{"id":476185,"date":"2023-08-09T07:26:52","date_gmt":"2023-08-09T07:26:52","guid":{"rendered":""},"modified":"2023-09-05T11:12:11","modified_gmt":"2023-09-05T11:12:11","slug":"categorical-data","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/categorical-data\/","title":{"rendered":"\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u30c7\u30fc\u30bf"},"content":{"rendered":"<p>\u30ab\u30c6\u30b4\u30ea \u30c7\u30fc\u30bf\u306f\u3001\u7d71\u8a08\u304a\u3088\u3073\u30c7\u30fc\u30bf\u5206\u6790\u306b\u304a\u3051\u308b\u30ab\u30c6\u30b4\u30ea\u5909\u6570\u30ab\u30c6\u30b4\u30ea\u306b\u5206\u985e\u3055\u308c\u308b\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u3067\u3059\u3002\u9023\u7d9a\u5024\u3067\u69cb\u6210\u3055\u308c\u308b\u6570\u5024\u30c7\u30fc\u30bf\u3068\u306f\u7570\u306a\u308a\u3001\u30ab\u30c6\u30b4\u30ea 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\u307e\u308c\u306a\u30ab\u30c6\u30b4\u30ea\u3067\u306f\u5341\u5206\u306a\u60c5\u5831\u304c\u63d0\u4f9b\u3055\u308c\u306a\u3044\u53ef\u80fd\u6027\u304c\u3042\u308b\u305f\u3081\u3001\u305d\u308c\u3089\u3092\u30de\u30fc\u30b8\u3059\u308b\u304b\u3001\u5225\u306e\u30b0\u30eb\u30fc\u30d7\u3068\u3057\u3066\u4f7f\u7528\u3059\u308b\u3068\u3001\u3053\u306e\u554f\u984c\u306e\u89e3\u6c7a\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u306a\u7279\u5fb4\u3068\u985e\u4f3c\u7528\u8a9e\u3068\u306e\u6bd4\u8f03\uff1a\u8868\u3068\u30ea\u30b9\u30c8<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u6027<\/th>\n<th>\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u30c7\u30fc\u30bf<\/th>\n<th>\u6570\u5024\u30c7\u30fc\u30bf<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u8868\u73fe<\/td>\n<td>\u30e9\u30d9\u30eb\u307e\u305f\u306f\u30b3\u30fc\u30c9<\/td>\n<td>\u6570\u5024<\/td>\n<\/tr>\n<tr>\n<td>\u5206\u6790\u624b\u6cd5<\/td>\n<td>\u30ab\u30a4\u4e8c\u4e57\u691c\u5b9a\u3001<\/td>\n<td>\u5e73\u5747\u5024\u3001\u4e2d\u592e\u5024\u3001<\/td>\n<\/tr>\n<tr>\n<td><\/td>\n<td>\u30af\u30ed\u30b9\u96c6\u8a08<\/td>\n<td>\u56de\u5e30<\/td>\n<\/tr>\n<tr>\n<td>\u30c7\u30fc\u30bf\u306e\u6027\u8cea<\/td>\n<td>\u96e2\u6563<\/td>\n<td>\u7d99\u7d9a\u7684<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u30c7\u30fc\u30bf\u306b\u95a2\u9023\u3059\u308b\u5c06\u6765\u306e\u5c55\u671b\u3068\u30c6\u30af\u30ce\u30ed\u30b8\u30fc<\/h2>\n<p>\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3068\u4eba\u5de5\u77e5\u80fd\u306e\u9032\u6b69\u306b\u4f34\u3044\u3001\u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u306e\u5206\u6790\u3068\u6d3b\u7528\u306f\u9032\u5316\u3057\u7d9a\u3051\u307e\u3059\u3002\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u6539\u5584\u306b\u3088\u308a\u3001\u30ab\u30c6\u30b4\u30ea\u5909\u6570\u306b\u57fa\u3065\u304f\u4e88\u6e2c\u3068\u610f\u601d\u6c7a\u5b9a\u30d7\u30ed\u30bb\u30b9\u306e\u7cbe\u5ea6\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u306e\u9032\u6b69\u306b\u3088\u308a\u3001\u975e\u69cb\u9020\u5316\u30c6\u30ad\u30b9\u30c8 \u30c7\u30fc\u30bf\u306e\u7406\u89e3\u3068\u5206\u985e\u304c\u5411\u4e0a\u3057\u3001\u30ab\u30c6\u30b4\u30ea \u30c7\u30fc\u30bf\u3092\u5229\u7528\u3059\u308b\u65b0\u305f\u306a\u53ef\u80fd\u6027\u304c\u958b\u304b\u308c\u307e\u3059\u3002<\/p>\n<h2>\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306e\u4f7f\u7528\u65b9\u6cd5\u3001\u307e\u305f\u306f\u30ab\u30c6\u30b4\u30ea \u30c7\u30fc\u30bf\u3068\u306e\u95a2\u9023\u4ed8\u3051\u65b9\u6cd5<\/h2>\n<p>\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u30c7\u30fc\u30bf\u53ce\u96c6\u3001\u7279\u306b Web \u30b9\u30af\u30ec\u30a4\u30d4\u30f3\u30b0\u3084\u30c7\u30fc\u30bf \u30de\u30a4\u30cb\u30f3\u30b0\u306b\u304a\u3044\u3066\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002\u3055\u307e\u3056\u307e\u306a\u30aa\u30f3\u30e9\u30a4\u30f3 \u30bd\u30fc\u30b9\u304b\u3089\u30ab\u30c6\u30b4\u30ea \u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3059\u308b\u5834\u5408\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u53ce\u96c6\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u306e IP \u30a2\u30c9\u30ec\u30b9\u3092\u30de\u30b9\u30af\u3057\u3001IP \u7981\u6b62\u3092\u9632\u304e\u3001\u30b9\u30e0\u30fc\u30ba\u306a\u30c7\u30fc\u30bf\u53d6\u5f97\u3092\u4fdd\u8a3c\u3067\u304d\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3092\u4f7f\u7528\u3057\u3066\u5730\u57df\u56fa\u6709\u306e Web \u30b5\u30a4\u30c8\u307e\u305f\u306f\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u3001\u30ed\u30fc\u30ab\u30e9\u30a4\u30ba\u3055\u308c\u305f\u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u306e\u53ce\u96c6\u304c\u5bb9\u6613\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<h2>\u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n<p>\u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u3068\u305d\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u8a73\u7d30\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<ol>\n<li><a href=\"https:\/\/www.sagepub.com\/sites\/default\/files\/upm-binaries\/19094_Chapter_1.pdf\" target=\"_new\" rel=\"noopener nofollow\">\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u30c7\u30fc\u30bf\u5206\u6790\u306e\u6982\u8981<\/a><\/li>\n<li><a href=\"https:\/\/www.statisticssolutions.com\/non-parametric-analysis-chi-square\/\" target=\"_new\" rel=\"noopener nofollow\">\u30ab\u30a4\u4e8c\u4e57\u691c\u5b9a\u306e\u8aac\u660e<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/data-visualization-techniques-in-python-8a833956f828\" target=\"_new\" rel=\"noopener nofollow\">\u30c7\u30fc\u30bf\u8996\u899a\u5316\u30c6\u30af\u30cb\u30c3\u30af<\/a><\/li>\n<\/ol>\n<p>\u7d50\u8ad6\u3068\u3057\u3066\u3001\u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u306f\u7d71\u8a08\u3068\u30c7\u30fc\u30bf\u5206\u6790\u306e\u57fa\u672c\u6982\u5ff5\u3067\u3042\u308a\u3001\u975e\u6570\u5024\u60c5\u5831\u306e\u5206\u985e\u3068\u7406\u89e3\u3092\u5bb9\u6613\u306b\u3057\u307e\u3059\u3002\u3055\u307e\u3056\u307e\u306a\u5206\u91ce\u3067\u5e83\u304f\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304b\u3089\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u6709\u610f\u7fa9\u306a\u6d1e\u5bdf\u3092\u5f15\u304d\u51fa\u3059\u4e0a\u3067\u306e\u91cd\u8981\u6027\u304c\u5f37\u8abf\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30c6\u30af\u30ce\u30ed\u30b8\u30fc\u304c\u9032\u5316\u3057\u7d9a\u3051\u308b\u306b\u3064\u308c\u3066\u3001\u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u306e\u5229\u7528\u306f\u610f\u601d\u6c7a\u5b9a\u3084\u4e88\u6e2c\u5206\u6790\u306b\u304a\u3044\u3066\u307e\u3059\u307e\u3059\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3059\u3088\u3046\u306b\u306a\u308b\u3067\u3057\u3087\u3046\u3002\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u5e83\u5927\u306a\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8\u304b\u3089\u306e\u30ab\u30c6\u30b4\u30ea \u30c7\u30fc\u30bf\u306e\u53ce\u96c6\u3068\u51e6\u7406\u306b\u4e0d\u53ef\u6b20\u306a\u30c4\u30fc\u30eb\u3067\u3042\u308a\u7d9a\u3051\u308b\u3067\u3057\u3087\u3046\u3002<\/p>","protected":false},"featured_media":467834,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476185","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Categorical Data: An Encyclopedia Article<\/mark>","faq_items":[{"question":"What is categorical data?","answer":"<p>Categorical data is a type of data that represents distinct groups or categories rather than continuous numerical values. It is commonly used in statistics and data analysis to classify information into qualitative characteristics, such as labels, names, or descriptors.<\/p>"},{"question":"How did categorical data originate?","answer":"<p>The concept of categorical data has its origins in early statistical studies, with Karl Pearson being a key pioneer in its development during the late 19th and early 20th centuries. Over time, it has been extensively utilized in various fields, thanks to the introduction of statistical tests like the chi-squared test.<\/p>"},{"question":"What are the two types of categorical data?","answer":"<p>Categorical data can be divided into two types: nominal data and ordinal data. Nominal data consists of categories with no inherent order, while ordinal data represents categories with a specific order or ranking.<\/p>"},{"question":"How is categorical data represented and analyzed?","answer":"<p>Categorical data is represented using labels or codes to identify each category. In analysis, it is used to perform tasks like frequency distribution, cross-tabulation, and chi-squared tests to explore relationships and associations between variables.<\/p>"},{"question":"What are the main uses of categorical data?","answer":"<p>Categorical data finds extensive applications in market research, social sciences, healthcare, business analytics, and more. It is used for market segmentation, survey analysis, and various other data-driven decision-making processes.<\/p>"},{"question":"What are some common challenges with categorical data?","answer":"<p>Dealing with missing data and low-frequency categories are common challenges with categorical data. Imputation techniques can be used to handle missing values, and merging or separating low-frequency categories can help ensure data integrity.<\/p>"},{"question":"How does the future look for categorical data?","answer":"<p>With advancements in data science and AI, the analysis and utilization of categorical data are expected to continue evolving. Improved algorithms and predictive models will enhance the accuracy of insights drawn from categorical variables.<\/p>"},{"question":"How are proxy servers related to categorical data?","answer":"<p>Proxy servers play a crucial role in collecting categorical data from various online sources, especially in web scraping and data mining. They help mask IP addresses, preventing bans and facilitating the retrieval of region-specific categorical data.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/476185","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/476185\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/467834"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=476185"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}