R for marketing research and analytics
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham [u.a.]
Springer
2015
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| Schriftenreihe: | Use R!
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| Online-Zugang: | Inhaltsverzeichnis |
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MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV042488459 | ||
| 003 | DE-604 | ||
| 005 | 20190722 | ||
| 007 | t| | ||
| 008 | 150408s2015 xx bd|| |||| 00||| eng d | ||
| 020 | |a 9783319144351 |9 978-3-319-14435-1 | ||
| 035 | |a (OCoLC)907621983 | ||
| 035 | |a (DE-599)BVBBV042488459 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1050 |a DE-1043 |a DE-11 |a DE-M347 |a DE-355 |a DE-1049 |a DE-92 |a DE-521 | ||
| 082 | 0 | |a 330.015195 |2 23 | |
| 084 | |a ST 250 |0 (DE-625)143626: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 084 | |a QP 611 |0 (DE-625)141908: |2 rvk | ||
| 100 | 1 | |a Chapman, Chris |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a R for marketing research and analytics |c Chris Chapman ; Elea McDonnell Feit |
| 264 | 1 | |a Cham [u.a.] |b Springer |c 2015 | |
| 300 | |a XVIII, 454 S. |b graph. Darst., Kt. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Use R! | |
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Economics / Statistics | |
| 650 | 4 | |a Marketing | |
| 650 | 4 | |a Statistics for Business/Economics/Mathematical Finance/Insurance | |
| 650 | 4 | |a Statistics and Computing/Statistics Programs | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Marketingforschung |0 (DE-588)4200055-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Marketingforschung |0 (DE-588)4200055-5 |D s |
| 689 | 0 | 1 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | 2 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Feit, Elea McDonnell |e Verfasser |0 (DE-588)14174586X |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-319-14436-8 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027923293&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-027923293 | |
Datensatz im Suchindex
| _version_ | 1819673884321906688 |
|---|---|
| adam_text | Contents
Preface............................................................... vii
Part I Basics of R 1
1 Welcome to R....................................................... 3
1.1 What Is R?...................................................... 3
1.2 Why R?.......................................................... 4
1.3 Why Not R?...................................................... 5
1.4 When R?......................................................... 6
1.5 Using This Book................................................. 6
1.5.1 About the Text......................................... 6
1.5.2 About the Data....................................... 7
1.5.3 Online Material.................... ............... . . 8
1.5.4 When Things Go Wrong .................................... 9
1.6 Key Points............................................ 10
2 An Overview of the R Language.................................. 11
2.1 Getting Started............................................. 11
2.1.1 Initial Steps ....................................... 11
2.1.2 Starting R ......................................... 12
2.2 A Quick Tour of R’s Capabilities ............................. 13
2.3 Basics of Working with R Commands ........................... 17
2.4 Basic Objects ........................,...... .............. 18
2.4.1 Vectors.......-....................... -............... 19
2.4.2 Help! A Brief Detour.................................. 21
2.4.3 More on Vectors and Indexing............................ 24
2.4.4 aaRgh! A Digression for New Programmers................. 26
2.4.5 Missing and Interesting Values ......................... 26
2.4.6 Using R for Mathematical Computation.................... 28
2.4.7 Lists................................................. 28
xi
Contents
xii
2.5 Data Frames..................................................... 30
2.6 Loading and Saving Data...................................... 34
2.6.1 Image Files............................................ 36
2.6.2 CSV Files............................................. 36
2.7 Writing Your Own Functions*..................................... 38
2.7.1 Language Structures*................................... 40
2.7.2 Anonymous Functions* .................................. 41
2.8 Clean Up!.................................................... 42
2. 9 Learning More* ............................................ 43
2.10 Key Points . ................................................ 44
Part П Fundamentals of Data Analysis
3 Describing Data ............................ ..................... 47
3.1 Simulating Data............................................... 47
3.1.1 Store Data: Setting the Structure..................... 48
3.1.2 Store Data: Simulating Data Points ................... 50
3.2 Functions to Summarize a Variable............................ 52
3.2.1 Discrete Variables.................................... 52
3.2.2 Continuous Variables.................................. 54
3.3 Summarizing Data Frames...................................... 56
3.3.1 summary ()............................................ 57
3.3.2 describe () .......................................... 58
3.3.3 Recommended Approach to Inspecting Data .............. 59
3.3.4 apply () ★............................................ 59
3.4 Single Variable Visualization................................ 61
3.4.1 Histograms........................................ 61
3.4.2 Вохріots ............................................. 66
3.4.3 QQ Plot to Check Normality*...... 68
3.4.4 Cumulative Distribution* .......,,.................* - 69
3.4.5 Language Brief: by () and aggregate ()................ 70
3.4.6 Maps.................................................. 72
3.5 Learning More*................................................ 74
3.6 Key Points..............,,. . ............................... 75
4 Relationships Between Continuous Variables ............ ....... 77
4.1 Retailer Data .......................................... 77
4.1.1 Simulating Customer Data ............................. 78
4.1.2 Simulating Online and In֊Store Sales Data............. 79
4.1.3 Simulating Satisfaction Survey Responses .............. 80
4.1.4 Simulating Non-Response Data . ........................ 82
4.2 Exploring Associations Between Variables with Scatterplots.... 83
4.2.1 Creating a Basic Scatterplot with plot () .......... 83
4.2.2 Color-Coding Points on a Scatterplot.... .............. 86
Contents
X1JUL
4.2.3 Adding a Legend to a Plot............................... 88
4.2.4 Plotting on a Log Scale................................. 89
4.3 Combining Plots in a Single Graphics Object.................... 90
4.4 Scatterplot Matrices .......................................... 92
4.4.1 pairs () ............................................... 92
4.4.2 scatterplotMatrix ( ) .................................. 93
4.5 Correlation Coefficients....................................... 95
4.5.1 Correlation Tes ts...................................... 97
4.5.2 Correlation Matrices ................................... 98
4.5.3 Transforming Variables before Computing Correlations .... 100
4.5.4 Typical Marketing Data Transformations .................102
4.5.5 Box-Cox Transformations* ...............................102
4.6 Exploring Associations in Survey Responses*....................104
4.6.1 j itter () *............................................105
4.6.2 polychoric ( ) *........................................106
4.7 Learning More*.................................................107
4.8 Key Points.....................................................108
5 Comparing Groups: Tables and Visualizations .......................Ill
5.1. Simulating Consumer Segment Data...............................Ill
5.1.1 S egment D a ta Defi ni ti o n..........................112
5.1.2 Language Brief: for () Loops............................114
5.1.3 Language Brief: if () Blocks............................116
5.1.4 Final Segment Data Generation ..........................118
5.2 Finding Descriptives by Group .................................120
5.2.1 Language Brief: Basic Formula Syntax....................123
5.2.2 Descrip tives for Two-Way Groups........................124
5.2.3 Visualization by Group: Frequencies and Proportions....126
5.2.4 Visualization by Group: Continuous Data.................129
5.3 Learning More*.................................................132
5.4 Key Points..........r.........................................133
6 Comparing Groups: Statistical Tests ...............................135
6.1 Data for Comparing Groups......................................135
6.2 Testing Group Frequencies: chisq. test {) .....................136
6.3 Testing Observed Proportions: binom . test () .................139
6.3.1 About Confidence Intervals..............................140
6.3.2 More About binom. test () and Binomial Distributions .141
6.4 Testing Group Means: t .test () ...............................142
6.5 Testing Multiple Group Means: ANOVA............................144
6.5.1 Model Comparison in ANOVA*..............................146
6.5.2 Visualizing Group Confidence Intervals .................147
6.5.3 Variable Selection in ANOVA: Stepwise Modeling*.........148
6.6 Bayesian ANOVA: Getting Started*...............................149
6.6.1 Why Bayes?..............................................150
xiv Contents
6.6.2 Basics of Bayesian ANOVA*...............................150
6.6.3 Inspecting the Posterior Draws*........................152
6.6.4 Plotting the Bayesian Credible Intervals*..............155
6.7 Learning More*................ ................................156
6.8 Key Points ....................................................157
7 Identifying Drivers of Outcomes: Linear Models.....................159
7.1 Amusement Park Data ..........................................160
7.1.1 Simulating the Amusement Park Data......................160
7.2 Fitting Linear Models with ltn() .............................162
7.2.1 Preliminary Data Inspection.......................... 163
7.2.2 Recap: Bivariate Association ...........................165
7.2.3 Linear Model with a Single Predictor ...................165
7.2.4 lm Objects..............................................166
7.2.5 Checking Model Fit......................................169
7.3 Fitting Linear Models with Multiple Predictors.................173
7.3.1 Comparing Models....................................... 125
7.3.2 Using a Model to Make Predictions.......................176
7.3.3 Standardizing the Predictors............................177
7.4 Using Factors as Predictors ...................................179
7.5 Interaction Terms..............................................182
7.5.1 Language Brief : Advanced Formula Syntax*...............183
7.6 Caution! Overfitting...........................................185
7.7 Recommended Procedure for Linear Model Fitting.................186
7.8 Bayesian Linear Models with MCMCregress () * ...............186
7.9 Learning More*.................................................188
7.10 Key Points .................................................. 190
Part III Advanced Marketing Applications
8 Reducing Data Complexity............................................195
8.1 Consumer Brand Rating Data ...................................195
8.1.1 Rescaling the Data . ...................................193
8.1.2 Aggregate Mean Ratings by Brand.........................198
8.2 Principal Component Analysis and Perceptual Maps..............200
8.2.1 PC A Example............................................20°
8.2.2 Visualizing PCA.........................................203
8.2.3 PCA for Brand Ratings...................................204
8.2.4 Perceptual Map of the Brands............................206
8.2.5 Cautions with Perceptual Maps...........................208
8.3 Exploratory Factor Analysis....................................209
8.3.1 Basic EFA Concepts......................................230
8.3.2 Finding an EFA Solution.................................233
Contents
XV
8.3.3 EFA Rotations.............................................213
8.3.4 Using Factor Scores for Brands ...........................216
8.4 Multidimensional Scaling.........................................218
8.4.1 Non - metric MDS..........................................219
8.5 Learning More*...................................................221
8.5.1 Principal Component Analysis..............................221
8.5.2 Factor Analysis...........................................221
8.5.3 Multidimensional Scaling..................................222
8.6 Key Points.......................................................222
8.6.1 Principal Component Analysis..............................222
8.6.2 Exploratory Factor Analysis...............................222
8.6.3 Multidimensional Scaling..................................223
Additional Linear Modeling Topics...................................225
9.1 Handling Highly Correlated Variables ............................226
9.1.1 An Initial Linear Model of Online Spend...................226
9.1.2 Remediating Collinearity..................................229
9.2 Linear Models for Binary Outcomes: Logistic Regression..........231.
9.2.1 Basics of the Logistic Regression Model...................231
9.2.2 Data for Logistic Regression of Season Passes.............232
9.2.3 Sales Table Data..........................................233
9.2.4 Language Brief: Classes and Attributes of Objects* .......234
9.2.5 Finalizing the Data.......................................236
9.2.6 Fitting a Logistic Regression Model ......................237
9.2.7 Reconsidering the Model...................................239
9.2.8 Additional Discussion.....................................242
9.3 Hierarchical Linear Models.......................................242
9.3.1 Some HUM Concepts.........................................243
9.3.2 Ratings-Based Conjoint Analysis for the Amusement Park. . 244
9.3.3 Simulating Ratings-Based Conjoint Data ...................245
9.3.4 An Initial Linear Model...................................246
9.3.5 Hierarchical Linear Model with lme4.......................248
9.3.6 The Complete Hierarchical Linear Model....................249
9.3.7 Summary of HUM with lme4 .................................251
9.4 Bayesian Hierarchical Linear Models*.............................252
9.4.1 Initial Linear Model with MCMCregress () * ...............253
9.4.2 Hierarchical Linear Model with MC MChregress () *.........253
9.4.3 Inspecting Distribution of Preference*....................256
9.5 A Quick Comparison of Frequentist Bayesian HUMs* ..............259
9.6 Learning More*...................................................263
9.6.1 Collinearity..............................................263
9.6.2 Logistic Regression.......................................263
9.6.3 Hierarchical Models.......................................263
9.6.4 Bayesian Hierarchical Models .............................263
9.7 Key Points...................................................... 264
9.7.1 Collinearity..............................................264
xvi Contents
9.7.2 Cogistic Regression.......................................264
9.7.3 Hierarchical Linear Models................................265
9.7.4 Bayesian Methods for Hierarchical Linear Models ..........266
10 Confirmatory Factor Analysis and Structural Equation Modeling 267
10.1 The Motivation for Structural Models............................268
10.1.1 Structural Models in. This Chapter.......................269
10.2 Scale Assessment: CFA ..........................................270
10.2.1 Simulating PIES CFA Data..................................272
10.2.2 Estimating the PIES CFA Model.............................277
10.2.3 Assessing the PIES CFA Model..............................278
10.3 General Models: Structural Equation Models .....................283
10.3.1 The Repeat Purchase Model in R............................284
10.3.2 Assessing the Repeat Purchase Model.......................286
10.4 The Partial Least Squares (PLS) Alternative.....................288
10.4.1 PLS-SEM for Repeat Purchase...............................289
10.4.2 Visualizing the Fitted PLS Model*.........................292
10.4.3 Assessing the PLS-SEM Model...............................293
10.4.4 PLS֊SEM. with the Larger Sample...........................295
10.5 Learning More*..................................................297
10.6 Key Points......................................................297 11
11 Segmentation: Clustering and Classification........... ..............299
11.1 Segmentation Philosophy..........................................299
11.1.1 The Difficulty of Segmentation............................299
11.1.2 Segmentation as Clustering and Classification...........300
11.2 Segmentation Data *..............................................302
11.3 Clustering.......................................................302
11.3.1 The Steps of Clustering...................................303
11-3-2 Hierarchical Clustering: Jnclust () Basics......-........305
11.3.3 Hierarchical Clustering Continued: Groups from he lust () 309
11.3.4 Mean-Based Clustering: km earns (}.......................311
11.3.5 Model-Based Clustering: Mclust () ........................314
11.3.6 Comparing Models with BIC() ..............................315
11-3-7 Latent Class Analysis: poLCA ()...........................317
11.3.8 Comparing Cluster Solutions ..............................320
11.3.9 Recap of Clustering.......................................322
11.4 Classification..................................................322
11.4.1 Naive Bayes Classification: naiveBayes () ................323
11.4.2 Random Forest Classification: randomForest () ............327
11.4.3 Random Forest Variable Importance.........................330
11.5 Prediction: Identifying Potential Customers*.....................333
11.6 Learning More*...................................................336
11.7 Key Points.......................................................337
Contents xvu
12 Association Rules for Market Basket Analysis........................339
12.1 The Basics of Association Rules................................340
12.1.1 Metrics.................................................340
12.2 Retail Transaction Data: Market Baskets........................341
12.2.1 Example Data: Groceries ................................342
12.2.2 Supermarket Data . ·...................................344
12.3 Finding and Visualizing Association Rules......................346
12.3.1 Finding and Plotting Subsets of Rules...................348
12.3.2 Using Profit Margin Data with Transactions: An Initial Start 349
12.3.3 Fanguage Brief: A Function for Margin Using an
Object’s class*.........................................351
12.4 Rules in Non-Transactional Data: Exploring Segments Again.....356
12.4.1 Fanguage Brief: Slicing Continuous Data with cut ()....356
12.4.2 Exploring Segment Associations .........................357
12.5 Teaming More*..................................................360
12.6 Key Points.....................................................360
13 Choice Modeling......................................................363
13.1 Choice-Based Conjoint Analysis Surveys ........................364
13.2 Simulating Choice Data*........................................365
13.3 Fitting a Choice Model.........................................370
13.3.1 Inspecting Choice Data..................................371
13.3.2 Fitting Choice Models with mlcgit () ...................372
13.3.3 Reporting Choice Model Findings.........................375
13.3.4 Share Predictions for Identical Alternatives ...........380
13.3.5 Planning the Sample Size for a Conjoint Study .........381
13.4 Adding Consumer Heterogeneity to Choice Models.................383
13.4.1 Estimating Mixed Fogit Models with mlogit () ..........383
13.4.2 Share Prediction for Heterogeneous Choice Models .......386
13.5 Hierarchical Bayes Choice Models...............................388
13.5.1 Estimating Hierarchical Bayes Choice Models with
ChoiceModelR........................................... 388
13.5.2 Share Prediction for Hierarchical Bayes Choice Models .... 395
13.6 Design of Choice-Based Conjoint Surveys*.......................397
13.7 Learning More*.................................................398
13.8 Key Points.....................................................399
Conclusion...............................................................401
A Appendix: R Versions and Related Software............................403
A.l R Base ........................................................403
A.2 RStudio........................................................404
A.3 Emacs Speaks Statistics........................................405
A.4 Eclipse 4- S tat FT............................................406
A. 5 Revolution R................................................ 407
Contents
xviii
A. 6 Other Options ................................................408
A.6.1 Text Editors...........................................408
A.6.2 R Commander............................................408
A.6.3 Rattle..................................................409
A,6.4 Deducer.................................................409
A. 6.5 TIB CO Enterprise Runtime for R.........................409
B Appendix: Scaling Up ................................................411
B. l Handling Data................................................411
B. 1.1 Data Wrangling . ......................................411
B.1.2 Microsoft Excel: gdata..................................412
B.1.3 SAS, SPSS, and Other Statistics Packages: foreign......412
B.l.4 SQL: RSQLite, sqldf and RODBC........................413
B.2 Handling Large Data Sets......................................415
B.3 Speeding Up Computation.......................................416
B.3.1 Efficient Coding and Data Storage.......................416
B. 3.2 Enhancing the R Engine..................................417
B.4 Time Series Analysis, Repeated Measures,
and Longitudinal Analysis......................................418
B. 5 Automated and Interactive Reporting...........................419
C Appendix: Packages Used..............................................423
C. l Core and Frequentist Statistics...............................424
C.2 Graphics......................................................424
C.3 Bayesian Methods..............................................425
C.4 Advanced Statistics...........................................426
C.5 Machine Learning..............................................426
C.6 Data Handling.................................................427
C. 7 Other Packages ................................................428
D Appendix: Online Materials and Data Files ......................... 431
D. 1 Data File Structure ......................................... . 431
D.2 Data File URL Cross-Reference............................... 432
D.2.1 Update on Data Locations................................432
References.............................................................. 435
Index....................................................................447
|
| any_adam_object | 1 |
| author | Chapman, Chris Feit, Elea McDonnell |
| author_GND | (DE-588)14174586X |
| author_facet | Chapman, Chris Feit, Elea McDonnell |
| author_role | aut aut |
| author_sort | Chapman, Chris |
| author_variant | c c cc e m f em emf |
| building | Verbundindex |
| bvnumber | BV042488459 |
| classification_rvk | ST 250 ST 601 QP 611 |
| ctrlnum | (OCoLC)907621983 (DE-599)BVBBV042488459 |
| dewey-full | 330.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.015195 |
| dewey-search | 330.015195 |
| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV042488459 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T04:25:37Z |
| institution | BVB |
| isbn | 9783319144351 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027923293 |
| oclc_num | 907621983 |
| open_access_boolean | |
| owner | DE-1050 DE-1043 DE-11 DE-M347 DE-355 DE-BY-UBR DE-1049 DE-92 DE-521 |
| owner_facet | DE-1050 DE-1043 DE-11 DE-M347 DE-355 DE-BY-UBR DE-1049 DE-92 DE-521 |
| physical | XVIII, 454 S. graph. Darst., Kt. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| series2 | Use R! |
| spellingShingle | Chapman, Chris Feit, Elea McDonnell R for marketing research and analytics Statistics Mathematical statistics Economics / Statistics Marketing Statistics for Business/Economics/Mathematical Finance/Insurance Statistics and Computing/Statistics Programs Statistik Wirtschaft R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd Marketingforschung (DE-588)4200055-5 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4056995-0 (DE-588)4200055-5 |
| title | R for marketing research and analytics |
| title_auth | R for marketing research and analytics |
| title_exact_search | R for marketing research and analytics |
| title_full | R for marketing research and analytics Chris Chapman ; Elea McDonnell Feit |
| title_fullStr | R for marketing research and analytics Chris Chapman ; Elea McDonnell Feit |
| title_full_unstemmed | R for marketing research and analytics Chris Chapman ; Elea McDonnell Feit |
| title_short | R for marketing research and analytics |
| title_sort | r for marketing research and analytics |
| topic | Statistics Mathematical statistics Economics / Statistics Marketing Statistics for Business/Economics/Mathematical Finance/Insurance Statistics and Computing/Statistics Programs Statistik Wirtschaft R Programm (DE-588)4705956-4 gnd Statistik (DE-588)4056995-0 gnd Marketingforschung (DE-588)4200055-5 gnd |
| topic_facet | Statistics Mathematical statistics Economics / Statistics Marketing Statistics for Business/Economics/Mathematical Finance/Insurance Statistics and Computing/Statistics Programs Statistik Wirtschaft R Programm Marketingforschung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027923293&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT chapmanchris rformarketingresearchandanalytics AT feiteleamcdonnell rformarketingresearchandanalytics |