<?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 Re: What to you anova or regression model in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300985#M16000</link>
    <description>&lt;P&gt;Regression. ANOVA primarily deals with one variable at a time where regression does not and you can consider interaction terms in a regression model.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Make sure you understand the assumptions for your regression model and how categorical values are handled.&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Tue, 27 Sep 2016 10:40:11 GMT</pubDate>
    <dc:creator>Reeza</dc:creator>
    <dc:date>2016-09-27T10:40:11Z</dc:date>
    <item>
      <title>What to use anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300983#M15999</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I have the below data structure.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;TABLE&gt;&lt;TBODY&gt;&lt;TR&gt;&lt;TD&gt;Dress_ID&lt;/TD&gt;&lt;TD&gt;Style&lt;/TD&gt;&lt;TD&gt;Price&lt;/TD&gt;&lt;TD&gt;Rating&lt;/TD&gt;&lt;TD&gt;Size&lt;/TD&gt;&lt;TD&gt;Season&lt;/TD&gt;&lt;TD&gt;NeckLine&lt;/TD&gt;&lt;TD&gt;SleeveLength&lt;/TD&gt;&lt;TD&gt;waiseline&lt;/TD&gt;&lt;TD&gt;Total Sales&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1006032852&lt;/TD&gt;&lt;TD&gt;Sexy&lt;/TD&gt;&lt;TD&gt;Low&lt;/TD&gt;&lt;TD&gt;4.6&lt;/TD&gt;&lt;TD&gt;M&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;sleevless&lt;/TD&gt;&lt;TD&gt;empire&lt;/TD&gt;&lt;TD&gt;2114&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1212192089&lt;/TD&gt;&lt;TD&gt;Casual&lt;/TD&gt;&lt;TD&gt;Low&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;L&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;Petal&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2160&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1190380701&lt;/TD&gt;&lt;TD&gt;vintage&lt;/TD&gt;&lt;TD&gt;High&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;L&lt;/TD&gt;&lt;TD&gt;Automn&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;full&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2206&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;966005983&lt;/TD&gt;&lt;TD&gt;Brief&lt;/TD&gt;&lt;TD&gt;Average&lt;/TD&gt;&lt;TD&gt;4.6&lt;/TD&gt;&lt;TD&gt;L&lt;/TD&gt;&lt;TD&gt;Spring&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;full&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2252&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;876339541&lt;/TD&gt;&lt;TD&gt;cute&lt;/TD&gt;&lt;TD&gt;Low&lt;/TD&gt;&lt;TD&gt;4.5&lt;/TD&gt;&lt;TD&gt;M&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;butterfly&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2298&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1068332458&lt;/TD&gt;&lt;TD&gt;bohemian&lt;/TD&gt;&lt;TD&gt;Low&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;M&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;v-neck&lt;/TD&gt;&lt;TD&gt;sleevless&lt;/TD&gt;&lt;TD&gt;empire&lt;/TD&gt;&lt;TD&gt;2344&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1220707172&lt;/TD&gt;&lt;TD&gt;Casual&lt;/TD&gt;&lt;TD&gt;Average&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;XL&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;full&lt;/TD&gt;&lt;TD&gt;null&lt;/TD&gt;&lt;TD&gt;2390&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1219677488&lt;/TD&gt;&lt;TD&gt;Novelty&lt;/TD&gt;&lt;TD&gt;Average&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;free&lt;/TD&gt;&lt;TD&gt;Automn&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;short&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2436&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1113094204&lt;/TD&gt;&lt;TD&gt;Flare&lt;/TD&gt;&lt;TD&gt;Average&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;free&lt;/TD&gt;&lt;TD&gt;Spring&lt;/TD&gt;&lt;TD&gt;v-neck&lt;/TD&gt;&lt;TD&gt;short&lt;/TD&gt;&lt;TD&gt;empire&lt;/TD&gt;&lt;TD&gt;2482&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;985292672&lt;/TD&gt;&lt;TD&gt;bohemian&lt;/TD&gt;&lt;TD&gt;Low&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;free&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;v-neck&lt;/TD&gt;&lt;TD&gt;sleevless&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2528&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;1117293701&lt;/TD&gt;&lt;TD&gt;party&lt;/TD&gt;&lt;TD&gt;Average&lt;/TD&gt;&lt;TD&gt;5&lt;/TD&gt;&lt;TD&gt;free&lt;/TD&gt;&lt;TD&gt;Summer&lt;/TD&gt;&lt;TD&gt;o-neck&lt;/TD&gt;&lt;TD&gt;full&lt;/TD&gt;&lt;TD&gt;natural&lt;/TD&gt;&lt;TD&gt;2574&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;898481530&lt;/TD&gt;&lt;TD&gt;Flare&lt;/TD&gt;&lt;TD&gt;Average&lt;/TD&gt;&lt;TD&gt;0&lt;/TD&gt;&lt;TD&gt;free&lt;/TD&gt;&lt;TD&gt;Spring&lt;/TD&gt;&lt;TD&gt;v-neck&lt;/TD&gt;&lt;TD&gt;short&lt;/TD&gt;&lt;TD&gt;null&lt;/TD&gt;&lt;TD&gt;&lt;P&gt;2620&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;/TD&gt;&lt;/TR&gt;&lt;/TBODY&gt;&lt;/TABLE&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I have to find out the factors which are effecting the Total Sales variable. Since most of the variables are categorical or rather all are, is using Anova a better option then going for Regression?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Regards,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Aditya&lt;/P&gt;</description>
      <pubDate>Tue, 27 Sep 2016 11:14:28 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300983#M15999</guid>
      <dc:creator>AdityaKir</dc:creator>
      <dc:date>2016-09-27T11:14:28Z</dc:date>
    </item>
    <item>
      <title>Re: What to you anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300985#M16000</link>
      <description>&lt;P&gt;Regression. ANOVA primarily deals with one variable at a time where regression does not and you can consider interaction terms in a regression model.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Make sure you understand the assumptions for your regression model and how categorical values are handled.&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 27 Sep 2016 10:40:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300985#M16000</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2016-09-27T10:40:11Z</dc:date>
    </item>
    <item>
      <title>Re: What to you anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300996#M16002</link>
      <description>&lt;P&gt;I would agree with&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/13879"&gt;@Reeza﻿&lt;/a&gt;, and go with&amp;nbsp;categorical regression using PROC GLM. &amp;nbsp;Hopefully, there is much more data than this, as I see at least 18 different levels across the categorical variables, and only 12 results, so finding a least squares solution may be impossible for this small sample.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Steve Denham&lt;/P&gt;</description>
      <pubDate>Tue, 27 Sep 2016 11:18:58 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/300996#M16002</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2016-09-27T11:18:58Z</dc:date>
    </item>
    <item>
      <title>Re: What to use anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301249#M16019</link>
      <description>&lt;PRE&gt;
I would not agree with Reeza.
Your data did not conform to Normal data,so not suited for ANOVA.But I would try Log-Linear Model.

data have;
infile cards expandtabs truncover;
input Dress_ID :$20.    Style :$20. Price :$20. Rating $ Size :$20.
Season  :$20. NeckLine  :$20. SleeveLength  :$20. waiseline :$20. TotalSales ;
cards;
1006032852  Sexy    Low 4.6 M   Summer  o-neck  sleevless empire   2114
1212192089  Casual  Low 0   L   Summer  o-neck  Petal   natural 2160
1190380701  vintage High    0   L   Automn  o-neck  full    natural 2206
966005983   Brief   Average 4.6 L   Spring  o-neck  full    natural 2252
876339541   cute    Low 4.5 M   Summer  o-neck  butterfly natural 2298
1068332458  bohemian    Low 0   M   Summer  v-neck  sleevless empire 2344
1220707172  Casual  Average 0   XL  Summer  o-neck  full    null 2390
1219677488  Novelty Average 0   free    Automn  o-neck  short   natural 2436
1113094204  Flare   Average 0   free    Spring  v-neck  short   empire  2482
985292672   bohemian    Low 0   free    Summer  v-neck  sleevless natural 2528
1117293701  party   Average 5   free    Summer  o-neck  full    natural 2574
898481530   Flare   Average 0   free    Spring  v-neck  short   null    2620
;
run;
proc catmod data=have;
weight totalsales;
model Dress_ID*Style*Price*Rating*Size*Season*NeckLine*SleeveLength*waiseline
=_response_
/ noparm pred=freq;
loglin Dress_ID Style Price Rating Size Season NeckLine SleeveLength waiseline;
quit;






Your data actually is count data,so you could try Poisson Regression,But I would doubt the result,
Since You have so many levels for each variable, especially for Dress_ID.
Which mean you have N Dress_ID and you have N obs, I could doubt if it could be convergent.

proc genmod data=have;
class Dress_ID Style Price Rating Size Season NeckLine SleeveLength waiseline;
model totalsales= Dress_ID Style Price Rating 
Size Season NeckLine SleeveLength waiseline / dist=poisson link=log;
run;


&lt;/PRE&gt;</description>
      <pubDate>Wed, 28 Sep 2016 13:10:44 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301249#M16019</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2016-09-28T13:10:44Z</dc:date>
    </item>
    <item>
      <title>Re: What to use anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301267#M16021</link>
      <description>&lt;P&gt;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/18408"&gt;@Ksharp﻿&lt;/a&gt;&amp;nbsp;I didn't recommend ANOVA &lt;span class="lia-unicode-emoji" title=":winking_face:"&gt;😉&lt;/span&gt;&lt;/P&gt;</description>
      <pubDate>Wed, 28 Sep 2016 13:37:31 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301267#M16021</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2016-09-28T13:37:31Z</dc:date>
    </item>
    <item>
      <title>Re: What to use anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301281#M16022</link>
      <description>Sure. My bad .</description>
      <pubDate>Wed, 28 Sep 2016 13:52:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301281#M16022</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2016-09-28T13:52:11Z</dc:date>
    </item>
    <item>
      <title>Re: What to use anova or regression model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301459#M16035</link>
      <description>&lt;PRE&gt;
I suggest you remove Dress_ID from model if you don't take it as a influence variable.

&lt;/PRE&gt;</description>
      <pubDate>Thu, 29 Sep 2016 08:10:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/What-to-use-anova-or-regression-model/m-p/301459#M16035</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2016-09-29T08:10:18Z</dc:date>
    </item>
  </channel>
</rss>

