Statistics for Marketing and Consumer Research （统计数据营销和消费者研究）.ppt

下载文档 降价啦

23
0
约1.93万字
约 47页
2017-09-02 发布于浙江
举报
版权申诉
保障服务

Statistics for Marketing and Consumer Research （统计数据营销和消费者研究）.ppt

1、本文档共47页，可阅读全部内容。
2、有哪些信誉好的足球投注网站（book118）网站文档一经付费（服务费），不意味着购买了该文档的版权，仅供个人/单位学习、研究之用，不得用于商业用途，未经授权，严禁复制、发行、汇编、翻译或者网络传播等，侵权必究。
3、本站所有内容均由合作方或网友上传，本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺！文档内容仅供研究参考，付费前请自行鉴别。如您付费，意味着您自己接受本站规则且自行承担风险，本站不退款、不进行额外附加服务；查看《如何避免下载的几个坑》。如果您已付费下载过本站文档，您可以点击这里二次下载。
4、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等，请点击“版权申诉”（推荐），也可以打举报电话：400-050-0827(电话支持时间：9:00-18:30)。

Statistics for Marketing and Consumer Research （统计数据营销和消费者研究）

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Statistics The agglomeration schedule is a table which shows the steps of the clustering procedure, indicating which cases (clusters) are merged and the merging distance The proximity matrix contains all distances between cases (it may be huge) Shows the cluster membership of individual cases only for a sub-set of solutions * Plots Shows the clustering process, indicating which cases are aggregated and the merging distance With many cases, the dendrogram is hardly readable The icicle plot (which can be restricted to cover a small range of clusters), shows at what stage cases are clustered. The plot is cumbersome and slows down the analysis (advice: no icicle) * Method Choose a hierarchical algorithm Choose the type of data (interval, counts binary) and the appropriate measure Specify whether the variables (values) should be standardized before analysis. Z-scores return variables with zero mean and unity variance. Other standardizations are possible. Distance measures can also be transformed * Cluster memberships If the number of clusters has been decided (or at least a range of solutions), it is possible to save the cluster membership for each case into new variables * The example: agglomeration schedule ? ? Cluster Combined ? ? Stage Number of clusters Cluster 1 Cluster 2 Distance Diff. Dist 490 10 8 12 544.4 ? 491 9 8 11 559.3 14.9 492 8 3 7 575.0 15.7 493 7 3 366 591.6 16.6 494 6 3 6 610.6 19.0 495 5 3 37 636.6 26.0 496 4 13 23 663.7 27.1 497 3 3 13 700.8 37.1 498 2 1 8 754.1 53.3 499 1 1 3 864.2 110.2 Last 10 stages of the process (10 to 1 clusters) As the algorithms proceeds towards the end, the distance increases * Scree diagram The scree diagram (not provided by SPSS but created from the agglomeration schedule) shows a larger distance increase when the cluster number goes below 4 Elbow? * Non-hierarchical solutionwith 4 clusters * K-means solution (4 clusters) Variables Number of clusters (fix