3
Allowing NULL Values 5
Column Types 6
What Is an Entity-Relationship Diagram? 7
The Zip Code Tables 8
Subscription Dataset 10
Purchases Dataset 11
Picturing Data Analysis Using Dataflows 12
What Is a Dataflow? 13
Dataflow Nodes (Operators) 15
READ: Reading a Database Table 15
OUTPUT: Outputting a Table (or Chart) 15
SELECT: Selecting Various Columns in the Table 15
FILTER: Filtering Rows Based on a Condition 15
APPEND: Appending New Calculated Columns 15
UNION: Combining Multiple Datasets into One 16
AGGREGATE: Aggregating Values 16
LOOKUP: Looking Up Values in One Table in Another 16
CROSSJOIN: General Join of Two Tables 16
JOIN: Join Two Tables Together Using a Key Column 16
SORT: Ordering the Results of a Dataset 17
Dataflows, SQL, and Relational Algebra 17
Contents
ix
SQL Queries 18
What to Do, Not How to Do It 18
A Basic SQL Query 19
A Basic Summary SQL Query 20
What it Means to Join Tables 22
Cross-Joins: The Most General Joins 23
Lookup: A Useful Join 24
Equijoins 26
Nonequijoins 27
Outer Joins 28
Other Important Capabilities in SQL 29
UNION ALL 30
CASE 30
IN 31
Subqueries Are Our Friend 32
Subqueries for Naming Variables 33
Subqueries for Handling Summaries 34
Subqueries and IN 36
Rewriting the “IN” as a JOIN 36
Correlated Subqueries 37
The NOT IN Operator 38
Subqueries for UNION ALL 39
Lessons Learned 40
Chapter 2 What’s In a Table? Getting Started with Data Exploration 43
What Is Data Exploration? 44
Excel for Charting 45
A Basic Chart: Column Charts 45
Inserting the Data 46
Creating the Column Chart 47
Formatting the Column Chart 49
Useful Variations on the Column Chart 52
A New Query 52
Side-by-Side Columns 52
Stacked Columns 54
Stacked and Normalized Columns 54
Number of Orders and Revenue 54
Other Types of Charts 56
Line Charts 56
Area Charts 57
X-Y Charts (Scatter Plots) 57
What Values Are in the Columns? 59
Histograms 60
Histograms of Counts 64
Cumulative Histograms of Counts 66
Histograms (Frequencies) for Numeric Values 67
Ranges Based on the Number of Digits, Using
Numeric Techniques 68
x Contents
Ranges Based on the Number of Digits, Using
String Techniques 69
More Refined Ranges: First Digit Plus Number of Digits 69
Breaking Numerics into Equal-Sized Groups 71
More Values to Explore — Min, Max, and Mode 72
Minimum and Maximum Values 72
The Most Common Value (Mode) 73
Calculating Mode Using Standard SQL 73
Calculating Mode Using SQL Extensions 74
Calculating Mode Using String Operations 75
Exploring String Values 76
Histogram of Length 76
Strings Starting or Ending with Spaces 76
Handling Upper- and Lowercase 77
What Characters Are in a String? 77
Exploring Values in Two Columns 79
What Are Average Sales By State? 79
How Often Are Products Repeated within a Single Order? 80
Direct Counting Approach 80
Comparison of Distinct Counts to Overall Counts 81
Which State Has the Most American Express Users? 83
From Summarizing One Column to Summarizing
All Columns 84
Good Summary for One Column 84
Query to Get All Columns in a Table 87
Using SQL to Generate Summary Code 88
Lessons Learned 90
Chapter 3 How Different Is Different? 91
Basic Statistical Concepts 92
The Null Hypothesis 93
Confidence and Probability 94
Normal Distribution 95
How Different Are the Averages? 99
The Approach 99
Standard Deviation for Subset Averages 100
Three Approaches 101
Estimation Based on Two Samples 102
Estimation Based on Difference 104
Counting Possibilities 104
How Many Men? 105
How Many Californians? 110
Null Hypothesis and Confidence 112
How Many Customers Are Still Active? 113
Given the Count, What Is the Probability? 114
Given the Probability, What Is the Number of Stops? 116
The Rate or the Number? 117
Contents xi
Ratios, and Their Statistics 118
Standard Error of a Proportion 118
Confidence Interval on Proportions 120
Difference of Proportions 121
Conservative Lower Bounds 122
Chi-Square 123
Expected Values 123
Chi-Square Calculation 124
Chi-Square Distribution 125
Chi-Square in SQL 127
What States Have Unusual Affinities for Which
Types of Products? 128
Data Investigation 129
SQL to Calculate Chi-Square Values 130
Affinity Results 131
Lessons Learned 132
Chapter 4 Where Is It All Happening? Location, Location, Location 133
Latitude and Longitude 134
Definition of Latitude and Longitude 134
Degrees, Minutes, Seconds, and All That 136
Distance between Two Locations 137
Euclidian Method 137
Accurate Method 139
Finding All Zip Codes within a Given Distance 141
Finding Nearest Zip Code in Excel 143
Pictures with Zip Codes 145
The Scatter Plot Map 145
Who Uses Solar Power for Heating? 146
Where Are the Customers? 148
Census Demographics 149
The Extremes: Richest and Poorest 150
Median Income 150
Proportion of Wealthy and Poor 152
Income Similarity and Dissimilarity Using Chi-Square 152
Comparison of Zip Codes with and without Orders 156
Zip Codes Not in Census File 156
Profiles of Zip Codes with and without Orders 157
Classifying and Comparing Zip Codes 159
Geographic Hierarchies 162
Wealthiest Zip Code in a State? 162
Zip Code with the Most Orders in Each State 165
Interesting Hierarchies in Geographic Data 167
Counties 167
Designated Marketing Areas (DMAs) 168
Census Hierarchies 168
Other Geographic Subdivisions 169
xii Contents
Calculating County Wealth 170
Identifying Counties 170
Measuring Wealth 171
Distribution of Values of Wealth 172
Which Zip Code Is Wealthiest Relative to Its County? 173
County with Highest Relative Order Penetration 175
Mapping in Excel 177
Why Create Maps? 178
It Can’t Be Done 179
Mapping on the Web 180
State Boundaries on Scatter Plots of Zip Codes 180
Plotting State Boundaries 180
Pictures of State Boundaries 182
Lessons Learned 183
Chapter 5 It’s a Matter of Time 185
Dates and Times in Databases 186
Some Fundamentals of Dates and Times in Databases 187
Extracting Components of Dates and Times 187
Converting to Standard Formats 189
Intervals (Durations) 190
Time Zones 191
Calendar Table 191
Starting to Investigate Dates 192
Verifying that Dates Have No Times 192
Comparing Counts by Date 193
Orderlines Shipped and Billed 193
Customers Shipped and Billed 195
Number of Different Bill and Ship Dates per Order 196
Counts of Orders and Order Sizes 197
Items as Measured by Number of Units 198
Items as Measured by Distinct Products 198
Size as Measured by Dollars 201
Days of the Week 203
Billing Date by Day of the Week 203
Changes in Day of the Week by Year 204
Comparison of Days of the Week for Two Dates 205
How Long between Two Dates? 206
Duration in Days 206
Duration in Weeks 208
Duration in Months 209
How Many Mondays? 210
A Business Problem about Days of the Week 210
Outline of a Solution 210
Solving It in SQL 212
Using a Calendar Table Instead 213
Contents xiii
Year-over-Year Comparisons 213
Comparisons by Day 213
Adding a Moving Average Trend Line 214
Comparisons by Week 215
Comparisons by Month 216
Month-to-Date Comparison 218
Extrapolation by Days in Month 220
Estimation Based on Day of Week 221
Estimation Based on Previous Year 223
Counting Active Customers by Day 224
How Many Customers on a Given Day? 224
How Many Customers Every Day? 224
How Many Customers of Different Types? 226
How Many Customers by Tenure Segment? 227
Simple Chart Animation in Excel 231
Order Date to Ship Date 231
Order Date to Ship Date by Year 234
Querying the Data 234
Creating the One-Year Excel Table 235
Creating and Customizing the Chart 236
Lessons Learned 238
Chapter 6 How Long Will Customers Last? Survival Analysis
to Understand Customers and Their Value 239
Background on Survival Analysis 240
Life Expectancy 242
Medical Research 243
Examples of Hazards 243
The Hazard Calculation 245
Data Investigation 245
Stop Flag 245
Tenure 247
Hazard Probability 249
Visualizing Customers: Time versus Tenure 250
Censoring 251
Survival and Retention 253
Point Estimate for Survival 254
Calculating Survival for All Tenures 254
Calculating Survival in SQL 256
Step 1. Create the Survival Table 257
Step 2: Load POPT and STOPT 257
Step 3: Calculate Cumulative Population 258
Step 4: Calculate the Hazard 259
Step 5: Calculate the Survival 259
Step 6: Fix ENDTENURE and NUMDAYS in Last Row 260
Generalizing the SQL 260
xiv Contents
A Simple Customer Retention Calculation 260
Comparison between Retention and Survival 262
Simple Example of Hazard and Survival 262
Constant Hazard 263
What Happens to a Mixture 264
Constant Hazard Corresponding to Survival 266
Comparing Different Groups of Customers 267
Summarizing the Markets 267
Stratifying by Market 268
Survival Ratio 270
Conditional Survival 272
Comparing Survival over Time 272
How Has a Particular Hazard Changed over Time? 273
What Is Customer Survival by Year of Start? 275
What Did Survival Look Like in the Past? 275
Important Measures Derived from Survival 278
Point Estimate of Survival 278
Median Customer Tenure 279
Average Customer Lifetime 281
Confidence in the Hazards 282
Using Survival for Customer Value Calculations 284
Estimated Revenue 285
Estimating Future Revenue for One Future Start 286
SQL Day-by-Day Approach 287
SQL Summary Approach 288
Estimated Revenue for a Simple Group of Existing Customers 289
Estimated Second Year Revenue for a Homogenous Group 289
Pre-calculating Yearly Revenue by Tenure 291
Estimated Future Revenue for All Customers 292
Lessons Learned 295
Chapter 7 Factors Affecting Survival: The What and
Why of Customer Tenure 297
What Factors Are Important and When 298
Explanation of the Approach 298
Using Averages to Compare Numeric Variables 301
The Answer 301
Answering the Question in SQL 302
Extension to Include Confidence Bounds 304
Hazard Ratios 306
Interpreting Hazard Ratios 306
Calculating Hazard Ratios 307
Why the Hazard Ratio 308
Left Truncation 309
Recognizing Left Truncation 309
Effect of Left Truncation 311
Contents xv
How to Fix Left Truncation, Conceptually 313
Estimating Hazard Probability for One Tenure 314
Estimating Hazard Probabilities for All Tenures 314
Time Windowing 316
A Business Problem 317
Time Windows = Left Truncation + Right Censoring 318
Calculating One Hazard Probability Using a Time Window 318
All Hazard Probabilities for a Time Window 319
Comparison of Hazards by Stops in Year 320
Competing Risks 321
Examples of Competing Risks 322
I=Involuntary Churn 322
V=Voluntary Churn 323
M=Migration 323
Other 324
Competing Risk “Hazard Probability” 324
Competing Risk “Survival” 326
What Happens to Customers over Time 327
Example 327
A Cohort-Based Approach 328
The Survival Analysis Approach 330
Before and After 332
Three Scenarios 333
A Billing Mistake 333
A Loyalty Program 333
Raising Prices 335
Using Survival Forecasts 335
Forecasting Identified Customers Who Stopped 336
Estimating Excess Stops 336
Before and After Comparison 337
Cohort-Based Approach 338
Direct Estimation of Event Effect 341
Approach to the Calculation 341
Time-Varying Covariate Survival Using SQL and Excel 342
Lessons Learned 344
Chapter 8 Customer Purchases and Other Repeated Events 347
Identifying Customers 348
Who Is the Customer? 348
How Many? 349
How Many Genders in a Household 351
Investigating First Names 354
Other Customer Information 358
First and Last Names 358
Addresses 360
Other Identifying Information 361
xvi Contents
How Many New Customers Appear Each Year? 362
Counting Customers 362
Span of Time Making Purchases 364
Average Time between Orders 367
Purchase Intervals 369
RFM Analysis 370
The Dimensions 370
Recency 371
Frequency 374
Monetary 374
Calculating the RFM Cell 375
Utility of RFM 377
A Methodology for Marketing Experiments 377
Customer Migration 378
RFM Limits 380
Which Households Are Increasing Purchase
Amounts Over Time? 381
Comparison of Earliest and Latest Values 381
Calculating the Earliest and Latest Values 381
Comparing the First and Last Values 386
Comparison of First Year Values and Last Year Values 390
Trend from the Best Fit Line 392
Using the Slope 393
Calculating the Slope 393
Time to Next Event 395
Idea behind the Calculation 395
Calculating Next Purchase Date Using SQL 396
From Next Purchase Date to Time-to-Event 397
Stratifying Time-to-Event 398
Lessons Learned 399
Chapter 9 What’s in a Shopping Cart? Market Basket Analysis
and Association Rules 401
Exploratory Market Basket Analysis 402
Scatter Plot of Products 402
Duplicate Products in Orders 403
Histogram of Number of Units 407
Products Associated with One-Time Customers 408
Products Associated with the Best Customers 410
Changes in Price 413
Combinations (Item Sets) 415
Combinations of Two Products 415
Number of Two-Way Combinations 415
Generating All Two-Way Combinations 417
Examples of Combinations 419
Variations on Combinations 420
Combinations of Product Groups 420
Multi-Way Combinations 422
Contents xvii
Households Not Orders 424
Combinations within a Household 424
Investigating Products within Households but
Not within Orders 425
Multiple Purchases of the Same Product 426
The Simplest Association Rules 428
Associations and Rules 428
Zero-Way Association Rules 429
What Is the Distribution of Probabilities? 429
What Do Zero-Way Associations Tell Us? 430
One-Way Association Rules 431
Example of One-Way Association Rules 431
Generating All One-Way Rules 433
One-Way Rules with Evaluation Information 434
One-Way Rules on Product Groups 436
Calculating Product Group Rules Using an
Intermediate Table 438
Calculating Product Group Rules Using
Window Functions 440
Two-Way Associations 441
Calculating Two-Way Associations 441
Using Chi-Square to Find the Best Rules 442
Applying Chi-Square to Rules 442
Applying Chi-Square to Rules in SQL 444
Comparing Chi-Square Rules to Lift 445
Chi-Square for Negative Rules 447
Heterogeneous Associations 448
Rules of the Form “State Plus Product” 448
Rules Mixing Different Types of Products 450
Extending Association Rules 451
Multi-Way Associations 451
Rules Using Attributes of Products 452
Rules with Different Left- and Right-Hand Sides 453
Before and After: Sequential Associations 454
Lessons Learned 455
Chapter 10 Data Mining Models in SQL 457
Introduction to Directed Data Mining 458
Directed Models 459
The Data in Modeling 459
Model Set 459
Score Set 461
Prediction Model Sets versus Profiling Model Sets 461
Examples of Modeling Tasks 463
Similarity Models 463
Yes-or-No Models (Binary Response Classification) 463
xviii Contents
Yes-or-No Models with Propensity Scores 464
Multiple Categories 465
Estimating Numeric Values 465
Model Evaluation 465
Look-Alike Models 466
What Is the Model? 466
What Is the Best Zip Code? 466
A Basic Look-Alike Model 468
Look-Alike Using Z-Scores 469
Example of Nearest Neighbor Model 473
Lookup Model for Most Popular Product 475
Most Popular Product 475
Calculating Most Popular Product Group 475
Evaluating the Lookup Model 477
Using a Profiling Lookup Model for Prediction 478
Using Binary Classification Instead 480
Lookup Model for Order Size 481
Most Basic Example: No Dimensions 481
Adding One Dimension 482
Adding More Dimensions 484
Examining Nonstationarity 484
Evaluating the Model Using an Average Value Chart 485
Lookup Model for Probability of Response 487
The Overall Probability as a Model 487
Exploring Different Dimensions 488
How Accurate Are the Models? 490
Adding More Dimensions 493
Naïve Bayesian Models (Evidence Models) 495
Some Ideas in Probability 495
Probabilities 496
Odds 497
Likelihood 497
Calculating the Naïve Bayesian Model 498
An Intriguing Observation 499
Bayesian Model of One Variable 500
Bayesian Model of One Variable in SQL 500
The “Naïve” Generalization 502
Naïve Bayesian Model: Scoring and Lift 504
Scoring with More Attributes 505
Creating a Cumulative Gains Chart 506
Comparison of Naïve Bayesian and Lookup Models 507
Lessons Learned 508
Chapter 11 The Best-Fit Line: Linear Regression Models 511
The Best-Fit Line 512
Tenure and Amount Paid 512
Contents xix
Properties of the Best-fit Line 513
What Does Best-Fit Mean? 513
Formula for Line 515
Expected Value 515
Error (Residuals) 517
Preserving the Averages 518
Inverse Model 518
Beware of the Data 519
Trend Lines in Charts 521
Best-fit Line in Scatter Plots 521
Logarithmic, Power, and Exponential Trend Curves 522
Polynomial Trend Curves 524
Moving Average 525
Best-fit Using LINEST() Function 528
Returning Values in Multiple Cells 528
Calculating Expected Values 530
LINEST() for Logarithmic, Exponential, and Power Curves 531
Measuring Goodness of Fit Using R2 532
The R2 Value 532
Limitations of R2 534
What R2 Really Means 535
Direct Calculation of Best-Fit Line Coefficients 536
Doing the Calculation 536
Calculating the Best-Fit Line in SQL 537
Price Elasticity 538
Price Frequency 539
Price Frequency for $20 Books 541
Price Elasticity Model in SQL 542
Price Elasticity Average Value Chart 543
Weighted Linear Regression 544
Customer Stops during the First Year 545
Weighted Best Fit 546
Weighted Best-Fit Line in a Chart 548
Weighted Best-Fit in SQL 549
Weighted Best-Fit Using Solver 550
The Weighted Best-Fit Line 550
Solver Is Better Than Guessing 551
More Than One Input Variable 552
Multiple Regression in Excel 552
Getting the Data 553
Investigating Each Variable Separately 554
Building a Model with Three Input Variables 555
Using Solver for Multiple Regression 557
Choosing Input Variables One-By-One 558
Multiple Regression in SQL 558
Lessons Learned 560
xx Contents
Chapter 12 Building Customer Signatures for Further Analysis 563
What Is a Customer Signature? 564
What Is a Customer? 565
Sources of Data for the Customer Signature 566
Current Customer Snapshot 566
Initial Customer Information 567
Self-Reported Information 568
External Data (Demographic and So On) 568
About Their Neighbors 569
Transaction Summaries 569
Using Customer Signatures 570
Predictive and Profile Modeling 570
Ad Hoc Analysis 570
Repository of Customer-Centric Business Metrics 570
Designing Customer Signatures 571
Column Roles 571
Identification Columns 571
Input Columns 572
Target Columns 572
Foreign Key Columns 572
Cutoff Date 573
Profiling versus Prediction 573
Time Frames 573
Naming of Columns 574
Eliminating Seasonality 574
Adding Seasonality Back In 575
Multiple Time Frames 576
Operations to Build a Customer Signature 577
Driving Table 578
Using an Existing Table as the Driving Table 578
Derived Table as the Driving Table 580
Looking Up Data 580
Fixed Lookup Tables 581
Customer Dimension Lookup Tables 582
Initial Transaction 584
Without Window Functions 584
With Window Functions 586
Pivoting 586
Payment Type Pivot 588
Channel Pivot 589
Year Pivot 590
Order Line Information Pivot 591
Summarizing 594
Basic Summaries 594
More Complex Summaries 594
Contents xxi
Extracting Features 596
Geographic Location Information 596
Date Time Columns 597
Patterns in Strings 598
Email Addresses 598
Addresses 599
Product Descriptions 599
Credit Card Numbers 600
Summarizing Customer Behaviors 601
Calculating Slope for Time Series 601
Calculating Slope from Pivoted Time Series 601
Calculating Slope for a Regular Time Series 603
Calculating Slope for an Irregular Time Series 604
Weekend Shoppers 604
Declining Usage Behavior 606
Lessons Learned 609
Appendix Equivalent Constructs Among Databases 611
String Functions 612
Searching for Position of One String within Another 612
IBM 612
Microsoft 613
mysql 613
Oracle 613
SAS proc sql 613
String Concatenation 614
IBM 614
Microsoft 614
mysql 614
Oracle 614
SAS proc sql 614
String Length Function 614
IBM 614
Microsoft 615
mysql 615
Oracle 615
SAS proc sql 615
Substring Function 615
IBM 615
Microsoft 615
mysql 615
Oracle 616
SAS proc sql 616
Replace One Substring with Another 616
IBM 616
Microsoft 616
xxii Contents
mysql 616
Oracle 616
SAS proc sql 616
Remove Leading and Trailing Blanks 617
IBM 617
Microsoft 617
mysql 617
Oracle 617
SAS proc sql 617
RIGHT Function 617
IBM 617
Microsoft 617
mysql 618
Oracle 618
SAS proc sql 618
LEFT Function 618
IBM 618
Microsoft 618
mysql 618
Oracle 618
SAS proc sql 619
ASCII Function 619
IBM 619
Microsoft 619
mysql 619
Oracle 619
SAS proc sql 619
Date Time Functions 619
Date Constant 619
IBM 620
Microsoft 620
mysql 620
Oracle 620
SAS proc sql 620
Current Date and Time 620
IBM 620
Microsoft 620
mysql 621
Oracle 621
SAS proc sql 621
Convert to YYYYMMDD String 621
IBM 621
Microsoft 621
mysql 621
Oracle 621
SAS proc sql 621
Contents xxiii
Year, Month, and Day of Month 622
IBM 622
Microsoft 622
mysql 622
Oracle 622
SAS proc sql 623
Day of Week (Integer and String) 623
IBM 623
Microsoft 623
mysql 623
Oracle 623
SAS proc sql 623
Adding (or Subtracting) Days from a Date 623
IBM 624
Microsoft 624
mysql 624
Oracle 624
SAS proc sql 624
Adding (or Subtracting) Months from a Date 624
IBM 624
Microsoft 624
mysql 624
Oracle 625
SAS proc sql 625
Difference between Two Dates in Days 625
IBM 625
Microsoft 625
mysql 625
Oracle 625
SAS proc sql 625
Difference between Two Dates in Months 625
IBM 626
Microsoft 626
mysql 626
Oracle 626
SAS proc sql 626
Extracting Date from Date Time 626
IBM 626
Microsoft 626
mysql 627
Oracle 627
SAS proc sql 627
Mathematical Functions 627
Remainder/Modulo 627
IBM 627
Microsoft 627
xxiv Contents
mysql 627
Oracle 627
SAS proc sql 628
Power 628
IBM 628
Microsoft 628
mysql 628
Oracle 628
SAS proc SQL 628
Floor 628
IBM 628
Microsoft 628
mysql 629
Oracle 629
SAS proc sql 629
“Random” Numbers 629
IBM 629
Microsoft 629
mysql 629
Oracle 629
SAS proc sql 630
Left Padding an Integer with Zeros 630
IBM 630
Microsoft 630
mysql 630
Oracle 630
SAS proc sql 630
Conversion from Number to String 630
IBM 630
Microsoft 631
mysql 631
Oracle 631
SAS proc sql 631
Other Functions and Features 631
Least and Greatest 631
IBM 631
Microsoft 632
mysql 632
Oracle 632
SAS proc sql 632
Return Result with One Row 632
IBM 632
Microsoft 633
mysql 633
Oracle 633
SAS proc sql 633
Contents xxv
Return a Handful of Rows 633
IBM 633
Microsoft 633
mysql 633
Oracle 634
SAS proc sql 634
Get List of Columns in a Table 634
IBM 634
Microsoft 634
mysql 634
Oracle 634
SAS proc sql 635
ORDER BY in Subqueries 635
IBM 635
Microsoft 635
mysql 635
Oracle 635
SAS proc sql 635
Window Functions 635
IBM 635
Microsoft 635
mysql 636
Oracle 636
SAS proc sql 636
Average of Integers 636
IBM 636
Microsoft 636
mysql 636
Oracle 636
SAS proc sql 636
Index 637


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楼上的兄台,能否下载下来压缩上传到论坛啊

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还钱来。骗人的。大家不要上当

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骗子。
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