<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Help with Linear Regression in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Help-with-Linear-Regression/m-p/965567#M48469</link>
    <description>&lt;P&gt;I am trying to work on linear regression models and find myself getting stuck.&amp;nbsp; Can anyone here point me in the&amp;nbsp; direction&amp;nbsp; of some references that I can use if I get hung up?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;These are the models I am trying to work on&amp;nbsp; Thanks.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;First: Unadjusted simple logistic regression (just exposure and outcome)&lt;BR /&gt;1. Fully adjusted multinomial logistic regression (including covariates in the model)&lt;BR /&gt;2. Fully adjusted multinomial logistic regression stratified by gender&lt;BR /&gt;a. Fully adjusted multinominal logistic regression stratified by region&lt;BR /&gt;3. Fully adjusted multinomial logistic regression with product term between&lt;BR /&gt;exercise and diabetes diagnosis (assessing EMM by diabetes)&lt;/P&gt;</description>
    <pubDate>Thu, 01 May 2025 23:43:49 GMT</pubDate>
    <dc:creator>JMK4200</dc:creator>
    <dc:date>2025-05-01T23:43:49Z</dc:date>
    <item>
      <title>Help with Linear Regression</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Help-with-Linear-Regression/m-p/965567#M48469</link>
      <description>&lt;P&gt;I am trying to work on linear regression models and find myself getting stuck.&amp;nbsp; Can anyone here point me in the&amp;nbsp; direction&amp;nbsp; of some references that I can use if I get hung up?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;These are the models I am trying to work on&amp;nbsp; Thanks.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;First: Unadjusted simple logistic regression (just exposure and outcome)&lt;BR /&gt;1. Fully adjusted multinomial logistic regression (including covariates in the model)&lt;BR /&gt;2. Fully adjusted multinomial logistic regression stratified by gender&lt;BR /&gt;a. Fully adjusted multinominal logistic regression stratified by region&lt;BR /&gt;3. Fully adjusted multinomial logistic regression with product term between&lt;BR /&gt;exercise and diabetes diagnosis (assessing EMM by diabetes)&lt;/P&gt;</description>
      <pubDate>Thu, 01 May 2025 23:43:49 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Help-with-Linear-Regression/m-p/965567#M48469</guid>
      <dc:creator>JMK4200</dc:creator>
      <dc:date>2025-05-01T23:43:49Z</dc:date>
    </item>
    <item>
      <title>Re: Help with Linear Regression</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Help-with-Linear-Regression/m-p/965582#M48470</link>
      <description>&lt;P&gt;First of all, all of your questions concern logistic regression, so it is better to phrase your problem as "Help with logistic regression".&lt;BR /&gt;Second, I am not sure if you wish to solve your problem with SAS or not. If the answer is "yes", then both &lt;A href="https://www.amazon.com/Logistic-Regression-Using-SAS-Application/dp/1635269091/ref=tmm_hrd_swatch_0?_encoding=UTF8&amp;amp;dib_tag=se&amp;amp;dib=eyJ2IjoiMSJ9.9H2w_d9LXshz2Kb9IljFqCTam9ScVWX_w84TCoqBDb2TaLV_lZmIbEVAmJPOylcIKkepFRc8ixZ9-lXP_VnfZPs33hv63zxC3Y5qEh6_EdiqlzIcbGMhqP9U0vdkU5uTtyKWJ1i_br2Jfb1kABjqv6_Ucf4gNI2lLdp59DBaWNlD-3tm8hTtVeApLGBytmQ5QI-ZehRcIqrQcGnMRkQJ7aaeoZU8GYc8lyfObLrrutOPOEWnx84gUEOMFd19VfJeU98SS11FPdWmrvcr4jvhY-XIZ6ABq7SLuaf_4wYkdNQ.MjqbsTQ805QCoU9EDxwsWy5uuHON0KKs_Q-tk66b9bo&amp;amp;qid=1746168938&amp;amp;sr=8-8&amp;amp;asin=1599946416&amp;amp;revisionId=&amp;amp;format=4&amp;amp;depth=1" target="_blank" rel="noopener"&gt;Amazon.com: Logistic Regression Using SAS: Theory and Application, Second Edition&lt;/A&gt;&amp;nbsp;and&amp;nbsp;&lt;A href="https://www.wiley.com/en-us/Categorical+Data+Analysis%2C+3rd+Edition-p-9781118710944" target="_blank" rel="noopener"&gt;Categorical Data Analysis, 3rd Edition | Wiley&lt;/A&gt;&amp;nbsp;are good choices. These books cover your questions and provide examples of building binary and multinomial logistic regression models in SAS. Other topics on categorical data analysis are also discussed in the duo. In addition, if you can understand Chinese, then &lt;A href="https://item.xhsd.com/items/1010000103132022" target="_blank" rel="noopener"&gt;《医学研究中的logistic回归分析及SAS实现》【正版图书 折扣 优惠 详情 书评 试读】 - 新华书店网上商城&lt;/A&gt;&amp;nbsp;is another book I would like to recommend. This book is a monograph specifically devoted to building logistic regression models in SAS, touching on topics not covered by the former two English books like building logistic regression models for complex survey data.&lt;/P&gt;
&lt;P&gt;On the other hand, if you only wish to learn something on logistic regression and may not build models with SAS, then I recommend&amp;nbsp;&lt;A href="https://www.amazon.com/Logistic-Regression-Chapman-Statistical-Science-ebook/dp/B00OD4OG90/ref=tmm_kin_swatch_0?_encoding=UTF8&amp;amp;dib_tag=se&amp;amp;dib=eyJ2IjoiMSJ9.zyLt6qHUBtMkFR3YDtDUjSxHrvh9WdHpuOjR1s-TYGgvsSSBm8i6rUL6DNqICHtFFhgp43ZjU0ofShT4-pEAutD-wvpmwZ58XTfvIFcI_Us8vXNKkR_ec88HNU6M-iBmzHJZ9DxfVu9YyTF3X-XeUsz_xBkSyMzjyyOjzooLl0qROqEE8nAlk2VLOZKV5k5o.xsT5H1_G7zCvsEN3kUZe3839KMJKYKV4ZfCkuHdWwcQ&amp;amp;qid=1746169800&amp;amp;sr=8-2" target="_blank" rel="noopener"&gt;Logistic Regression Models (Chapman &amp;amp; Hall/CRC Texts in Statistical Science) 1, Hilbe, Joseph M. - Amazon.com&lt;/A&gt;. This book is yet another monograph on logistic regression. What makes this book different from the aforementioned trio is that the last one primarily uses Stata and discusses more theoretical intricacies of logistic regression like the Fisher scoring method, an algorithm used in estimating the parameters in logistic regression.&lt;/P&gt;</description>
      <pubDate>Fri, 02 May 2025 07:23:21 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Help-with-Linear-Regression/m-p/965582#M48470</guid>
      <dc:creator>Season</dc:creator>
      <dc:date>2025-05-02T07:23:21Z</dc:date>
    </item>
  </channel>
</rss>

