diff --git a/salesplansuccess_illustration.html b/salesplansuccess_illustration.html
index 5a2e527..3d88471 100644
--- a/salesplansuccess_illustration.html
+++ b/salesplansuccess_illustration.html
@@ -14794,325 +14794,325 @@ <h3 id="Looking-at-the-generated-random-historical-sales-dataset">Looking at the
       <th>1</th>
       <td>2018</td>
       <td>2</td>
-      <td>102.71</td>
+      <td>115.14</td>
     </tr>
     <tr>
       <th>2</th>
       <td>2018</td>
       <td>3</td>
-      <td>186.99</td>
+      <td>227.58</td>
     </tr>
     <tr>
       <th>3</th>
       <td>2018</td>
       <td>4</td>
-      <td>130.85</td>
+      <td>163.36</td>
     </tr>
     <tr>
       <th>4</th>
       <td>2018</td>
       <td>5</td>
-      <td>143.76</td>
+      <td>116.96</td>
     </tr>
     <tr>
       <th>5</th>
       <td>2018</td>
       <td>6</td>
-      <td>246.37</td>
+      <td>243.13</td>
     </tr>
     <tr>
       <th>6</th>
       <td>2018</td>
       <td>7</td>
-      <td>176.14</td>
+      <td>180.72</td>
     </tr>
     <tr>
       <th>7</th>
       <td>2018</td>
       <td>8</td>
-      <td>138.16</td>
+      <td>127.50</td>
     </tr>
     <tr>
       <th>8</th>
       <td>2018</td>
       <td>9</td>
-      <td>154.84</td>
+      <td>184.22</td>
     </tr>
     <tr>
       <th>9</th>
       <td>2018</td>
       <td>10</td>
-      <td>214.47</td>
+      <td>167.16</td>
     </tr>
     <tr>
       <th>10</th>
       <td>2018</td>
       <td>11</td>
-      <td>159.25</td>
+      <td>140.04</td>
     </tr>
     <tr>
       <th>11</th>
       <td>2018</td>
       <td>12</td>
-      <td>169.06</td>
+      <td>279.49</td>
     </tr>
     <tr>
       <th>12</th>
       <td>2019</td>
       <td>1</td>
-      <td>270.85</td>
+      <td>216.66</td>
     </tr>
     <tr>
       <th>13</th>
       <td>2019</td>
       <td>2</td>
-      <td>189.69</td>
+      <td>82.31</td>
     </tr>
     <tr>
       <th>14</th>
       <td>2019</td>
       <td>3</td>
-      <td>223.53</td>
+      <td>167.76</td>
     </tr>
     <tr>
       <th>15</th>
       <td>2019</td>
       <td>4</td>
-      <td>393.68</td>
+      <td>197.20</td>
     </tr>
     <tr>
       <th>16</th>
       <td>2019</td>
       <td>5</td>
-      <td>212.55</td>
+      <td>90.92</td>
     </tr>
     <tr>
       <th>17</th>
       <td>2019</td>
       <td>6</td>
-      <td>201.52</td>
+      <td>115.14</td>
     </tr>
     <tr>
       <th>18</th>
       <td>2019</td>
       <td>7</td>
-      <td>217.51</td>
+      <td>198.82</td>
     </tr>
     <tr>
       <th>19</th>
       <td>2019</td>
       <td>8</td>
-      <td>145.41</td>
+      <td>162.54</td>
     </tr>
     <tr>
       <th>20</th>
       <td>2019</td>
       <td>9</td>
-      <td>286.69</td>
+      <td>214.08</td>
     </tr>
     <tr>
       <th>21</th>
       <td>2019</td>
       <td>10</td>
-      <td>283.23</td>
+      <td>133.24</td>
     </tr>
     <tr>
       <th>22</th>
       <td>2019</td>
       <td>11</td>
-      <td>171.14</td>
+      <td>122.07</td>
     </tr>
     <tr>
       <th>23</th>
       <td>2019</td>
       <td>12</td>
-      <td>216.10</td>
+      <td>219.66</td>
     </tr>
     <tr>
       <th>24</th>
       <td>2020</td>
       <td>1</td>
-      <td>258.59</td>
+      <td>246.25</td>
     </tr>
     <tr>
       <th>25</th>
       <td>2020</td>
       <td>2</td>
-      <td>145.10</td>
+      <td>216.03</td>
     </tr>
     <tr>
       <th>26</th>
       <td>2020</td>
       <td>3</td>
-      <td>220.38</td>
+      <td>275.97</td>
     </tr>
     <tr>
       <th>27</th>
       <td>2020</td>
       <td>4</td>
-      <td>176.51</td>
+      <td>212.07</td>
     </tr>
     <tr>
       <th>28</th>
       <td>2020</td>
       <td>5</td>
-      <td>135.30</td>
+      <td>139.40</td>
     </tr>
     <tr>
       <th>29</th>
       <td>2020</td>
       <td>6</td>
-      <td>277.47</td>
+      <td>232.95</td>
     </tr>
     <tr>
       <th>30</th>
       <td>2020</td>
       <td>7</td>
-      <td>201.40</td>
+      <td>174.49</td>
     </tr>
     <tr>
       <th>31</th>
       <td>2020</td>
       <td>8</td>
-      <td>178.54</td>
+      <td>88.90</td>
     </tr>
     <tr>
       <th>32</th>
       <td>2020</td>
       <td>9</td>
-      <td>421.05</td>
+      <td>195.17</td>
     </tr>
     <tr>
       <th>33</th>
       <td>2020</td>
       <td>10</td>
-      <td>327.50</td>
+      <td>243.93</td>
     </tr>
     <tr>
       <th>34</th>
       <td>2020</td>
       <td>11</td>
-      <td>195.60</td>
+      <td>153.38</td>
     </tr>
     <tr>
       <th>35</th>
       <td>2020</td>
       <td>12</td>
-      <td>371.67</td>
+      <td>175.18</td>
     </tr>
     <tr>
       <th>36</th>
       <td>2021</td>
       <td>1</td>
-      <td>314.75</td>
+      <td>172.12</td>
     </tr>
     <tr>
       <th>37</th>
       <td>2021</td>
       <td>2</td>
-      <td>190.89</td>
+      <td>199.29</td>
     </tr>
     <tr>
       <th>38</th>
       <td>2021</td>
       <td>3</td>
-      <td>362.36</td>
+      <td>185.19</td>
     </tr>
     <tr>
       <th>39</th>
       <td>2021</td>
       <td>4</td>
-      <td>206.13</td>
+      <td>177.27</td>
     </tr>
     <tr>
       <th>40</th>
       <td>2021</td>
       <td>5</td>
-      <td>233.89</td>
+      <td>133.62</td>
     </tr>
     <tr>
       <th>41</th>
       <td>2021</td>
       <td>6</td>
-      <td>489.76</td>
+      <td>206.10</td>
     </tr>
     <tr>
       <th>42</th>
       <td>2021</td>
       <td>7</td>
-      <td>293.54</td>
+      <td>196.62</td>
     </tr>
     <tr>
       <th>43</th>
       <td>2021</td>
       <td>8</td>
-      <td>317.80</td>
+      <td>148.46</td>
     </tr>
     <tr>
       <th>44</th>
       <td>2021</td>
       <td>9</td>
-      <td>448.61</td>
+      <td>229.90</td>
     </tr>
     <tr>
       <th>45</th>
       <td>2021</td>
       <td>10</td>
-      <td>364.85</td>
+      <td>307.39</td>
     </tr>
     <tr>
       <th>46</th>
       <td>2021</td>
       <td>11</td>
-      <td>231.16</td>
+      <td>271.22</td>
     </tr>
     <tr>
       <th>47</th>
       <td>2021</td>
       <td>12</td>
-      <td>304.58</td>
+      <td>315.43</td>
     </tr>
     <tr>
       <th>48</th>
       <td>2022</td>
       <td>1</td>
-      <td>375.68</td>
+      <td>360.99</td>
     </tr>
     <tr>
       <th>49</th>
       <td>2022</td>
       <td>2</td>
-      <td>197.73</td>
+      <td>206.22</td>
     </tr>
     <tr>
       <th>50</th>
       <td>2022</td>
       <td>3</td>
-      <td>370.95</td>
+      <td>252.66</td>
     </tr>
     <tr>
       <th>51</th>
       <td>2022</td>
       <td>4</td>
-      <td>492.67</td>
+      <td>293.18</td>
     </tr>
     <tr>
       <th>52</th>
       <td>2022</td>
       <td>5</td>
-      <td>237.94</td>
+      <td>194.02</td>
     </tr>
     <tr>
       <th>53</th>
       <td>2022</td>
       <td>6</td>
-      <td>409.81</td>
+      <td>309.28</td>
     </tr>
     <tr>
       <th>54</th>
       <td>2022</td>
       <td>7</td>
-      <td>392.62</td>
+      <td>324.19</td>
     </tr>
   </tbody>
 </table>
@@ -15144,7 +15144,7 @@ <h3 id="Creating-an-object-of-the-class-SalesPlanSuccess,-feeding-into-it-the-hi
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sps</span> <span class="o">=</span> <span class="n">SalesPlanSuccess</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">historical_data</span><span class="p">,</span> <span class="n">plan</span> <span class="o">=</span> <span class="mi">4700</span><span class="p">)</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sps</span> <span class="o">=</span> <span class="n">SalesPlanSuccess</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">historical_data</span><span class="p">,</span> <span class="n">plan</span> <span class="o">=</span> <span class="mi">3800</span><span class="p">,</span> <span class="n">product</span> <span class="o">=</span> <span class="s1">&#39;superbumab&#39;</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -15171,7 +15171,7 @@ <h3 id="Processing-the-fed-dataset-and-estimating-the-parameters-of-the-ARIMA(2,
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sps</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sps</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;LSE&#39;</span><span class="p">)</span>
 </pre></div>
 
      </div>
@@ -15198,7 +15198,7 @@ <h3 id="Looking-at-the-estimates-of-the-parameters-of-the-ARIMA(2,1,0)-model-fro
 <div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sps</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sps</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
 </pre></div>
 
      </div>
@@ -15216,71 +15216,18 @@ <h3 id="Looking-at-the-estimates-of-the-parameters-of-the-ARIMA(2,1,0)-model-fro
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
-
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 
 
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
-<table class="simpletable">
-<caption>SARIMAX Results</caption>
-<tr>
-  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>54</td>  
-</tr>
-<tr>
-  <th>Model:</th>            <td>ARIMA(2, 0, 0)</td>  <th>  Log Likelihood     </th>  <td>3.684</td>
-</tr>
-<tr>
-  <th>Date:</th>            <td>Sun, 04 Sep 2022</td> <th>  AIC                </th>  <td>2.631</td>
-</tr>
-<tr>
-  <th>Time:</th>                <td>21:06:14</td>     <th>  BIC                </th> <td>12.576</td>
-</tr>
-<tr>
-  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>6.467</td>
-</tr>
-<tr>
-  <th></th>                       <td> - 54</td>      <th>                     </th>    <td> </td>  
-</tr>
-<tr>
-  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>  
-</tr>
-</table>
-<table class="simpletable">
-<tr>
-     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  
-</tr>
-<tr>
-  <th>const</th>  <td>   -0.1970</td> <td>    0.063</td> <td>   -3.108</td> <td> 0.002</td> <td>   -0.321</td> <td>   -0.073</td>
-</tr>
-<tr>
-  <th>x1</th>     <td>    0.6520</td> <td>    0.175</td> <td>    3.717</td> <td> 0.000</td> <td>    0.308</td> <td>    0.996</td>
-</tr>
-<tr>
-  <th>ar.L1</th>  <td>   -0.6925</td> <td>    0.126</td> <td>   -5.490</td> <td> 0.000</td> <td>   -0.940</td> <td>   -0.445</td>
-</tr>
-<tr>
-  <th>ar.L2</th>  <td>   -0.5691</td> <td>    0.122</td> <td>   -4.660</td> <td> 0.000</td> <td>   -0.808</td> <td>   -0.330</td>
-</tr>
-<tr>
-  <th>sigma2</th> <td>    0.0501</td> <td>    0.013</td> <td>    3.964</td> <td> 0.000</td> <td>    0.025</td> <td>    0.075</td>
-</tr>
-</table>
-<table class="simpletable">
-<tr>
-  <th>Ljung-Box (L1) (Q):</th>     <td>0.95</td> <th>  Jarque-Bera (JB):  </th> <td>1.55</td> 
-</tr>
-<tr>
-  <th>Prob(Q):</th>                <td>0.33</td> <th>  Prob(JB):          </th> <td>0.46</td> 
-</tr>
-<tr>
-  <th>Heteroskedasticity (H):</th> <td>0.80</td> <th>  Skew:              </th> <td>-0.15</td>
-</tr>
-<tr>
-  <th>Prob(H) (two-sided):</th>    <td>0.65</td> <th>  Kurtosis:          </th> <td>2.22</td> 
-</tr>
-</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>	  Coefficient estimates in LSE
+Monthly drift:				-0.136
+End of quarter:				 0.466
+AR1:					-0.165
+AR2:					-0.538
+Standard deviation of residuals:	 0.209
+</pre>
 </div>
-
 </div>
 
 </div>
@@ -15357,7 +15304,7 @@ <h3 id="On-the-plot-below,-the-X-axis-shows-the-expected-annual-sales-vs.-the-pl
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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
 "
 class="
 jp-needs-light-background
diff --git a/salesplansuccess_illustration.ipynb b/salesplansuccess_illustration.ipynb
index e577fa5..1e32209 100644
--- a/salesplansuccess_illustration.ipynb
+++ b/salesplansuccess_illustration.ipynb
@@ -149,325 +149,325 @@
        "      <th>1</th>\n",
        "      <td>2018</td>\n",
        "      <td>2</td>\n",
-       "      <td>102.71</td>\n",
+       "      <td>115.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2018</td>\n",
        "      <td>3</td>\n",
-       "      <td>186.99</td>\n",
+       "      <td>227.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2018</td>\n",
        "      <td>4</td>\n",
-       "      <td>130.85</td>\n",
+       "      <td>163.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2018</td>\n",
        "      <td>5</td>\n",
-       "      <td>143.76</td>\n",
+       "      <td>116.96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>2018</td>\n",
        "      <td>6</td>\n",
-       "      <td>246.37</td>\n",
+       "      <td>243.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>2018</td>\n",
        "      <td>7</td>\n",
-       "      <td>176.14</td>\n",
+       "      <td>180.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>2018</td>\n",
        "      <td>8</td>\n",
-       "      <td>138.16</td>\n",
+       "      <td>127.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>2018</td>\n",
        "      <td>9</td>\n",
-       "      <td>154.84</td>\n",
+       "      <td>184.22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>2018</td>\n",
        "      <td>10</td>\n",
-       "      <td>214.47</td>\n",
+       "      <td>167.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>2018</td>\n",
        "      <td>11</td>\n",
-       "      <td>159.25</td>\n",
+       "      <td>140.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>2018</td>\n",
        "      <td>12</td>\n",
-       "      <td>169.06</td>\n",
+       "      <td>279.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>2019</td>\n",
        "      <td>1</td>\n",
-       "      <td>270.85</td>\n",
+       "      <td>216.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>2019</td>\n",
        "      <td>2</td>\n",
-       "      <td>189.69</td>\n",
+       "      <td>82.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>2019</td>\n",
        "      <td>3</td>\n",
-       "      <td>223.53</td>\n",
+       "      <td>167.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>2019</td>\n",
        "      <td>4</td>\n",
-       "      <td>393.68</td>\n",
+       "      <td>197.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>2019</td>\n",
        "      <td>5</td>\n",
-       "      <td>212.55</td>\n",
+       "      <td>90.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>2019</td>\n",
        "      <td>6</td>\n",
-       "      <td>201.52</td>\n",
+       "      <td>115.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>2019</td>\n",
        "      <td>7</td>\n",
-       "      <td>217.51</td>\n",
+       "      <td>198.82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>2019</td>\n",
        "      <td>8</td>\n",
-       "      <td>145.41</td>\n",
+       "      <td>162.54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>2019</td>\n",
        "      <td>9</td>\n",
-       "      <td>286.69</td>\n",
+       "      <td>214.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>2019</td>\n",
        "      <td>10</td>\n",
-       "      <td>283.23</td>\n",
+       "      <td>133.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>2019</td>\n",
        "      <td>11</td>\n",
-       "      <td>171.14</td>\n",
+       "      <td>122.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>2019</td>\n",
        "      <td>12</td>\n",
-       "      <td>216.10</td>\n",
+       "      <td>219.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>2020</td>\n",
        "      <td>1</td>\n",
-       "      <td>258.59</td>\n",
+       "      <td>246.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>2020</td>\n",
        "      <td>2</td>\n",
-       "      <td>145.10</td>\n",
+       "      <td>216.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>2020</td>\n",
        "      <td>3</td>\n",
-       "      <td>220.38</td>\n",
+       "      <td>275.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>2020</td>\n",
        "      <td>4</td>\n",
-       "      <td>176.51</td>\n",
+       "      <td>212.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>2020</td>\n",
        "      <td>5</td>\n",
-       "      <td>135.30</td>\n",
+       "      <td>139.40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>2020</td>\n",
        "      <td>6</td>\n",
-       "      <td>277.47</td>\n",
+       "      <td>232.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>2020</td>\n",
        "      <td>7</td>\n",
-       "      <td>201.40</td>\n",
+       "      <td>174.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>2020</td>\n",
        "      <td>8</td>\n",
-       "      <td>178.54</td>\n",
+       "      <td>88.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>2020</td>\n",
        "      <td>9</td>\n",
-       "      <td>421.05</td>\n",
+       "      <td>195.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>2020</td>\n",
        "      <td>10</td>\n",
-       "      <td>327.50</td>\n",
+       "      <td>243.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>2020</td>\n",
        "      <td>11</td>\n",
-       "      <td>195.60</td>\n",
+       "      <td>153.38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>2020</td>\n",
        "      <td>12</td>\n",
-       "      <td>371.67</td>\n",
+       "      <td>175.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>2021</td>\n",
        "      <td>1</td>\n",
-       "      <td>314.75</td>\n",
+       "      <td>172.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>2021</td>\n",
        "      <td>2</td>\n",
-       "      <td>190.89</td>\n",
+       "      <td>199.29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>2021</td>\n",
        "      <td>3</td>\n",
-       "      <td>362.36</td>\n",
+       "      <td>185.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>2021</td>\n",
        "      <td>4</td>\n",
-       "      <td>206.13</td>\n",
+       "      <td>177.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>2021</td>\n",
        "      <td>5</td>\n",
-       "      <td>233.89</td>\n",
+       "      <td>133.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>2021</td>\n",
        "      <td>6</td>\n",
-       "      <td>489.76</td>\n",
+       "      <td>206.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td>2021</td>\n",
        "      <td>7</td>\n",
-       "      <td>293.54</td>\n",
+       "      <td>196.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>2021</td>\n",
        "      <td>8</td>\n",
-       "      <td>317.80</td>\n",
+       "      <td>148.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>2021</td>\n",
        "      <td>9</td>\n",
-       "      <td>448.61</td>\n",
+       "      <td>229.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>2021</td>\n",
        "      <td>10</td>\n",
-       "      <td>364.85</td>\n",
+       "      <td>307.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>2021</td>\n",
        "      <td>11</td>\n",
-       "      <td>231.16</td>\n",
+       "      <td>271.22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>2021</td>\n",
        "      <td>12</td>\n",
-       "      <td>304.58</td>\n",
+       "      <td>315.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>2022</td>\n",
        "      <td>1</td>\n",
-       "      <td>375.68</td>\n",
+       "      <td>360.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>2022</td>\n",
        "      <td>2</td>\n",
-       "      <td>197.73</td>\n",
+       "      <td>206.22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>2022</td>\n",
        "      <td>3</td>\n",
-       "      <td>370.95</td>\n",
+       "      <td>252.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>2022</td>\n",
        "      <td>4</td>\n",
-       "      <td>492.67</td>\n",
+       "      <td>293.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>2022</td>\n",
        "      <td>5</td>\n",
-       "      <td>237.94</td>\n",
+       "      <td>194.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>2022</td>\n",
        "      <td>6</td>\n",
-       "      <td>409.81</td>\n",
+       "      <td>309.28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td>2022</td>\n",
        "      <td>7</td>\n",
-       "      <td>392.62</td>\n",
+       "      <td>324.19</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -476,60 +476,60 @@
       "text/plain": [
        "    Year  Month   Sales\n",
        "0   2018      1  100.00\n",
-       "1   2018      2  102.71\n",
-       "2   2018      3  186.99\n",
-       "3   2018      4  130.85\n",
-       "4   2018      5  143.76\n",
-       "5   2018      6  246.37\n",
-       "6   2018      7  176.14\n",
-       "7   2018      8  138.16\n",
-       "8   2018      9  154.84\n",
-       "9   2018     10  214.47\n",
-       "10  2018     11  159.25\n",
-       "11  2018     12  169.06\n",
-       "12  2019      1  270.85\n",
-       "13  2019      2  189.69\n",
-       "14  2019      3  223.53\n",
-       "15  2019      4  393.68\n",
-       "16  2019      5  212.55\n",
-       "17  2019      6  201.52\n",
-       "18  2019      7  217.51\n",
-       "19  2019      8  145.41\n",
-       "20  2019      9  286.69\n",
-       "21  2019     10  283.23\n",
-       "22  2019     11  171.14\n",
-       "23  2019     12  216.10\n",
-       "24  2020      1  258.59\n",
-       "25  2020      2  145.10\n",
-       "26  2020      3  220.38\n",
-       "27  2020      4  176.51\n",
-       "28  2020      5  135.30\n",
-       "29  2020      6  277.47\n",
-       "30  2020      7  201.40\n",
-       "31  2020      8  178.54\n",
-       "32  2020      9  421.05\n",
-       "33  2020     10  327.50\n",
-       "34  2020     11  195.60\n",
-       "35  2020     12  371.67\n",
-       "36  2021      1  314.75\n",
-       "37  2021      2  190.89\n",
-       "38  2021      3  362.36\n",
-       "39  2021      4  206.13\n",
-       "40  2021      5  233.89\n",
-       "41  2021      6  489.76\n",
-       "42  2021      7  293.54\n",
-       "43  2021      8  317.80\n",
-       "44  2021      9  448.61\n",
-       "45  2021     10  364.85\n",
-       "46  2021     11  231.16\n",
-       "47  2021     12  304.58\n",
-       "48  2022      1  375.68\n",
-       "49  2022      2  197.73\n",
-       "50  2022      3  370.95\n",
-       "51  2022      4  492.67\n",
-       "52  2022      5  237.94\n",
-       "53  2022      6  409.81\n",
-       "54  2022      7  392.62"
+       "1   2018      2  115.14\n",
+       "2   2018      3  227.58\n",
+       "3   2018      4  163.36\n",
+       "4   2018      5  116.96\n",
+       "5   2018      6  243.13\n",
+       "6   2018      7  180.72\n",
+       "7   2018      8  127.50\n",
+       "8   2018      9  184.22\n",
+       "9   2018     10  167.16\n",
+       "10  2018     11  140.04\n",
+       "11  2018     12  279.49\n",
+       "12  2019      1  216.66\n",
+       "13  2019      2   82.31\n",
+       "14  2019      3  167.76\n",
+       "15  2019      4  197.20\n",
+       "16  2019      5   90.92\n",
+       "17  2019      6  115.14\n",
+       "18  2019      7  198.82\n",
+       "19  2019      8  162.54\n",
+       "20  2019      9  214.08\n",
+       "21  2019     10  133.24\n",
+       "22  2019     11  122.07\n",
+       "23  2019     12  219.66\n",
+       "24  2020      1  246.25\n",
+       "25  2020      2  216.03\n",
+       "26  2020      3  275.97\n",
+       "27  2020      4  212.07\n",
+       "28  2020      5  139.40\n",
+       "29  2020      6  232.95\n",
+       "30  2020      7  174.49\n",
+       "31  2020      8   88.90\n",
+       "32  2020      9  195.17\n",
+       "33  2020     10  243.93\n",
+       "34  2020     11  153.38\n",
+       "35  2020     12  175.18\n",
+       "36  2021      1  172.12\n",
+       "37  2021      2  199.29\n",
+       "38  2021      3  185.19\n",
+       "39  2021      4  177.27\n",
+       "40  2021      5  133.62\n",
+       "41  2021      6  206.10\n",
+       "42  2021      7  196.62\n",
+       "43  2021      8  148.46\n",
+       "44  2021      9  229.90\n",
+       "45  2021     10  307.39\n",
+       "46  2021     11  271.22\n",
+       "47  2021     12  315.43\n",
+       "48  2022      1  360.99\n",
+       "49  2022      2  206.22\n",
+       "50  2022      3  252.66\n",
+       "51  2022      4  293.18\n",
+       "52  2022      5  194.02\n",
+       "53  2022      6  309.28\n",
+       "54  2022      7  324.19"
       ]
      },
      "execution_count": 7,
@@ -556,7 +556,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sps = SalesPlanSuccess(data = historical_data, plan = 4700)"
+    "sps = SalesPlanSuccess(data = historical_data, plan = 3800, product = 'superbumab')"
    ]
   },
   {
@@ -574,7 +574,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sps.fit()"
+    "sps.fit(mode='LSE')"
    ]
   },
   {
@@ -592,106 +592,20 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<table class=\"simpletable\">\n",
-       "<caption>SARIMAX Results</caption>\n",
-       "<tr>\n",
-       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>54</td>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Model:</th>            <td>ARIMA(2, 0, 0)</td>  <th>  Log Likelihood     </th>  <td>3.684</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Date:</th>            <td>Sun, 04 Sep 2022</td> <th>  AIC                </th>  <td>2.631</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Time:</th>                <td>21:06:14</td>     <th>  BIC                </th> <td>12.576</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>6.467</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th></th>                       <td> - 54</td>      <th>                     </th>    <td> </td>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>  \n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>const</th>  <td>   -0.1970</td> <td>    0.063</td> <td>   -3.108</td> <td> 0.002</td> <td>   -0.321</td> <td>   -0.073</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>x1</th>     <td>    0.6520</td> <td>    0.175</td> <td>    3.717</td> <td> 0.000</td> <td>    0.308</td> <td>    0.996</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L1</th>  <td>   -0.6925</td> <td>    0.126</td> <td>   -5.490</td> <td> 0.000</td> <td>   -0.940</td> <td>   -0.445</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L2</th>  <td>   -0.5691</td> <td>    0.122</td> <td>   -4.660</td> <td> 0.000</td> <td>   -0.808</td> <td>   -0.330</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>sigma2</th> <td>    0.0501</td> <td>    0.013</td> <td>    3.964</td> <td> 0.000</td> <td>    0.025</td> <td>    0.075</td>\n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.95</td> <th>  Jarque-Bera (JB):  </th> <td>1.55</td> \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Prob(Q):</th>                <td>0.33</td> <th>  Prob(JB):          </th> <td>0.46</td> \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Heteroskedasticity (H):</th> <td>0.80</td> <th>  Skew:              </th> <td>-0.15</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Prob(H) (two-sided):</th>    <td>0.65</td> <th>  Kurtosis:          </th> <td>2.22</td> \n",
-       "</tr>\n",
-       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
-      ],
-      "text/plain": [
-       "<class 'statsmodels.iolib.summary.Summary'>\n",
-       "\"\"\"\n",
-       "                               SARIMAX Results                                \n",
-       "==============================================================================\n",
-       "Dep. Variable:                      y   No. Observations:                   54\n",
-       "Model:                 ARIMA(2, 0, 0)   Log Likelihood                   3.684\n",
-       "Date:                Sun, 04 Sep 2022   AIC                              2.631\n",
-       "Time:                        21:06:14   BIC                             12.576\n",
-       "Sample:                             0   HQIC                             6.467\n",
-       "                                 - 54                                         \n",
-       "Covariance Type:                  opg                                         \n",
-       "==============================================================================\n",
-       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "------------------------------------------------------------------------------\n",
-       "const         -0.1970      0.063     -3.108      0.002      -0.321      -0.073\n",
-       "x1             0.6520      0.175      3.717      0.000       0.308       0.996\n",
-       "ar.L1         -0.6925      0.126     -5.490      0.000      -0.940      -0.445\n",
-       "ar.L2         -0.5691      0.122     -4.660      0.000      -0.808      -0.330\n",
-       "sigma2         0.0501      0.013      3.964      0.000       0.025       0.075\n",
-       "===================================================================================\n",
-       "Ljung-Box (L1) (Q):                   0.95   Jarque-Bera (JB):                 1.55\n",
-       "Prob(Q):                              0.33   Prob(JB):                         0.46\n",
-       "Heteroskedasticity (H):               0.80   Skew:                            -0.15\n",
-       "Prob(H) (two-sided):                  0.65   Kurtosis:                         2.22\n",
-       "===================================================================================\n",
-       "\n",
-       "Warnings:\n",
-       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t  Coefficient estimates in LSE\n",
+      "Monthly drift:\t\t\t\t-0.136\n",
+      "End of quarter:\t\t\t\t 0.466\n",
+      "AR1:\t\t\t\t\t-0.165\n",
+      "AR2:\t\t\t\t\t-0.538\n",
+      "Standard deviation of residuals:\t 0.209\n"
+     ]
     }
    ],
    "source": [
-    "sps.model_fit.summary()"
+    "sps.summary()"
    ]
   },
   {
@@ -729,7 +643,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]