{"id":125006,"date":"2023-04-08T12:02:57","date_gmt":"2023-04-08T04:02:57","guid":{"rendered":"https:\/\/ocw.nycu.edu.tw\/?post_type=course_page&#038;p=125006"},"modified":"2024-04-16T08:33:12","modified_gmt":"2024-04-16T00:33:12","slug":"regression-analysis-104","status":"publish","type":"course_page","link":"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course\/college-of-science\/institute-of-statistic\/regression-analysis-104","title":{"rendered":"\u8ff4\u6b78\u5206\u6790 Regression Analysi -104\u5b78\u5e74\u5ea6 | \u7d71\u8a08\u5b78\u7814\u7a76\u6240 \u9ec3\u51a0\u83ef\u8001\u5e2b"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"125006\" class=\"elementor elementor-125006\">\n\t\t\t\t\t\t\t\t\t<section class=\"penci-section penci-disSticky penci-structure-10 elementor-section elementor-top-section elementor-element elementor-element-90a0113 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"90a0113\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"penci-ercol-100 penci-ercol-order-1 penci-sticky-ct    elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-78bf394\" data-id=\"78bf394\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9a5e0e8 elementor-widget elementor-widget-breadcrumbs\" data-id=\"9a5e0e8\" data-element_type=\"widget\" data-widget_type=\"breadcrumbs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<link rel=\"stylesheet\" href=\"https:\/\/ocw.nycu.edu.tw\/wp-content\/plugins\/elementor-pro\/assets\/css\/widget-theme-elements.min.css\"><p id=\"breadcrumbs\"><span><span><a href=\"https:\/\/ocw.nycu.edu.tw\/\">Homepage<\/a><\/span><\/span><\/p>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"penci-section penci-disSticky penci-structure-20 elementor-section elementor-top-section elementor-element elementor-element-0025485 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0025485\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"penci-ercol-50 penci-ercol-order-1 penci-sticky-ct    elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0446ed9\" data-id=\"0446ed9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2f5840a elementor-widget elementor-widget-penci-info-box\" data-id=\"2f5840a\" data-element_type=\"widget\" data-widget_type=\"penci-info-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"penci_info_box_95402\" class=\"penci-block-vc penci-info-box penci-ibox-float-left penci-view-default penci-shape-circle\">\r\n\t\t\t<div class=\"penci-ibox-inner\">\r\n\t\t\t\t\t\t\t\t<div class=\"penci-ibox-icon penci-ibox-icon--icon penci-icon penci-tibox-text\"><span class=\"penci-ibox-icon-fa\"><\/span><\/div>\t\t\t\t<div class=\"penci-ibox-content-wrap\">\r\n\t\t\t\t\t<div class=\"penci-ibox-stit\">Regression Analysis  - 104 Academic Year<\/div>\t\t\t\t\t<h3 class=\"penci-ibox-title\">\u8ff4\u6b78\u5206\u6790  -104\u5e74\u5ea6<\/h3>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c6381f4 elementor-widget elementor-widget-eael-adv-tabs\" data-id=\"c6381f4\" data-element_type=\"widget\" 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0v-40c0-6.6-5.4-12-12-12h-40c-6.6 0-12 5.4-12 12v40c0 6.6 5.4 12 12 12h40c6.6 0 12-5.4 12-12zm96-260v352c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V112c0-26.5 21.5-48 48-48h48V12c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v52h128V12c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v52h48c26.5 0 48 21.5 48 48zm-48 346V160H48v298c0 3.3 2.7 6 6 6h340c3.3 0 6-2.7 6-6z\"><\/path><\/svg>                                                            \n                                                            <span class=\"eael-tab-title  title-after-icon\">\u8ab2\u7a0b\u884c\u4e8b\u66c6<\/span>                            \n                                                    <\/li>\n                                    <\/ul>\n            <\/div>\n            \n            <div class=\"eael-tabs-content\">\n\t\t        \n                    <div id=\"home-tab\" class=\"clearfix eael-tab-content-item active-default\" data-title-link=\"home-tab\">\n\t\t\t\t        \t\t\t\t\t        <p>\u00a0<\/p><p style=\"margin-bottom: 10px; line-height: 28px; color: #101010; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4, Arial; font-size: 16px;\">\u672c\u8ab2\u7a0b\u662f\u7531\u00a0<a href=\"https:\/\/stat.nycu.edu.tw\/\" target=\"_blank\" rel=\"noopener\">\u570b\u7acb\u967d\u660e\u4ea4\u901a\u5927\u5b78\u7d71\u8a08\u5b78\u7814\u7a76\u6240<\/a>\u63d0\u4f9b\u3002<\/p><p>The goals of this course are to introduce regression analysis for continuous and discrete data. Topics include simple and multiple linear regressions, inferences for regression coefficients, confounding and interaction, regression diagnostics, logistic regressions, Poisson regressions, and generalized linear models.<\/p><p>The course consists of lectures and laboratory sessions. The lectures are given on Tuesday 9:00-11:00. The lectures will primarily review and reinforce major issues. There is a laboratory session on Tuesday 11:10-12:00. The laboratory exercise will be distributed prior to each class, and students are expected to read each lab exercise at home. Each student will be assigned to a lab group and discuss the exercise with group members in the lab. At the end of the lab, there will be a seminar-type discussion. Each group is required to hand in a write-up of laboratory problems.<\/p><p>The course uses the R software for statistical computing. Students are expected to be familiar with the usage of the software.<\/p><p>\u00a0<\/p><p><span style=\"color: #3366ff; font-size: 16px;\">\u8ab2\u7a0b\u7528\u66f8\uff1a<\/span><\/p><p><span style=\"font-size: 16px;\">Handouts corresponding to each lecture will be available on the course website before each class.<\/span><\/p><p><span style=\"font-size: 16px;\">The required textbooks for this course are \uff1a Montgomery, D.C., Peck, E.A., Vining, G.G. (2012). Introduction to Linear Regression Analysis (5th Edition). Wiley. (ILRA)<\/span><\/p><p><span style=\"color: #ff00ff;\">\u70ba\u6c42\u5b78\u7fd2\u6210\u6548\u5b8c\u7f8e\uff0c\u8acb\u8cfc\u8cb7\u8ab2\u672c\uff01<\/span><\/p><p>\u00a0<\/p><table style=\"color: #101010; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4, Arial; font-size: 16px;\" border=\"1\"><tbody><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\" width=\"100\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"font-weight: 400; white-space: normal;\">\u6388\u8ab2\u6559\u5e2b<\/span><\/span><\/th><td bgcolor=\"#EEEEEE\" width=\"518\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0\u7d71\u8a08\u5b78\u7814\u7a76\u6240 \u9ec3\u51a0\u83ef\u8001\u5e2b<\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"font-weight: 400; white-space: normal;\">\u8ab2\u7a0b\u5b78\u5206<\/span><\/span><\/th><td class=\"text\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a03\u5b78\u5206<\/span><\/span><\/span><\/span><\/td><\/tr><tr><th class=\"text\" style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"font-weight: 400; white-space: normal;\">\u6388\u8ab2\u5e74\u5ea6<\/span><\/span><\/th><td class=\"text\" bgcolor=\"#EEEEEE\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0104\u5b78\u5e74\u5ea6<\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"font-weight: 400; white-space: normal;\">\u6388\u8ab2\u5c0d\u8c61<\/span><\/span><\/th><td class=\"text\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0\u7814\u7a76\u751f<\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"font-weight: 400; white-space: normal;\">\u9810\u5099\u77e5\u8b58<\/span><\/span><\/th><td class=\"text\" bgcolor=\"#EEEEEE\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0Students are expected to have background on undergraduate probability, and mathematical statistics. Computer programming knowledge on R and\/or C\/C++ is required.<\/span><\/span><\/span><\/span><\/td><\/tr><tr><th style=\"text-align: center;\" nowrap=\"nowrap\" bgcolor=\"#FFF3DB\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"font-weight: 400; white-space: normal;\">\u8ab2\u7a0b\u63d0\u4f9b<\/span><\/span><\/th><td class=\"text\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\"><span style=\"color: #313131; font-family: PT Serif, serif;\">\u00a0<a href=\"#video\">\u8ab2\u7a0b\u5f71\u97f3<\/a>\u00a0 \u00a0<a href=\"#syllabus\">\u8ab2\u7a0b\u7db1\u8981<\/a> \u00a0\u00a0<a href=\"#calendar\">\u8ab2\u7a0b\u884c\u4e8b\u66c6<\/a><\/span><\/span><\/span><\/span><\/td><\/tr><\/tbody><\/table>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"video-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"video-tab\">\n\t\t\t\t        \t\t\t\t\t        <p>\n<table id=\"tablepress-573\" class=\"tablepress tablepress-id-573\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\">\u9031\u6b21<\/th><th class=\"column-2\">\u8ab2\u7a0b\u5167\u5bb9<\/th><th class=\"column-3\">\u8ab2\u7a0b\u5f71\u97f3<\/th>\n<\/tr>\n<\/thead>\n<tbody class=\"row-hover\">\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Introduction<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125090\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\">Lecture 1<\/td><td class=\"column-2\">Lecture 1-1: A review of basic statistical concepts<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125109\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lecture 1-2: Measures of association with emphasis on the di\u000berence of means<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125114\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\">Lecture 2<\/td><td class=\"column-2\">Lecture 2: Basics of linear regression analysis<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125119\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 2 \u540c\u5b78\u5831\u544a: \u8b1d\u5ff5\u5ef7 \u9673\u67cf\u9b41 \u8a31\u51f1\u748b \u5e2d\u744b\u8fb0 \u9676\u51a0\u862d \u5289\u51a0\u59a4\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125124\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-7 odd\">\n\t<td class=\"column-1\">Lecture 4<\/td><td class=\"column-2\">Lecture 4: Correlation<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125129\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-8 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 4 \u540c\u5b78\u5831\u544a: \u674e\u6770\u3001\u6797\u7d93\u6ff0\u3001\u9673\u73ee\u6587\u3001\u9673\u5955\u826f\u3001\u5f6d\u6069\u69ae\u3001\u674e\u9a4a\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125134\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-9 odd\">\n\t<td class=\"column-1\">Lecture 5<\/td><td class=\"column-2\">Lecture 5-1: Analysis of variance (ANOVA) table and prediction of y<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125139\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-10 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lecture 5-2: Basics of multiple linear regression<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125145\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-11 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 5 \u540c\u5b78\u5831\u544a: \u77f3\u6615\u79c0\u3001\u674e\u6771\u6069\u3001\u4faf\u6631\u5fb7\u3001\u5289\u5b78\u6c5d\u3001\u674e\u4fca\u660c\u3001\u65b9\u601d\u5a77\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125150\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-12 even\">\n\t<td class=\"column-1\">Lecture 6<\/td><td class=\"column-2\">Lecture 6-1: Hypothesis testing in multiple regression<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125155\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-13 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lecture 6-2: Polynomial terms and dummy variables<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125161\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-14 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 6 \u540c\u5b78\u5831\u544a: \u984f\u5929\u4fdd\u3001\u5289\u53c8\u9f4a\u3001\u85cd\u7389\u670b\u3001\u66fe\u90c1\u7fd4\u3001\u5510\u5fc3\u8aa0\u3001\u6797\u5fd7\u8c6a\u000b\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125166\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-15 odd\">\n\t<td class=\"column-1\">Lecture 7<\/td><td class=\"column-2\">Lecture 7: Interaction and confounding<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125176\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-16 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lecture 7 \u88dc\u5145: Confounding and interaction in epidemiology<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125182\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-17 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 7 \u540c\u5b78\u5831\u544a: \u9ec3\u90c1\u8c6a\u3001\u6881\u601d\u5a55\u3001\u5f35\u767b\u51f1\u3001\u4f55\u6770\u7ff0\u3001\u5468\u4f73\u745c\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125187\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-18 even\">\n\t<td class=\"column-1\">Lecture 8<\/td><td class=\"column-2\">Lecture 8-1: Regression diagnosis<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125192\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-19 odd\">\n\t<td class=\"column-1\">Lecture 8-2: Variable selection and model building<\/td><td class=\"column-2\">Lecture 8-2: Variable selection and model building<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125197\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-20 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 9 \u540c\u5b78\u5831\u544a: \u8a31\u51f1\u748b\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125202\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-21 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 9 \u540c\u5b78\u5831\u544a: \u674e\u6770\u3001\u6797\u7d93\u6ff0\u3001\u9673\u73ee\u6587\u3001\u9673\u5955\u826f\u3001\u5f6d\u6069\u69ae\u3001\u674e\u9a4a\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125207\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-22 even\">\n\t<td class=\"column-1\">Lecture 11<\/td><td class=\"column-2\">Lecture 11: Relative risk, odds ratio and signi\fcance testing for 2\u0002*2 tables<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125212\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-23 odd\">\n\t<td class=\"column-1\">Lecture 12<\/td><td class=\"column-2\">Lecture 12: Introduction to logistic regression<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125217\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-24 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 12 \u540c\u5b78\u5831\u544a: \u77f3\u6615\u79c0\u3001\u674e\u6771\u6069\u3001\u4faf\u6631\u5fb7\u3001\u5289\u5b78\u6c5d\u3001\u674e\u4fca\u660c\u3001\u65b9\u601d\u5a77\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125222\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-25 odd\">\n\t<td class=\"column-1\">Lecture 13<\/td><td class=\"column-2\">Lecture 13-1: Logistic regression for contingency tables<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125227\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-26 even\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lecture 13-2: Goodness-of-\ft for logistic regression<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125232\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-27 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 13 \u540c\u5b78\u5831\u544a: \u984f\u5929\u4fdd\u3001\u5289\u53c8\u9f4a\u3001\u85cd\u7389\u670b\u3001\u66fe\u90c1\u7fd4\u3001\u5510\u5fc3\u8aa0\u3001\u6797\u5fd7\u8c6a\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125237\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-28 even\">\n\t<td class=\"column-1\">Lecture 14<\/td><td class=\"column-2\">Lecture 14: Logistic regression for case-control data and conditional logistic regression<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125242\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-29 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 14 \u540c\u5b78\u5831\u544a: \u4f55\u6770\u7ff0\u3001\u6881\u601d\u5a55\u3001\u5f35\u767b\u51f1\u3001\u5468\u5bb6\u745c\u3001\u9ec3\u90c1\u8c6a\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125247\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-30 even\">\n\t<td class=\"column-1\">Lecture 15<\/td><td class=\"column-2\">Lecture 15: Analysis of polytomous data<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125260\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-31 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 15 \u540c\u5b78\u5831\u544a: \u8b1d\u5ff5\u5ef7 \u9673\u67cf\u9b41 \u8a31\u51f1\u748b \u5e2d\u744b\u8fb0 \u9676\u51a0\u862d \u5289\u51a0\u59a4\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125265\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-32 even\">\n\t<td class=\"column-1\">Lecture 16<\/td><td class=\"column-2\">Lecture 16: Poisson regression and log-linear model<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125270\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-33 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 16 \u540c\u5b78\u5831\u544a: \u674e\u6770\u3001\u6797\u7d93\u6ff0\u3001\u9673\u73ee\u6587\u3001\u9673\u5955\u826f\u3001\u5f6d\u6069\u69ae\u3001\u674e\u9a4a\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125275\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-34 even\">\n\t<td class=\"column-1\">Lecture 17<\/td><td class=\"column-2\">Lecture 17: Generalized linear models<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125280\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<tr class=\"row-35 odd\">\n\t<td class=\"column-1\"><\/td><td class=\"column-2\">Lab 17 \u540c\u5b78\u5831\u544a: \u77f3\u6615\u79c0\u3001\u674e\u6771\u6069\u3001\u4faf\u6631\u5fb7\u3001\u5289\u5b78\u6c5d\u3001\u674e\u4fca\u660c\u3001\u65b9\u601d\u5a77\u540c\u5b78<\/td><td class=\"column-3\"><a href=\"\/?post_type=course_page&amp;p=125285\">\u7dda\u4e0a\u89c0\u770b<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-573 from cache --><\/p>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"syllabus-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"syllabus-tab\">\n\t\t\t\t        \t\t\t\t\t        <p class=\"title\"><strong>\u8ab2\u7a0b\u76ee\u6a19<\/strong><\/p><p class=\"text\">The goals of this course are to introduce regression analysis for continuous and discrete data. Topics include simple and multiple linear regressions, inferences for regression coefficients, confounding and interaction, regression diagnostics, logistic regressions, Poisson regressions, and generalized linear models.<\/p><p>The course consists of lectures and laboratory sessions. The lectures are given on Tuesday 9:00-11:00. The lectures will primarily review and reinforce major issues. There is a laboratory session on Tuesday 11:10-12:00. The laboratory exercise will be distributed prior to each class, and students are expected to read each lab exercise at home. Each student will be assigned to a lab group and discuss the exercise with group members in the lab. At the end of the lab, there will be a seminar-type discussion. Each group is required to hand in a write-up of laboratory problems.<\/p><p>The course uses the R software for statistical computing. Students are expected to be familiar with the usage of the software.<\/p><p>\u00a0<\/p><p class=\"title\"><strong>\u8ab2\u7a0b\u7ae0\u7bc0<\/strong><\/p><table border=\"1\" cellspacing=\"0\"><tbody><tr class=\"stitle\"><td bgcolor=\"#e4d8b0\"><strong> \u55ae\u5143\u4e3b\u984c<\/strong><\/td><td bgcolor=\"#e4d8b0\"><strong> \u5167\u5bb9\u7db1\u8981<\/strong><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">A review of basic statistical concepts<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA APPENDIX C.1, and an introductory statistics book<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Measures of association with emphasis on the difference of means\u3000<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Basics of linear regression analysis\u3000<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 2.1, 2.2, 2.3 except 2.3.3, 2.4, 2.11<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Correlation\u3000<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\"> ILRA 2.6, 2.12.2<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Analysis of variance (ANOVA) table and prediction of y\u3000<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 2.3.3, 2.5<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Basics of multiple linear regression\u3000<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">ILRA 3.1, 3.2<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Hypothesis testing in multiple regression<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\"> ILRA 3.3<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Polynomial terms and dummy variables<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">ILRA 3.10, 7.1, 7.2.1, 7.2.2, 8.1, 8.2<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Interaction and confounding\u3000<\/span><\/td><td style=\"padding: 5px;\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Regression diagnosis<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">ILRA 4.1, 4.2, 4.4, 5.1, 5.2, 5.3, 5.4, 5.5, 6.1, 6.2, 6.3<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Variable selection and model building<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA Chapter 10<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Relative risk, odds ratio and significance testing for 2x2 tables\u3000<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">ILRA 13.2.1, 13.2.2, 13.2.3, 13.2.4<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Introduction to logistic regression\u3000<\/span><\/td><td style=\"padding: 5px;\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Logistic regression for contingency tables<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Goodness-of-t for logistic regression<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 13.2.4, 13.2.5<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Logistic regression of case-control data and conditional logistic regression<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Analysis of polytomous data<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 13.2.7<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\">Generalized linear models<\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span class=\"text\"> ILRA 13.4<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">Poisson regression\u3000<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\"> ILRA 13.3<\/span><\/td><\/tr><\/tbody><\/table><p>\u00a0<\/p><p class=\"title\"><strong>\u8ab2\u7a0b\u66f8\u76ee<\/strong><\/p><p class=\"text\">Handouts corresponding to each lecture will be available on the course website before each class.<br \/>The required textbooks for this course are \uff1a Montgomery, D.C., Peck, E.A., Vining, G.G. (2012). Introduction to Linear Regression Analysis (5th Edition). Wiley. (ILRA)<\/p>\t\t\t\t                            <\/div>\n\t\t        \n                    <div id=\"calendar-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"calendar-tab\">\n\t\t\t\t        \t\t\t\t\t        <p>\u00a0<\/p><p>\u672c\u8ab2\u7a0b\u884c\u4e8b\u66c6\u63d0\u4f9b\u8ab2\u7a0b\u9032\u5ea6\u8207\u8003\u8a66\u8cc7\u8a0a\u3002<\/p><table border=\"1\" cellspacing=\"0\"><tbody><tr class=\"stitle\"><td bgcolor=\"#e4d8b0\"><strong><br \/>\u55ae\u5143<\/strong><\/td><td bgcolor=\"#e4d8b0\"><strong> \u55ae\u5143\u4e3b\u984c<\/strong><\/td><td bgcolor=\"#e4d8b0\"><strong> \u95b1\u8b80\u8cc7\u6599<\/strong><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">1<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\"> A review of basic statistical concepts<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA APPENDIX C.1, and an introductory statistics book<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">2<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Measures of association with emphasis on the difference of means<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">3<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Basics of linear regression analysis<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 2.1, 2.2, 2.3 except 2.3.3, 2.4, 2.11<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">4<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Correlation<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">ILRA 2.6, 2.12.2<\/span><\/span><\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">5<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Analysis of variance (ANOVA) table and prediction of y<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 2.3.3, 2.5<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">6<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Basics of multiple linear regression<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">ILRA 3.1, 3.2<\/span><\/span><\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">7<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Hypothesis testing in multiple regression<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 3.3<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">8<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Polynomial terms and dummy variables<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">ILRA 3.10, 7.1, 7.2.1, 7.2.2, 8.1, 8.2<\/span><\/span><\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">9<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Interaction and confounding\u3000<\/span><\/td><td style=\"padding: 5px;\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">10<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Regression diagnosis<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">ILRA 4.1, 4.2, 4.4, 5.1, 5.2, 5.3, 5.4, 5.5, 6.1, 6.2, 6.3<\/span><\/span><\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">11<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Variable selection and model building<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA Chapter 10<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">12<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Relative risk, odds ratio and significance testing for 2x2 tables<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">ILRA 13.2.1, 13.2.2, 13.2.3, 13.2.4<\/span><\/span><\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">13<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Introduction to logistic regression<\/span><\/td><td style=\"padding: 5px;\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">14<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Logistic regression for contingency tables<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">15<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Goodness-of-t for logistic regression<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 13.2.4, 13.2.5<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">16<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Logistic regression of case-control data and conditional logistic regression<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\">\u00a0<\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">17<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Analysis of polytomous data<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 13.2.7<\/span><\/td><\/tr><tr><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">18<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">Generalized linear models<\/span><\/span><\/span><\/td><td style=\"padding: 5px;\" bgcolor=\"#f2f2ec\"><span style=\"font-family: Liberation Serif, serif;\"><span style=\"font-size: medium;\"><span lang=\"en-US\">ILRA 13.4<\/span><\/span><\/span><\/td><\/tr><tr><td style=\"padding: 5px;\"><span class=\"text\">19<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">Poisson regression<\/span><\/td><td style=\"padding: 5px;\"><span class=\"text\">ILRA 13.3<\/span><\/td><\/tr><\/tbody><\/table>\t\t\t\t                            <\/div>\n\t\t                    <\/div>\n        <\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d7723a8 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"d7723a8\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! 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class=\"elementor-item\" tabindex=\"-1\">\u535a\u96c5\u8b1b\u5ea7<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-123757\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course\/speech-of-general-education-center\" class=\"elementor-item\" tabindex=\"-1\">\u7d93\u5178\u901a\u8b58\u6559\u80b2\u8b1b\u5ea7<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-128238\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course\/reading-of-classic-masterpiece\" class=\"elementor-item\" tabindex=\"-1\">\u7d93\u5178\u540d\u8457\u9078\u8b80<\/a><\/li>\n<li class=\"menu-item menu-item-type-post_type menu-item-object-course_page ajax-mega-menu menu-item-120395\"><a href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course\/general-education\" class=\"elementor-item\" tabindex=\"-1\">\u901a\u8b58\u8ab2\u7a0b<\/a><\/li>\n<\/ul>\t\t\t<\/nav>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Regression Ana&hellip;<\/p>\n","protected":false},"featured_media":125757,"parent":125007,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"footnotes":""},"categories":[215],"tags":[349,353,10161],"class_list":["post-125006","course_page","type-course_page","status-publish","format-standard","has-post-thumbnail","hentry","category-institute-of-statistics","tag-label-all-course-ch","tag-label-college-of-science","tag-label-institute-of-statistics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u8ff4\u6b78\u5206\u6790 Regression Analysi -104\u5b78\u5e74\u5ea6 | \u7d71\u8a08\u5b78\u7814\u7a76\u6240 \u9ec3\u51a0\u83ef\u8001\u5e2b - NYCU OCW<\/title>\n<meta name=\"description\" content=\"Regression Analysis - 104 Academic Year \u8ff4\u6b78\u5206\u6790 -104\u5e74\u5ea6 \u8ab2\u7a0b\u9996\u9801 \u8ab2\u7a0b\u5f71\u97f3 \u8ab2\u7a0b\u7db1\u8981 \u8ab2\u7a0b\u884c\u4e8b\u66c6 \u00a0\u672c\u8ab2\u7a0b\u662f\u7531\u00a0\u570b\u7acb\u967d\u660e\u4ea4\u901a\u5927\u5b78\u7d71\u8a08\u5b78\u7814\u7a76\u6240\u63d0\u4f9b\u3002The goals of this course are to introduce regression\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ocw.nycu.edu.tw\/?course_page=all-course\/college-of-science\/institute-of-statistic\/regression-analysis-104\" \/>\n<meta property=\"og:locale\" content=\"zh_TW\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u8ff4\u6b78\u5206\u6790 Regression Analysi -104\u5b78\u5e74\u5ea6 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