diff --git a/paper/.~lock.weGotThePowerDraftPaper.html# b/paper/.~lock.weGotThePowerDraftPaper.html#
index 045d4752318981bcc5e642447272d0850a8de33e..da8be80d4b4c7c8f9241ce31fde47fdd30adc66f 100644
--- a/paper/.~lock.weGotThePowerDraftPaper.html#
+++ b/paper/.~lock.weGotThePowerDraftPaper.html#
@@ -1 +1 @@
-Ben Anderson,ben,ou029107.otago.ac.nz,15.11.2018 14:52,file:///Users/ben/Library/Application%20Support/LibreOffice/4;
\ No newline at end of file
+Ben Anderson,ben,octomac.local,28.11.2018 17:27,file:///Users/ben/Library/Application%20Support/LibreOffice/4;
\ No newline at end of file
diff --git a/paper/figs/statPowerEsts80means_All.png b/paper/figs/statPowerEsts80means_All.png
index 4b491349d9bdb5abf42708bc04d7ad35704404fa..36952bd15d9678ceb5bd870f187efdf48c5f0fa6 100644
Binary files a/paper/figs/statPowerEsts80means_All.png and b/paper/figs/statPowerEsts80means_All.png differ
diff --git a/paper/figs/statPowerEsts80means_p0.01.png b/paper/figs/statPowerEsts80means_p0.01.png
index bb346f2ecf4ce7e104be980bcadf3f5d50b0ac49..8c8bd6086ae20e08b6c4b33c79adb4df9217a198 100644
Binary files a/paper/figs/statPowerEsts80means_p0.01.png and b/paper/figs/statPowerEsts80means_p0.01.png differ
diff --git a/paper/weGotThePowerDraftPaper.Rmd b/paper/weGotThePowerDraftPaper.Rmd
index 52fd26341d00b217c6e0063b323e607628c10f82..8189bfc3a59c5d440e0ecf0d8b8b1431952bd15f 100644
--- a/paper/weGotThePowerDraftPaper.Rmd
+++ b/paper/weGotThePowerDraftPaper.Rmd
@@ -160,8 +160,21 @@ if(file.exists(heatPumpData)){
   stop()
 }
 
+```
+
+```{r rawTable}
 gst <- summary(gsDT)
 
+knitr::kable(caption = "Summary of loaded grid spy data", gst)
+```
+
+Notice that there are negawatts! Remove rf_46 and all negative values as per https://cfsotago.github.io/GREENGridData/gridSpy1mOutliersReport_v1.0.html
+
+```{r cleanGSdata}
+# remove rf_46 and all negative values as per https://cfsotago.github.io/GREENGridData/gridSpy1mOutliersReport_v1.0.html
+
+gsDT <- gsDT[linkID != "rf_46" & powerW >= 0]
+
 gsDT <- gsDT[, month := lubridate::month(r_dateTime)]
 gsDT <- gsDT[, year := lubridate::year(r_dateTime)]
 
@@ -174,6 +187,14 @@ gsDT <- gsDT[tmpM >= 9 & tmpM <= 11, season := "Spring"]
 # re-order to make sense
 gsDT <- gsDT[, season := factor(season, levels = c("Spring", "Summer", "Autumn", "Winter"))]
 
+knitr::kable(caption = "Summary of cleaned grid spy data", summary(gsDT))
+
+nHH <- uniqueN(gsDT$linkID)
+```
+
+Number of households in cleaned heatpump data: `r nHH`
+
+```{r load household data}
 # Load GREEN Grid household data
 if(file.exists(ggHHData)){
   message("Loading: ", ggHHData )
@@ -183,20 +204,6 @@ if(file.exists(ggHHData)){
   stop()
 }
 
-knitr::kable(caption = "Summary of grid spy data", gst)
-
-# there are negawatts!
-gsDT <- gsDT[, negW := "PosW"]
-gsDT <- gsDT[ powerW < 0, negW := "NegaW"]
-
-t <- table(gsDT$linkID,gsDT$negW)
-t
-prop.table(t)
-
-```
-
-```{r dataPrep}
-
 hhDT <- hhDT[Q57 == 1, nPeople := "1"]
 hhDT <- hhDT[Q57 == 2, nPeople := "2"]
 hhDT <- hhDT[Q57 == 3, nPeople := "3"]
@@ -209,7 +216,7 @@ testDT <- gsDT[lubridate::hour(r_dateTime) > 15 & # 16:00 ->
                         lubridate::hour(r_dateTime) < 20 & # <- 20:00
                         lubridate::wday(r_dateTime) != 6 & # not Saturday
                         lubridate::wday(r_dateTime) != 7 & # not Sunday
-                        year == 2015 & negW == "PosW", # remove the negawatts (see https://github.com/CfSOtago/GREENGridData/issues/6)
+                        year == 2015,
                         .(meanW = mean(powerW, na.rm = TRUE),
                           sdW = sd(powerW, na.rm = TRUE)), keyby = .(season, linkID)]
 setkey(testDT, linkID)
diff --git a/paper/weGotThePowerDraftPaper.html b/paper/weGotThePowerDraftPaper.html
index bccb2d44b4febd7e763b3c2963e6c734d4932a5d..9170d7d4751c2f7da36d6f590b100303fc1e6a96 100644
--- a/paper/weGotThePowerDraftPaper.html
+++ b/paper/weGotThePowerDraftPaper.html
@@ -239,7 +239,7 @@ div.tocify {
 <h1 class="title toc-ignore">Statistical Power, Statistical Significance, Study Design and Decision Making: A Worked Example</h1>
 <h3 class="subtitle"><em>Sizing Demand Response Trials in New Zealand</em></h3>
 <h4 class="author"><em>Ben Anderson and Tom Rushby (Contact: <a href="mailto:b.anderson@soton.ac.uk">b.anderson@soton.ac.uk</a>, <code>@dataknut</code>)</em></h4>
-<h4 class="date"><em>Last run at: 2018-11-15 14:49:28</em></h4>
+<h4 class="date"><em>Last run at: 2018-11-28 17:22:51</em></h4>
 
 </div>
 
@@ -297,7 +297,190 @@ div.tocify {
 <div id="means" class="section level2">
 <h2><span class="header-section-number">4.1</span> Means</h2>
 <table>
-<caption><span id="tab:dataPrep">Table 4.1: </span>Summary of mean consumption per household by season</caption>
+<caption><span id="tab:rawTable">Table 4.1: </span>Summary of loaded grid spy data</caption>
+<thead>
+<tr class="header">
+<th></th>
+<th align="center">hhID</th>
+<th align="center">linkID</th>
+<th align="center">r_dateTime</th>
+<th align="center">circuit</th>
+<th align="center">powerW</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td></td>
+<td align="center">Length:14250284</td>
+<td align="center">Length:14250284</td>
+<td align="center">Min. :2015-04-01 00:00:00</td>
+<td align="center">Length:14250284</td>
+<td align="center">Min. : -655.00</td>
+</tr>
+<tr class="even">
+<td></td>
+<td align="center">Class :character</td>
+<td align="center">Class :character</td>
+<td align="center">1st Qu.:2015-06-22 12:39:00</td>
+<td align="center">Class :character</td>
+<td align="center">1st Qu.: 0.00</td>
+</tr>
+<tr class="odd">
+<td></td>
+<td align="center">Mode :character</td>
+<td align="center">Mode :character</td>
+<td align="center">Median :2015-09-16 13:12:00</td>
+<td align="center">Mode :character</td>
+<td align="center">Median : 0.00</td>
+</tr>
+<tr class="even">
+<td></td>
+<td align="center">NA</td>
+<td align="center">NA</td>
+<td align="center">Mean :2015-09-21 08:00:39</td>
+<td align="center">NA</td>
+<td align="center">Mean : 147.92</td>
+</tr>
+<tr class="odd">
+<td></td>
+<td align="center">NA</td>
+<td align="center">NA</td>
+<td align="center">3rd Qu.:2015-12-17 17:52:00</td>
+<td align="center">NA</td>
+<td align="center">3rd Qu.: 61.29</td>
+</tr>
+<tr class="even">
+<td></td>
+<td align="center">NA</td>
+<td align="center">NA</td>
+<td align="center">Max. :2016-03-31 23:59:00</td>
+<td align="center">NA</td>
+<td align="center">Max. :27759.00</td>
+</tr>
+</tbody>
+</table>
+<p>Notice that there are negawatts! Remove rf_46 and all negative values as per <a href="https://cfsotago.github.io/GREENGridData/gridSpy1mOutliersReport_v1.0.html" class="uri">https://cfsotago.github.io/GREENGridData/gridSpy1mOutliersReport_v1.0.html</a></p>
+<table>
+<caption><span id="tab:cleanGSdata">Table 4.2: </span>Summary of cleaned grid spy data</caption>
+<thead>
+<tr class="header">
+<th></th>
+<th align="center">hhID</th>
+<th align="center">linkID</th>
+<th align="center">r_dateTime</th>
+<th align="center">circuit</th>
+<th align="center">powerW</th>
+<th align="center">month</th>
+<th align="center">year</th>
+<th align="center">tmpM</th>
+<th align="center">season</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td></td>
+<td align="center">Length:13298965</td>
+<td align="center">Length:13298965</td>
+<td align="center">Min. :2015-04-01 00:00:00</td>
+<td align="center">Length:13298965</td>
+<td align="center">Min. : 0.0</td>
+<td align="center">Min. : 1.000</td>
+<td align="center">Min. :2015</td>
+<td align="center">Min. : 1.000</td>
+<td align="center">Spring:3351249</td>
+</tr>
+<tr class="even">
+<td></td>
+<td align="center">Class :character</td>
+<td align="center">Class :character</td>
+<td align="center">1st Qu.:2015-06-20 15:32:00</td>
+<td align="center">Class :character</td>
+<td align="center">1st Qu.: 0.0</td>
+<td align="center">1st Qu.: 4.000</td>
+<td align="center">1st Qu.:2015</td>
+<td align="center">1st Qu.: 4.000</td>
+<td align="center">Summer:2875049</td>
+</tr>
+<tr class="odd">
+<td></td>
+<td align="center">Mode :character</td>
+<td align="center">Mode :character</td>
+<td align="center">Median :2015-09-14 20:06:00</td>
+<td align="center">Mode :character</td>
+<td align="center">Median : 0.0</td>
+<td align="center">Median : 7.000</td>
+<td align="center">Median :2015</td>
+<td align="center">Median : 7.000</td>
+<td align="center">Autumn:3471128</td>
+</tr>
+<tr class="even">
+<td></td>
+<td align="center">NA</td>
+<td align="center">NA</td>
+<td align="center">Mean :2015-09-19 21:24:45</td>
+<td align="center">NA</td>
+<td align="center">Mean : 152.0</td>
+<td align="center">Mean : 6.581</td>
+<td align="center">Mean :2015</td>
+<td align="center">Mean : 6.581</td>
+<td align="center">Winter:3601539</td>
+</tr>
+<tr class="odd">
+<td></td>
+<td align="center">NA</td>
+<td align="center">NA</td>
+<td align="center">3rd Qu.:2015-12-16 12:26:00</td>
+<td align="center">NA</td>
+<td align="center">3rd Qu.: 50.8</td>
+<td align="center">3rd Qu.: 9.000</td>
+<td align="center">3rd Qu.:2015</td>
+<td align="center">3rd Qu.: 9.000</td>
+<td align="center">NA</td>
+</tr>
+<tr class="even">
+<td></td>
+<td align="center">NA</td>
+<td align="center">NA</td>
+<td align="center">Max. :2016-03-31 23:59:00</td>
+<td align="center">NA</td>
+<td align="center">Max. :27759.0</td>
+<td align="center">Max. :12.000</td>
+<td align="center">Max. :2016</td>
+<td align="center">Max. :12.000</td>
+<td align="center">NA</td>
+</tr>
+</tbody>
+</table>
+<p>Number of households in cleaned heatpump data: 28</p>
+<pre><code>## Loading: ~/Dropbox/Work/Otago_CfS_Ben/data/nzGREENGrid/ggHouseholdAttributesSafe.csv</code></pre>
+<pre><code>## Parsed with column specification:
+## cols(
+##   .default = col_integer(),
+##   linkID = col_character(),
+##   hasApplianceSummary = col_character(),
+##   Oven = col_character(),
+##   `Fridge / Freezer 1` = col_character(),
+##   `Fridge / Freezer 2` = col_character(),
+##   `Fridge / Freezer 3` = col_character(),
+##   Dishwasher = col_character(),
+##   Microwave = col_character(),
+##   `Washing Machine` = col_character(),
+##   `Clothes Dryer` = col_character(),
+##   `Hot water cylinder` = col_character(),
+##   `Other Appliance` = col_character(),
+##   `Electric heater` = col_character(),
+##   `Heated towel rails` = col_character(),
+##   `PV Inverter` = col_character(),
+##   `Energy Storage` = col_character(),
+##   `Other Generation Device` = col_character(),
+##   hasLongSurvey = col_character(),
+##   StartDate = col_character(),
+##   Q14_1 = col_double()
+##   # ... with 12 more columns
+## )</code></pre>
+<pre><code>## See spec(...) for full column specifications.</code></pre>
+<table>
+<caption>(#tab:load household data)Summary of mean consumption per household by season</caption>
 <thead>
 <tr class="header">
 <th align="left">season</th>
@@ -332,9 +515,9 @@ div.tocify {
 <tr class="even">
 <td align="left">Spring</td>
 <td align="left">3</td>
-<td align="right">207.619377</td>
-<td align="right">171.401166</td>
-<td align="right">7</td>
+<td align="right">210.076391</td>
+<td align="right">187.625482</td>
+<td align="right">6</td>
 </tr>
 <tr class="odd">
 <td align="left">Spring</td>
@@ -360,9 +543,9 @@ div.tocify {
 <tr class="even">
 <td align="left">Summer</td>
 <td align="left">3</td>
-<td align="right">87.760306</td>
-<td align="right">133.023910</td>
-<td align="right">7</td>
+<td align="right">86.328405</td>
+<td align="right">145.661285</td>
+<td align="right">6</td>
 </tr>
 <tr class="odd">
 <td align="left">Summer</td>
@@ -395,9 +578,9 @@ div.tocify {
 <tr class="odd">
 <td align="left">Autumn</td>
 <td align="left">3</td>
-<td align="right">245.971947</td>
-<td align="right">194.352385</td>
-<td align="right">8</td>
+<td align="right">245.460272</td>
+<td align="right">209.918748</td>
+<td align="right">7</td>
 </tr>
 <tr class="even">
 <td align="left">Autumn</td>
@@ -430,9 +613,9 @@ div.tocify {
 <tr class="even">
 <td align="left">Winter</td>
 <td align="left">3</td>
-<td align="right">475.616350</td>
-<td align="right">280.427370</td>
-<td align="right">8</td>
+<td align="right">476.930850</td>
+<td align="right">302.869555</td>
+<td align="right">7</td>
 </tr>
 <tr class="odd">
 <td align="left">Winter</td>
@@ -444,7 +627,7 @@ div.tocify {
 </tbody>
 </table>
 <table>
-<caption><span id="tab:dataPrep">Table 4.1: </span>Summary of mean consumption per household in winter</caption>
+<caption>(#tab:load household data)Summary of mean consumption per household in winter</caption>
 <thead>
 <tr class="header">
 <th align="right">meanMeanW</th>
@@ -454,13 +637,13 @@ div.tocify {
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">412.6407</td>
-<td align="right">264.3291</td>
-<td align="right">28</td>
+<td align="right">410.6491</td>
+<td align="right">269.1503</td>
+<td align="right">27</td>
 </tr>
 </tbody>
 </table>
-<p><img src="weGotThePowerDraftPaper_files/figure-html/dataPrep-1.png" width="672" /></p>
+<p><img src="weGotThePowerDraftPaper_files/figure-html/load%20household%20data-1.png" width="672" /></p>
 <p>Observations are summarised to mean W per household during 16:00 - 20:00 on weekdays for year = 2015.</p>
 <pre><code>## Warning: replacing previous import &#39;data.table::melt&#39; by &#39;reshape2::melt&#39;
 ## when loading &#39;weGotThePower&#39;</code></pre>
@@ -476,7 +659,7 @@ Figure 4.1: Power analysis results (p = 0.01, power = 0.8)
 </p>
 </div>
 <pre><code>## Saving 7 x 5 in image</code></pre>
-<p>Effect size at n = 1000: 9.08.</p>
+<p>Effect size at n = 1000: 9.29.</p>
 <p>Figure <a href="#fig:ggHPSampleSizeFig80all">4.2</a> shows the plot for all results.</p>
 <pre><code>## Scale for &#39;y&#39; is already present. Adding another scale for &#39;y&#39;, which
 ## will replace the existing scale.</code></pre>
@@ -487,16 +670,16 @@ Figure 4.2: Power analysis results (power = 0.8)
 </p>
 </div>
 <pre><code>## Saving 7 x 5 in image</code></pre>
-<p>At same effect size (9.0816159%, n = 1000, p = 0.01):</p>
+<p>At same effect size (9.292105%, n = 1000, p = 0.01):</p>
 <ul>
-<li>p = 0.05, n = 575</li>
+<li>p = 0.05, n = 600</li>
 <li>p = 0.1, n = 425</li>
-<li>p = 0.2, n = 250</li>
+<li>p = 0.2, n = 275</li>
 </ul>
 <p>Full table of results:</p>
 <pre><code>## Using &#39;effectSize&#39; as value column. Use &#39;value.var&#39; to override</code></pre>
 <table>
-<caption><span id="tab:meansPowerTable">Table 4.2: </span>Power analysis for means results table (partial)</caption>
+<caption><span id="tab:meansPowerTable">Table 4.3: </span>Power analysis for means results table (partial)</caption>
 <thead>
 <tr class="header">
 <th align="right">sampleN</th>
@@ -509,143 +692,143 @@ Figure 4.2: Power analysis results (power = 0.8)
 <tbody>
 <tr class="odd">
 <td align="right">50</td>
-<td align="right">41.16</td>
-<td align="right">32.08</td>
-<td align="right">27.32</td>
-<td align="right">21.60</td>
+<td align="right">42.11</td>
+<td align="right">32.82</td>
+<td align="right">27.95</td>
+<td align="right">22.10</td>
 </tr>
 <tr class="even">
 <td align="right">100</td>
-<td align="right">28.90</td>
-<td align="right">22.60</td>
-<td align="right">19.27</td>
-<td align="right">15.26</td>
+<td align="right">29.57</td>
+<td align="right">23.13</td>
+<td align="right">19.72</td>
+<td align="right">15.62</td>
 </tr>
 <tr class="odd">
 <td align="right">150</td>
-<td align="right">23.54</td>
-<td align="right">18.43</td>
-<td align="right">15.73</td>
-<td align="right">12.46</td>
+<td align="right">24.09</td>
+<td align="right">18.86</td>
+<td align="right">16.09</td>
+<td align="right">12.75</td>
 </tr>
 <tr class="even">
 <td align="right">200</td>
-<td align="right">20.36</td>
-<td align="right">15.95</td>
-<td align="right">13.61</td>
-<td align="right">10.79</td>
+<td align="right">20.83</td>
+<td align="right">16.32</td>
+<td align="right">13.93</td>
+<td align="right">11.04</td>
 </tr>
 <tr class="odd">
 <td align="right">250</td>
-<td align="right">18.20</td>
-<td align="right">14.27</td>
-<td align="right">12.17</td>
-<td align="right">9.65</td>
+<td align="right">18.62</td>
+<td align="right">14.60</td>
+<td align="right">12.46</td>
+<td align="right">9.87</td>
 </tr>
 <tr class="even">
 <td align="right">300</td>
-<td align="right">16.61</td>
-<td align="right">13.02</td>
-<td align="right">11.11</td>
-<td align="right">8.81</td>
+<td align="right">16.99</td>
+<td align="right">13.32</td>
+<td align="right">11.37</td>
+<td align="right">9.01</td>
 </tr>
 <tr class="odd">
 <td align="right">350</td>
-<td align="right">15.37</td>
-<td align="right">12.05</td>
-<td align="right">10.29</td>
-<td align="right">8.15</td>
+<td align="right">15.73</td>
+<td align="right">12.33</td>
+<td align="right">10.53</td>
+<td align="right">8.34</td>
 </tr>
 <tr class="even">
 <td align="right">400</td>
-<td align="right">14.37</td>
-<td align="right">11.27</td>
-<td align="right">9.62</td>
-<td align="right">7.63</td>
+<td align="right">14.71</td>
+<td align="right">11.53</td>
+<td align="right">9.85</td>
+<td align="right">7.80</td>
 </tr>
 <tr class="odd">
 <td align="right">450</td>
-<td align="right">13.55</td>
-<td align="right">10.63</td>
-<td align="right">9.07</td>
-<td align="right">7.19</td>
+<td align="right">13.86</td>
+<td align="right">10.87</td>
+<td align="right">9.28</td>
+<td align="right">7.36</td>
 </tr>
 <tr class="even">
 <td align="right">500</td>
-<td align="right">12.85</td>
-<td align="right">10.08</td>
-<td align="right">8.61</td>
-<td align="right">6.82</td>
+<td align="right">13.15</td>
+<td align="right">10.31</td>
+<td align="right">8.80</td>
+<td align="right">6.98</td>
 </tr>
 <tr class="odd">
 <td align="right">550</td>
-<td align="right">12.25</td>
-<td align="right">9.61</td>
-<td align="right">8.20</td>
-<td align="right">6.50</td>
+<td align="right">12.54</td>
+<td align="right">9.83</td>
+<td align="right">8.39</td>
+<td align="right">6.65</td>
 </tr>
 <tr class="even">
 <td align="right">600</td>
-<td align="right">11.73</td>
-<td align="right">9.20</td>
-<td align="right">7.86</td>
-<td align="right">6.23</td>
+<td align="right">12.00</td>
+<td align="right">9.41</td>
+<td align="right">8.04</td>
+<td align="right">6.37</td>
 </tr>
 <tr class="odd">
 <td align="right">650</td>
-<td align="right">11.27</td>
-<td align="right">8.84</td>
-<td align="right">7.55</td>
-<td align="right">5.98</td>
+<td align="right">11.53</td>
+<td align="right">9.04</td>
+<td align="right">7.72</td>
+<td align="right">6.12</td>
 </tr>
 <tr class="even">
 <td align="right">700</td>
-<td align="right">10.86</td>
-<td align="right">8.52</td>
-<td align="right">7.27</td>
-<td align="right">5.76</td>
+<td align="right">11.11</td>
+<td align="right">8.72</td>
+<td align="right">7.44</td>
+<td align="right">5.90</td>
 </tr>
 <tr class="odd">
 <td align="right">750</td>
-<td align="right">10.49</td>
-<td align="right">8.23</td>
-<td align="right">7.03</td>
-<td align="right">5.57</td>
+<td align="right">10.73</td>
+<td align="right">8.42</td>
+<td align="right">7.19</td>
+<td align="right">5.70</td>
 </tr>
 <tr class="even">
 <td align="right">800</td>
-<td align="right">10.16</td>
-<td align="right">7.97</td>
-<td align="right">6.80</td>
-<td align="right">5.39</td>
+<td align="right">10.39</td>
+<td align="right">8.15</td>
+<td align="right">6.96</td>
+<td align="right">5.52</td>
 </tr>
 <tr class="odd">
 <td align="right">850</td>
-<td align="right">9.85</td>
-<td align="right">7.73</td>
-<td align="right">6.60</td>
-<td align="right">5.23</td>
+<td align="right">10.08</td>
+<td align="right">7.91</td>
+<td align="right">6.75</td>
+<td align="right">5.35</td>
 </tr>
 <tr class="even">
 <td align="right">900</td>
-<td align="right">9.57</td>
-<td align="right">7.51</td>
-<td align="right">6.41</td>
-<td align="right">5.08</td>
+<td align="right">9.80</td>
+<td align="right">7.69</td>
+<td align="right">6.56</td>
+<td align="right">5.20</td>
 </tr>
 <tr class="odd">
 <td align="right">950</td>
-<td align="right">9.32</td>
-<td align="right">7.31</td>
-<td align="right">6.24</td>
-<td align="right">4.95</td>
+<td align="right">9.53</td>
+<td align="right">7.48</td>
+<td align="right">6.39</td>
+<td align="right">5.06</td>
 </tr>
 <tr class="even">
 <td align="right">1000</td>
-<td align="right">9.08</td>
-<td align="right">7.13</td>
-<td align="right">6.08</td>
-<td align="right">4.82</td>
+<td align="right">9.29</td>
+<td align="right">7.29</td>
+<td align="right">6.22</td>
+<td align="right">4.93</td>
 </tr>
 </tbody>
 </table>
@@ -655,7 +838,7 @@ Figure 4.2: Power analysis results (power = 0.8)
 <p>Does not require a sample. As a relatively simple example, suppose we were interested in the adoption of heat pumps in two equal sized samples. Suppose we thought in one sample (say, home owners) we thought it might be 40% and in rental properties it would be 25% (ref BRANZ 2015). What sample size would we need to conclude a significant difference with power = 0.8 and at various p values?</p>
 <p><code>pwr::pwr.tp.test()</code> (ref pwr) can give us the answer…</p>
 <table>
-<caption><span id="tab:propTable1">Table 4.3: </span>Samples required if p1 = 40% and p2 = 25%</caption>
+<caption><span id="tab:propTable1">Table 4.4: </span>Samples required if p1 = 40% and p2 = 25%</caption>
 <thead>
 <tr class="header">
 <th align="right">n</th>
@@ -693,7 +876,7 @@ Figure 4.2: Power analysis results (power = 0.8)
 </table>
 <p>We can repeat this for other values of p1 and p2. For example, suppose both were much smaller (e.g. 10% and 15%)… Clearly we need <em>much</em> larger samples.</p>
 <table>
-<caption><span id="tab:propTable2">Table 4.4: </span>Samples required if p1 = 10% and p2 = 15%</caption>
+<caption><span id="tab:propTable2">Table 4.5: </span>Samples required if p1 = 10% and p2 = 15%</caption>
 <thead>
 <tr class="header">
 <th align="right">n</th>
@@ -762,31 +945,31 @@ Figure 4.2: Power analysis results (power = 0.8)
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
-<td align="right">147.9273</td>
-<td align="right">161.6783</td>
-<td align="right">7</td>
+<td align="right">199.7797</td>
+<td align="right">165.67094</td>
+<td align="right">5</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
-<td align="right">301.9291</td>
-<td align="right">76.8570</td>
-<td align="right">7</td>
+<td align="right">266.7466</td>
+<td align="right">50.77998</td>
+<td align="right">9</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
-<td align="right">429.2748</td>
-<td align="right">248.5965</td>
-<td align="right">14</td>
+<td align="right">527.6414</td>
+<td align="right">346.87919</td>
+<td align="right">15</td>
 </tr>
 <tr class="even">
 <td align="left">4+</td>
-<td align="right">470.3224</td>
-<td align="right">297.9899</td>
-<td align="right">24</td>
+<td align="right">355.9932</td>
+<td align="right">246.71091</td>
+<td align="right">21</td>
 </tr>
 </tbody>
 </table>
-<p>So a sample of 52.</p>
+<p>So a sample of 50.</p>
 <p><img src="weGotThePowerDraftPaper_files/figure-html/ggMeanDiffs-1.png" width="672" /></p>
 <p>T test 1 &lt;-&gt; 3</p>
 <table>
@@ -804,21 +987,21 @@ Figure 4.2: Power analysis results (power = 0.8)
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">147.9273</td>
-<td align="right">429.2748</td>
-<td align="right">-281.3475</td>
-<td align="right">-3.116754</td>
-<td align="right">0.0061527</td>
-<td align="right">-471.4924</td>
-<td align="right">-91.20272</td>
+<td align="right">199.7797</td>
+<td align="right">527.6414</td>
+<td align="right">-327.8617</td>
+<td align="right">-2.82063</td>
+<td align="right">0.0128783</td>
+<td align="right">-575.5436</td>
+<td align="right">-80.17989</td>
 </tr>
 </tbody>
 </table>
-<p>The results show that the mean power demand for the control group was 429.27W and for Intervention 1 was 147.93W. This is a (very) large difference in the mean of 281.35. The results of the t test are:</p>
+<p>The results show that the mean power demand for the control group was 527.64W and for Intervention 1 was 199.78W. This is a (very) large difference in the mean of 327.86. The results of the t test are:</p>
 <ul>
-<li>effect size = 281W or 66% representing a <em>substantial bang for buck</em> for whatever caused the difference;</li>
-<li>95% confidence interval for the test = -471.49 to -91.2 representing <em>considerable</em> uncertainty/variation;</li>
-<li>p value of 0.006 representing a <em>relatively low</em> risk of a false positive result but which (just) fails the conventional p &lt; 0.05 threshold.</li>
+<li>effect size = 328W or 62% representing a <em>substantial bang for buck</em> for whatever caused the difference;</li>
+<li>95% confidence interval for the test = -575.54 to -80.18 representing <em>considerable</em> uncertainty/variation;</li>
+<li>p value of 0.013 representing a <em>relatively low</em> risk of a false positive result but which (just) fails the conventional p &lt; 0.05 threshold.</li>
 </ul>
 <p>T test 1 &lt;-&gt; 4+</p>
 <table>
@@ -836,21 +1019,21 @@ Figure 4.2: Power analysis results (power = 0.8)
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">147.9273</td>
-<td align="right">470.3224</td>
-<td align="right">-322.3952</td>
-<td align="right">-3.739141</td>
-<td align="right">0.0013971</td>
-<td align="right">-502.9035</td>
-<td align="right">-141.8869</td>
+<td align="right">199.7797</td>
+<td align="right">355.9932</td>
+<td align="right">-156.2135</td>
+<td align="right">-1.705672</td>
+<td align="right">0.1228453</td>
+<td align="right">-363.944</td>
+<td align="right">51.51696</td>
 </tr>
 </tbody>
 </table>
 <p>Now:</p>
 <ul>
-<li>effect size = 322W or 68.55% representing a still <em>reasonable bang for buck</em> for whatever caused the difference;</li>
-<li>95% confidence interval for the test = -502.9 to -141.89 representing <em>even greater</em> uncertainty/variation;</li>
-<li>p value of 0.001 representing a <em>higher</em> risk of a false positive result which fails the conventional p &lt; 0.05 threshold and also the less conservative p &lt; 0.1.</li>
+<li>effect size = 156W or 43.88% representing a still <em>reasonable bang for buck</em> for whatever caused the difference;</li>
+<li>95% confidence interval for the test = -363.94 to 51.52 representing <em>even greater</em> uncertainty/variation;</li>
+<li>p value of 0.123 representing a <em>higher</em> risk of a false positive result which fails the conventional p &lt; 0.05 threshold and also the less conservative p &lt; 0.1.</li>
 </ul>
 </div>
 <div id="getting-it-right" class="section level2">
@@ -871,31 +1054,31 @@ Figure 4.2: Power analysis results (power = 0.8)
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
-<td align="right">159.2209</td>
-<td align="right">151.7489</td>
-<td align="right">88</td>
+<td align="right">181.7009</td>
+<td align="right">151.61975</td>
+<td align="right">87</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
-<td align="right">285.1232</td>
-<td align="right">63.8895</td>
-<td align="right">149</td>
+<td align="right">284.9767</td>
+<td align="right">64.27473</td>
+<td align="right">157</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
-<td align="right">511.0046</td>
-<td align="right">279.3558</td>
-<td align="right">308</td>
+<td align="right">512.7269</td>
+<td align="right">291.45021</td>
+<td align="right">289</td>
 </tr>
 <tr class="even">
 <td align="left">4+</td>
-<td align="right">417.3538</td>
-<td align="right">267.6910</td>
-<td align="right">495</td>
+<td align="right">421.9563</td>
+<td align="right">271.98331</td>
+<td align="right">467</td>
 </tr>
 </tbody>
 </table>
-<p>So n = 1040</p>
+<p>So n = 1000</p>
 <div class="figure"><span id="fig:largeNmeanDiffs"></span>
 <img src="weGotThePowerDraftPaper_files/figure-html/largeNmeanDiffs-1.png" alt="Mean W demand per group for large sample (Error bars = 95% confidence intervals for the sample mean)" width="672" />
 <p class="caption">
@@ -918,20 +1101,20 @@ Figure 5.1: Mean W demand per group for large sample (Error bars = 95% confidenc
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">159.2209</td>
-<td align="right">511.0046</td>
-<td align="right">-351.7837</td>
-<td align="right">-15.50063</td>
+<td align="right">181.7009</td>
+<td align="right">512.7269</td>
+<td align="right">-331.026</td>
+<td align="right">-14.01147</td>
 <td align="right">0</td>
-<td align="right">-396.4678</td>
-<td align="right">-307.0996</td>
+<td align="right">-377.5317</td>
+<td align="right">-284.5203</td>
 </tr>
 </tbody>
 </table>
 <p>In this case:</p>
 <ul>
-<li>effect size = 351.7837236W or 68.84% representing a still <em>reasonable bang for buck</em> for whatever caused the difference;</li>
-<li>95% confidence interval for the test = -396.47 to -307.1 representing <em>much less</em> uncertainty/variation;</li>
+<li>effect size = 331.0260257W or 64.56% representing a still <em>reasonable bang for buck</em> for whatever caused the difference;</li>
+<li>95% confidence interval for the test = -377.53 to -284.52 representing <em>much less</em> uncertainty/variation;</li>
 <li>p value of 0 representing a <em>very low</em> risk of a false positive result as it passes all conventional thresholds.</li>
 </ul>
 <p>re-run T tests 1 person vs 4+</p>
@@ -950,20 +1133,20 @@ Figure 5.1: Mean W demand per group for large sample (Error bars = 95% confidenc
 </thead>
 <tbody>
 <tr class="odd">
-<td align="right">159.2209</td>
-<td align="right">417.3538</td>
-<td align="right">-258.1329</td>
-<td align="right">-12.80393</td>
+<td align="right">181.7009</td>
+<td align="right">421.9563</td>
+<td align="right">-240.2554</td>
+<td align="right">-11.68658</td>
 <td align="right">0</td>
-<td align="right">-297.8882</td>
-<td align="right">-218.3776</td>
+<td align="right">-280.7865</td>
+<td align="right">-199.7243</td>
 </tr>
 </tbody>
 </table>
 <p>In this case:</p>
 <ul>
-<li>effect size = 258.1328841W or 61.85% representing a still <em>reasonable bang for buck</em> for whatever caused the difference;</li>
-<li>95% confidence interval for the test = -297.89 to -218.38 representing <em>much less</em> uncertainty/variation;</li>
+<li>effect size = 240.255403W or 56.94% representing a still <em>reasonable bang for buck</em> for whatever caused the difference;</li>
+<li>95% confidence interval for the test = -280.79 to -199.72 representing <em>much less</em> uncertainty/variation;</li>
 <li>p value of 0 representing a <em>very low</em> risk of a false positive result as it passes all conventional thresholds.</li>
 </ul>
 </div>
@@ -985,7 +1168,7 @@ Figure 5.1: Mean W demand per group for large sample (Error bars = 95% confidenc
 </div>
 <div id="runtime" class="section level1">
 <h1><span class="header-section-number">8</span> Runtime</h1>
-<p>Analysis completed in 42.5 seconds ( 0.71 minutes) using <a href="https://cran.r-project.org/package=knitr">knitr</a> in <a href="http://www.rstudio.com">RStudio</a> with R version 3.5.1 (2018-07-02) running on x86_64-apple-darwin15.6.0.</p>
+<p>Analysis completed in 45.84 seconds ( 0.76 minutes) using <a href="https://cran.r-project.org/package=knitr">knitr</a> in <a href="http://www.rstudio.com">RStudio</a> with R version 3.5.1 (2018-07-02) running on x86_64-apple-darwin15.6.0.</p>
 </div>
 <div id="r-environment" class="section level1">
 <h1><span class="header-section-number">9</span> R environment</h1>
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