{"id":320,"date":"2023-06-13T13:36:25","date_gmt":"2023-06-13T20:36:25","guid":{"rendered":"https:\/\/live-usc-dornsife.pantheonsite.io\/larry-goldstein\/?page_id=320"},"modified":"2023-06-20T13:02:18","modified_gmt":"2023-06-20T20:02:18","slug":"math-650-statistical-consulting","status":"publish","type":"page","link":"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/","title":{"rendered":"Math 650 &#8211; Statistical Consulting"},"content":{"rendered":"\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <p><big>Statistical Consulting: Use of modern statistical methods for data analysis with R<\/big><br \/>\n<big><strong>Instructor:<\/strong>\u00a0<a href=\"https:\/\/dornsife.usc.edu\/larry-goldstein\/\">Larry Goldstein<\/a>, larry at usc dot edu, KAP 406D, 213 740 2405.\u00a0<\/big><\/p>\n<p><strong>Office Hours:<\/strong>\u00a0MW 10-11<\/p>\n<p><big><strong>Grader:<\/strong>\u00a0<\/big><big>Xiaojing\u00a0<\/big><big>Xing, \u00a0xiaojinx at usc edu,\u00a0 KAP 403, \u00a0213 821-1474<\/big><br \/>\n<big><strong>Lecture:<\/strong>\u00a0 KAP 134, MW 2:00-3:20<\/big><\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Text and Course Coverage<\/h2>\n<p><big><\/big><big><a href=\"http:\/\/www-bcf.usc.edu\/~gareth\/ISL\/data.html\">An Introduction to Statistical Learning<\/a>, James, Witten, Hastie and Tibshirani<br \/>\nTime Permitting, from the text book the course will cover:<\/big><\/p>\n<p><big>Chapter 1: Introduction<br \/>\nChapter 2: Statistical Learning<br \/>\nChapter 4: Classification<br \/>\nChapter 5: Resampling Methods<br \/>\nChapter 6: Linear Model Selection and Regularization<br \/>\nChapter 8: Tree Based Methods<br \/>\nChapter 10: Unsupervised Learning<\/big><\/p>\n<p>Though the main emphasis of the course is on the handling of real data and the use of R, the course will also include various mathematical `interludes&#8217; that explain, justify and broaden the understanding of the basis on which some of the methods introduced rest, in particular for those techniques that may not have been covered in previous core courses.<\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Links<\/h2>\n<p><big>R Resources:<a href=\"http:\/\/cran.r-project.org\/doc\/manuals\/R-intro.html\">\u00a0Introduction to R<\/a>, Another\u00a0<a href=\"http:\/\/cran.r-project.org\/doc\/contrib\/Lam-IntroductionToR_LHL.pdf\">Introduction to R<\/a>,\u00a0\u00a0<a href=\"http:\/\/www.r-project.org\/\">R homepage<\/a>,\u00a0\u00a0<a href=\"http:\/\/www.jeremymiles.co.uk\/regressionbook\/extras\/appendix2\/R\/\">Introductory example<\/a>,\u00a0\u00a0<a href=\"http:\/\/www.r-tutor.com\/\">R-Tutorial<\/a>, and\u00a0<a href=\"http:\/\/research.stowers-institute.org\/efg\/R\/\">R Graphics Gallery<\/a><\/big>,<a href=\"http:\/\/www.clemson.edu\/economics\/faculty\/wilson\/R-tutorial\/analyzing_data.html\">\u00a0<big>Boston Housing Variables<\/big><\/a><\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Exams and Grading Policy<\/h2>\n<p>Grading Policy<\/p>\n<ul>\n<li>30% Homework and in class assignments<\/li>\n<li>30% Midterm exam, Monday Oct 3rd , n=17, median = 378<\/li>\n<li>35% Final Project: each student will pick a consulting topic, prepare a writeup and make a class presentation. The presentation should describe the problem considered, why it is of interest, the data available, and the goals of inference. Then the method of data analysis should be discussed, the results of that analysis, along with the conclusions made and a sense of how reliable those conclusions are. You may include R code written for specifically for the project if you find that it contains some component of interest. There are no preset limits on the length of the writeup, but ballpark it could be from 4-10 pages, without code.<\/li>\n<li>5% Participation in presentations of course final projects.<\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Assignments<\/h2>\n<p><big>Chapter 2 Exercises: \u00a0 Conceptual 1-7, Applied 8-10 \u00a0 \u00a0Additional Exercise #1, Due Sept 7th<br \/>\nChapter 4 Exercises:\u00a0<\/big><big>\u00a0 Conceptual 1-9, Applied 10-13 \u00a0Due Sept 27th<br \/>\nChapter 5 Exercises:\u00a0\u00a0 Conce<\/big><big>ptual 1-4, Applied 5-8\u00a0\u00a0\u00a0\u00a0\u00a0 Due Oct 18th<br \/>\n<\/big><big>Chapter 6 Exercises:\u00a0\u00a0 Conce<\/big><big>ptual 1,3,4,5,6,7, Applied 9,11\u00a0 Due Nov 4th<br \/>\nChapter 8 Excecises:\u00a0\u00a0 Conceptual 1-5, Applied 8,10,11 Due Nov 18th<br \/>\nChapter 10 Exercises: Conceptual 2,4,6, Applied 7,8,9 Due Dec 2nd<\/big><\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <p><big><strong>Project Writeups<\/strong>: Due December 1st.<br \/>\n<\/big><big><br \/>\n<strong>Project Presentations<\/strong><\/big><big><\/p>\n<p><\/big><big>Nov 2:\u00a0 Cong Wu<br \/>\nNov 7:\u00a0 Han Li, Xiaoya Xiong<br \/>\nNov 9:\u00a0 Jie Ren, Zheng Dai<br \/>\nNov 14: Xinrui He, Daoud Burghal<br \/>\nNov 16:\u00a0<\/big><big>Enes Ozel, Moses Wintner<\/big><big><br \/>\nNov 21:\u00a0\u00a0<\/big><big>Xin-Zeng Wu,\u00a0<\/big><big>Mary Same<\/big><big><br \/>\nNov. 28: Ian Thacker, Yanqin Duanmu<br \/>\nNov. 30:\u00a0<\/big><big>Melike Tuysuzoglu<\/big><big>, Nachikethas Jagadeesan<\/big><\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Projects<\/h2>\n<p><big><\/big>As you begin thinking of a potential project, please keep the following items in mind:<\/p>\n<p>1. The overall question or questions you would like to address.<\/p>\n<p>2. What data you will use and where it can be obtained.<\/p>\n<p>3. What specific predictors are available, roughly how many there are, and how large a sample size you will have.<\/p>\n<p>4. What specific response you would like to predict, and what model and methods you will use to predict it.<\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Data Links of Interest<\/h2>\n<ul>\n<li><a href=\"http:\/\/wonder.cdc.gov\/\">CDC On Line Data Bases<\/a><\/li>\n<li><a href=\"http:\/\/wonder.cdc.gov\/\">www.data.gov<\/a><\/li>\n<li><a href=\"http:\/\/sda.berkeley.edu\/\">http:\/\/sda.berkeley.edu\/<\/a><\/li>\n<li><a href=\"http:\/\/archive.ics.uci.edu\/ml\/datasets.html\">http:\/\/archive.ics.uci.edu\/ml\/datasets.html<\/a><\/li>\n<li><a href=\"http:\/\/www.bigdata-startups.com\/public-data\/\">http:\/\/www.bigdata-startups.com\/public-data\/<\/a><\/li>\n<li><a href=\"http:\/\/www.statsci.org\/datasets.html\">http:\/\/www.statsci.org\/datasets.html<\/a><\/li>\n<li><a href=\"http:\/\/www.pro-football-reference.com\/play-index\/play_finder.cgi\">http:\/\/www.pro-football-reference.com\/play-index\/play_finder.cgi<\/a><\/li>\n<li><a href=\"https:\/\/www.kaggle.com\/\">https:\/\/www.kaggle.com\/<\/a><\/li>\n<li><a href=\"https:\/\/www.rita.dot.gov\/bts\/data_and_statistics\/index.html\">https:\/\/www.rita.dot.gov\/bts\/data_and_statistics\/index.html<\/a><\/li>\n<li><a href=\"http:\/\/investexcel.net\/multiple-stock-quote-downloader-for-excel\/\">http:\/\/investexcel.net\/multiple-stock-quote-downloader-for-excel\/<\/a><\/li>\n<\/ul>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n\n\n\n\n  \n    \n\n\n\n\n\n\n<div\n  class=\"cc--component-container cc--rich-text \"\n\n  \n  \n  \n  \n  \n  \n  >\n  <div class=\"c--component c--rich-text\"\n    \n      >\n\n    \n      \n<div class=\"f--field f--wysiwyg\">\n\n    \n  <h2>Important Dates and Information<\/h2>\n<p><big>October 7<sup>th<\/sup>, last day to register and add, or to drop without mark of W<\/big><br \/>\n<big>November 11<sup>th<\/sup>, last day to drop a class with mark of W<br \/>\n<\/big><big>December 2<sup>nd<\/sup>, classes end.<\/big><\/p>\n<p><big><a href=\"http:\/\/classes.usc.edu\/term-20163\/calendar\/\">Full registration calendar<\/a><\/big><br \/>\n<big><a href=\"https:\/\/dornsife.usc.edu\/ase\/statement-on-academic-conduct-and-support-systems\/\">Statement of Academic Conduct<\/a>\u00a0<\/big><\/p>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":370,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-320","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Math 650 - Statistical Consulting - Larry Goldstein<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Math 650 - Statistical Consulting - Larry Goldstein\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/\" \/>\n<meta property=\"og:site_name\" content=\"Larry Goldstein\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-20T20:02:18+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/\",\"url\":\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/\",\"name\":\"Math 650 - Statistical Consulting - Larry Goldstein\",\"isPartOf\":{\"@id\":\"https:\/\/dornsife.usc.edu\/larry-goldstein\/#website\"},\"datePublished\":\"2023-06-13T20:36:25+00:00\",\"dateModified\":\"2023-06-20T20:02:18+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/dornsife.usc.edu\/larry-goldstein\/math-650-statistical-consulting\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/dornsife.usc.edu\/larry-goldstein\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Math 650 &#8211; 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