copied repo from github

c069e1bb · Ben Anderson · c069e1bb · c069e1bb · c069e1bb · c069e1bb
Commit c069e1bb authored Feb 13, 2018 by Ben Anderson
--- a/.gitignore
+++ b/.gitignore
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
--- a/2015_2016/README.md
+++ b/2015_2016/README.md
+=======
+# FEEG6025 Semester 1 2015-2016
+
+Data Analysis &amp; Experimental Methods (for Engineering) taught by:
+ * [@dataknut](https://twitter.com/dataknut)
+ * [@Stepha_Gauthier](https://twitter.com/@Stepha_Gauthier)
+ * [Luke Blunden](http://www.southampton.ac.uk/engineering/about/staff/lsb1.page)
+
+Code used in University of Southampton Faculty of Engineering & Environment (FEE) Masters Module:
+ - Module [Description](http://www.southampton.ac.uk/engineering/undergraduate/modules/feeg6025_data_analysis_and_experimental_methods_for_civil_and_environmental_engineering.page?)
+ - Module [Profile](https://syllabus.soton.ac.uk/pdf/moduleprofile/FEEG6025)
+ - Module [Syllabus 2015-2016](https://syllabus.soton.ac.uk/view/syllabus/module/FEEG6025/201516)
+ - Module [Resources (blackboard.soton.ac.uk)](https://blackboard.soton.ac.uk/) - look for FEEG6025 (if you've registered)
+ - [Twitter feed](https://twitter.com/uosfeeg6025) - follow us whether you're a student or not :-)
+
+We have made extensive use of:
+* Field et al's excellent [Discovering Statistics Using R](https://uk.sagepub.com/en-gb/eur/discovering-statistics-using-r/book236067);
+* [RStudio](https://www.rstudio.com/products/RStudio/#Desktop)
+* [Swirl](http://swirlstats.com/)
+
+## Structure
+ * Week 1 - Welcome, Module Introduction & Study Design
+ * Week 2 - Data collection instruments: In which we played with a small survey and some heart rate data
+ * Week 3 - Descriptive and Exploratory Analysis: In which we played with distributions and analysed the small survey data
+ * Week 4 - Time Series Analysis: In which we played with heart rate data, metabolic rates and other time series
+ * Week 5 - Inferential Statistics: In which we used t tests and anova as well as testing for normal distributions
+ * Week 6 - Fatigue, Rainflow and Autoregression: In which we did some time series modelling of biscuit prices
+ * Week 7 - Statistical modelling I: In which we introduced regression and tested differences between heart and metabolic rates
+ * Week 8 - Statistical modelling II (More regression): In which we used DECC's NEED data and regression models to analyse factors affecting domestic gas consumption
+ * Week 9 - Statistical modelling III (Even more regression): In which we continued to use DECC's NEED data to analyse factors affecting domestic gas consumption
+ * Week 10 - Non-parametric inference & Data Mining: In which we introduced some useful non-parametric tests and demonstrated the seductive snake oil of factor & cluster analysis
+ * Week 11 - Data management & archiving: In which we explained research ethics, data protection (Acts), data management, data preservation and data re-use using excellent material from the [UKDS](https://www.ukdataservice.ac.uk/manage-data/lifecycle) and [ADRN](http://adrn.ac.uk/protecting-privacy/legal)
+ * Week 12 - Revision week
+ 
+### Also:
+ * Assignment 1 (Study Design): in which students designed a data collection study
+ * Assignment 2 (Data Analysis): in which some students are bravely analysing > 24 million rows of electricity smart meter data.
+
+If you want to re-use any of the code, read the License file!
--- a/2015_2016/other/analyse_DECC_NEED_eul_file_2014.R
+++ b/2015_2016/other/analyse_DECC_NEED_eul_file_2014.R
+# Meta ----
+# Script to load & analyse DECC NEED data
+# Data from: http://discover.ukdataservice.ac.uk/catalogue/?sn=7518
+# See also: https://www.gov.uk/government/collections/national-energy-efficiency-data-need-framework
+
+# code by: b.anderson@soton.ak.uk (@dataknut)
+
+### Housekeeping  ------------------------
+
+# clear the workspace
+rm(list=ls())
+
+# where is the default working directory?
+getwd()
+
+# set a variable with the data location
+# You will need to change this
+dpath <- "~/UoS One Drive/PG/Southampton/FEEG6025 Data Analysis & Experimental Methods for Engineers/Data"
+# set a variable for where we want the results to go
+# You will need to change this too!
+rpath <- "~/UoS One Drive/PG/Southampton/FEEG6025 Data Analysis & Experimental Methods for Engineers/Week 3"
+
+# input file name
+# long form of data (pre-processed)
+# notice that it has a .dta suffix - this is a STATA format data file
+ifile <- "need_eul_may2014_merged_100pc.dta"
+
+### Required packages ----
+# We're going to load a STATA file so we need the 'foreign' package
+#install.packages("foreign") # if needed
+library(foreign)
+library(data.table)
+
+### Load data  ------------------------
+
+# change working directory to where the data is
+setwd(dpath)
+
+# load the data into a data frame
+# this may take a few seconds - it is quite large!
+needEulf2014DT <- as.data.table(read.dta(ifile))
+
+# change the working directory once loaded in case we want to save out any results
+setwd(rpath)
+
+# how much data do we have?
+dim(needEulf2014DT)
+
+## Q0: Check out the data ----
+# Q0.0: What are the variable names?
+# Hint: use names()
+names(needEulf2014DT)
+
+# Q0.1: What kind of data do we have and what are the central tendencies?
+# Hint: use summary()
+summary(needEulf2014DT)
+
+# Q0.1a: Do that again for gas & electricity consumption - do you notice anything strange?
+# Why might that have happened?
+# Hint: use summary() for each variable
+summary(needEulf2014DT$Gcons)
+summary(needEulf2014DT$Econs)
+
+# Q0.2: How many properties do not have 'valid' elec readings per year?
+# Hint: use table(x,y) and the EconsValid variable
+table(needEulf2014DT$EconsValid, needEulf2014DT$year)
+# What do the codes G, L, M & V mean?
+# Hint: RTFM :-) -> http://bit.ly/1LazmvE -> "Excel workbook explaining variable codes"
+
+# Q0.2a Draw a mosaicplot to visualise this
+# Hint: You will need 4 colours as there are 4 'valid' codes
+mosaicplot(year ~ EconsValid, 
+           data = needEulf2014DT,
+           main = "Valid codes by year (Electricity)",
+           xlab = "Year",
+           ylab = "N",
+           color = c("tan1", "tan2", "sienna1", "green1"))
+
+# Q0.3: How many properties do not have 'valid' gas readings per year? 
+# How many do not have gas at all?
+# Hint: use table(x,y) and the GconsValid variable
+table(needEulf2014DT$GconsValid, needEulf2014DT$year)
+
+# Q0.3a Draw a mosaicplot to visualise this
+# no hint needed except you will need 5 colours this time to include O (off gas)
+mosaicplot(year ~ GconsValid, 
+           data = needEulf2014DT,
+           main = "Valid codes by year (Gas)",
+           xlab = "Year",
+           ylab = "N",
+           color = c("tan1", "tan2", "sienna1", "black", "green1"))
+
+
+## Q1: Descriptive Analysis over time ----
+# Q1.1: How does elec consumption vary over time?
+# Hint: use a boxplot(x ~y) and don't forget to add axis labels!
+boxplot(Econs ~ year,
+        data = needEulf2014DT,
+        xlab = "Year",
+        ylab = "E consumption (kWh)")
+
+# Q1.2: How does gas consumption vary over time?
+boxplot(Gcons ~ year,
+        data = needEulf2014DT,
+        xlab = "Year",
+        ylab = "G consumption (kWh)")
+
+## Q2: Descriptive Analysis by UK region for 2012 ----
+# Q2.1 is there any difference in domestic electricity consumption by UK region in 2012?
+# Hint: use a boxplot() but select year == 2012 by sub-setting first & don't forget to label axes
+cons2012DT <- as.data.table(subset(needEulf2014DT, needEulf2014DT$year == 2012))
+
+boxplot(cons2012DT$Econs ~ cons2012DT$ba_region,
+        xlab = "Region",
+        ylab = "E consumption (kWh, 2012)")
+
+# Q2.1 is there any difference in domestic gas consumption by UK region in 2012?
+# Hint: use a boxplot() but select year == 2012 by sub-setting first
+boxplot(Gcons ~ ba_region,
+        data = cons2012DT,
+        xlab = "Region",
+        ylab = "G consumption (kWh, 2012)")
+
+## Q3: Descriptive Analysis by energy efficiency band in 2012 ----
+# Q3.1 is there a difference in domestic electricity consumption by EE band in 2012?
+# Hint: have a quick look at the EE_BAND first
+table(cons2012DT$EE_BAND)
+
+# now use boxplot (no hint, you should know how to do this!)
+boxplot(Econs ~ EE_BAND,
+        data = cons2012DT,
+        xlab = "EE Band",
+        ylab = "E consumption (kWh, 2012)")
+
+# Q3.1 is there a difference in domestic electricity consumption by EE band in 2012?
+# (no hint, you should know how to do this!)
+boxplot(Gcons ~ EE_BAND,
+        data = cons2012DT,
+        xlab = "EE Band",
+        ylab = "G consumption (kWh, 2012)")
+
+# Q4: Is there an association between property age and wall structure using 2012 subset? ----
+# Why use the 2012 subset? What would happen if we used all the data?
+mosaicplot(PROP_AGE ~ WALL_CONS, 
+           data = cons2012DT,
+           main = "Dwelling age by wall type",
+           xlab = "Dwelling age",
+           ylab = "Wall type",
+           color = c("green", "red"))
+
+# Homework! Edit this file to save the graphs so you can use them in a report ----
+# Hint: refer to the playWithDistributions.R code :-)
\ No newline at end of file
--- a/2015_2016/results/aggOctEvePeakWide.csv
+++ b/2015_2016/results/aggOctEvePeakWide.csv
--- a/2015_2016/results/aggOctEvePeakWide.xlsx
+++ b/2015_2016/results/aggOctEvePeakWide.xlsx
--- a/2015_2016/results/cer_clusterHourlyMeans.csv
+++ b/2015_2016/results/cer_clusterHourlyMeans.csv
+"","Group.1","X","ID","Mean_kWh.0","Mean_kWh.1","Mean_kWh.2","Mean_kWh.3","Mean_kWh.4","Mean_kWh.5","Mean_kWh.6","Mean_kWh.7","Mean_kWh.8","Mean_kWh.9","Mean_kWh.10","Mean_kWh.11","Mean_kWh.12","Mean_kWh.13","Mean_kWh.14","Mean_kWh.15","Mean_kWh.16","Mean_kWh.17","Mean_kWh.18","Mean_kWh.19","Mean_kWh.20","Mean_kWh.21","Mean_kWh.22","Mean_kWh.23","kmeanssubsetDT_s.cluster"
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--- a/2015_2016/results/cer_clusterHourlyMeans_Scaled.xlsx
+++ b/2015_2016/results/cer_clusterHourlyMeans_Scaled.xlsx
--- a/2015_2016/results/cer_clusterHourlyMeans_raw.csv
+++ b/2015_2016/results/cer_clusterHourlyMeans_raw.csv
+"","Group.1","X","ID","Mean_kWh.0","Mean_kWh.1","Mean_kWh.2","Mean_kWh.3","Mean_kWh.4","Mean_kWh.5","Mean_kWh.6","Mean_kWh.7","Mean_kWh.8","Mean_kWh.9","Mean_kWh.10","Mean_kWh.11","Mean_kWh.12","Mean_kWh.13","Mean_kWh.14","Mean_kWh.15","Mean_kWh.16","Mean_kWh.17","Mean_kWh.18","Mean_kWh.19","Mean_kWh.20","Mean_kWh.21","Mean_kWh.22","Mean_kWh.23","sample","res_stim","res_tariff","ba_income","ba_nadults","ba_nchildren","ba_npeople","ba_empl","ba_floorarea","cluster"
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+"6",6,3225.94978479197,5926.0243902439,0.273343152682001,0.199285578747628,0.165920951543481,0.153576410422548,0.150452908779562,0.158503031425001,0.203221363447031,0.318769727403156,0.396443328550933,0.417159091960939,0.436450363308187,0.433586083213773,0.469541560605359,0.455413384551303,0.41886027676216,0.424576155875411,0.494786990327209,0.657225297357338,0.728500069421947,0.75868146896839,0.71668207062526,0.664588836950988,0.562178113574305,0.421348822140973,NA,NA,NA,NA,NA,NA,NA,NA,NA,6
+"7",7,2074.40701754386,4166.01403508772,0.504394736842105,0.412292303338993,0.363163271080928,0.340681663837012,0.35817311827957,0.439224278438031,0.51017753254103,0.564390662139219,0.565012507074137,0.530501075268817,0.536670118845501,0.546769383135258,0.572387153367289,0.586892246745897,0.562508149405773,0.571579569892473,0.662460045274477,0.871679796264856,0.97316960950764,0.999014601018676,0.964785738539898,0.900823146576118,0.79307441992077,0.646546519524618,NA,NA,NA,NA,NA,NA,NA,NA,NA,7
+"8",8,1977.27516778523,4018.80536912752,0.869659666594501,0.673492206105218,0.574235981814245,0.540662697553583,0.554195929854947,0.548311431045681,0.639448581944144,0.868724507469149,0.939592227754925,0.906672223424983,0.961412426932236,0.994078588439056,1.02845053041784,1.05910759904741,1.00647077289457,1.01212221260013,1.12821844555098,1.39313909937216,1.59078577614202,1.68858042866421,1.65274518294003,1.53873544057155,1.39464721801256,1.13775860575882,NA,NA,NA,NA,NA,NA,NA,NA,NA,8
+"9",9,1046.15372424723,2595.82725832013,0.148085834057564,0.112697714840755,0.0977836511425796,0.0933946628495476,0.0927107765451664,0.0991769848167272,0.122936174019733,0.191944097950003,0.231781682940545,0.242730944225755,0.242015157711773,0.238992612852104,0.259034354071878,0.245825622411942,0.21974985941414,0.223180665610143,0.263751648688717,0.340590307244006,0.38211277542048,0.405745514033025,0.392567966872859,0.365239813915444,0.311424824906702,0.228575711875671,NA,NA,NA,NA,NA,NA,NA,NA,NA,9
+"10",10,3146.80206540447,5805.22547332186,0.136624812614513,0.10618760757315,0.0928658875131864,0.0875628227194492,0.0874646327244462,0.0894728776858586,0.117003442340792,0.178398867358836,0.199778357670313,0.200884043084781,0.198222558436511,0.196014963078119,0.208359807895175,0.199423852090389,0.184385930820054,0.186318888457054,0.220385097995669,0.284171617344956,0.349043112542335,0.374805757592582,0.355889067791905,0.327398645272334,0.281988007328855,0.206941369163289,NA,NA,NA,NA,NA,NA,NA,NA,NA,10
--- a/2015_2016/results/cer_clusterHourlyMeans_raw.xlsx
+++ b/2015_2016/results/cer_clusterHourlyMeans_raw.xlsx
--- a/2015_2016/results/cer_clusterHourlyMeans_scaled.csv
+++ b/2015_2016/results/cer_clusterHourlyMeans_scaled.csv
+"","Group.1","X","ID","Mean_kWh.0","Mean_kWh.1","Mean_kWh.2","Mean_kWh.3","Mean_kWh.4","Mean_kWh.5","Mean_kWh.6","Mean_kWh.7","Mean_kWh.8","Mean_kWh.9","Mean_kWh.10","Mean_kWh.11","Mean_kWh.12","Mean_kWh.13","Mean_kWh.14","Mean_kWh.15","Mean_kWh.16","Mean_kWh.17","Mean_kWh.18","Mean_kWh.19","Mean_kWh.20","Mean_kWh.21","Mean_kWh.22","Mean_kWh.23","kmeanssubsetDT_s.cluster"
+"1",1,-0.116822738992549,-0.117779734421229,3.573694221776,4.36524141303448,4.96030525104582,5.47960373437447,5.06377649515657,4.98501018084891,4.71893438312954,3.71775878633344,3.35474035783299,3.43937900240289,3.43080658010292,3.42798747791531,3.20185191366268,3.30963479269807,3.10716802028621,3.11424455616866,3.25878655997568,3.09146448125561,3.18198142513003,3.17032606693239,3.34680436682448,3.40609533851834,3.29253776022372,3.27415478692625,1
+"2",2,0.189830442989757,0.189516895730301,-0.078101169764338,-0.109383237213047,-0.0981905992454049,-0.0730460613247893,-0.0718308504614748,-0.0801446698682024,0.00221791526068288,0.202216436015031,0.375281140099813,0.605029301670972,0.657565643654541,0.671776602886494,0.637617567488134,0.667089242649473,0.64741665612995,0.659650736978605,0.659997259384983,0.614141045874797,0.503712249954099,0.403421701789921,0.323335150484341,0.223855289642538,0.130410591962599,0.0218001954961775,2
+"3",3,-0.945965157258388,-0.946821575876854,-0.0974650554012801,-0.154355545511578,-0.17467514091153,-0.196339578159146,-0.190478085771085,-0.196865356177998,-0.136785227906494,-0.145841617301251,-0.163440340629203,-0.172586446393883,-0.183952609461319,-0.164774895895288,-0.127075147582309,-0.163768384271116,-0.167843866423664,-0.172079137639854,-0.164032374910897,-0.107784486225283,-0.0831077592105849,-0.0862491425377101,-0.106515602722184,-0.0866475666590762,-0.0866822050549851,-0.0851442764959623,3
+"4",4,-0.195176280307936,-0.195354434425268,0.410040488268865,0.42131659641856,0.478840020625363,0.475690897478557,0.432094106422025,0.384104322247433,0.499809123736362,1.05733280974815,1.48954446933952,1.7253059621904,1.75061738450028,1.74721046131751,1.71130062045033,1.66015175948556,1.70209553454809,1.73300627064977,1.65616737314234,1.47291879528956,1.42596283560417,1.33344401742809,1.19858444498358,1.09835834195862,0.870010693200581,0.604748270295663,4
+"5",5,0.079081014245236,0.07893259011656,1.19824864360691,0.903639234214332,0.695960156486691,0.550212589358904,0.424847063399177,0.358209856231011,0.311611429441299,0.513024322888012,0.502101714347899,0.385074547772319,0.455159266786502,0.499782325515503,0.513192629703028,0.70117519352123,0.748433963843948,0.717896423783964,0.759372962551237,0.814096745768329,1.0405398373045,1.22623566836553,1.31754073458484,1.40078157046349,1.46624567220733,1.42947650569444,5
+"6",6,0.912405910319756,0.913702992827485,-0.219746542639081,-0.219445173582384,-0.231352915849536,-0.226946120611166,-0.221361447354523,-0.219924692195565,-0.204603677395319,-0.228456482354795,-0.213156845360812,-0.206901460893567,-0.199302121816727,-0.230821734467909,-0.211991949450672,-0.242133832670529,-0.263286064215894,-0.268846012419839,-0.269073037622221,-0.223334919519766,-0.243713203953416,-0.24423204943555,-0.241300600596615,-0.228446048825894,-0.227305953382448,-0.208830351134902,6
+"7",7,-0.0316388625713523,-0.0320010979032803,0.74351033043379,0.986618873701046,1.09609677881945,1.10081945721495,1.10788509020376,1.36140320626953,1.06265927348026,0.497327474402549,0.250554293742773,0.140339398269898,0.107115068324938,0.108330747896184,0.0723575021756881,0.149265268000147,0.186172396295053,0.168446552703016,0.152081222284459,0.21223924111012,0.245723410964782,0.227588753976239,0.271644637396983,0.297683483732252,0.356621657602885,0.491805771095003,7
+"8",8,-0.111268397534267,-0.111100540234248,2.26630427861408,2.46555721466008,2.51662562918466,2.51995616204475,2.36227744902237,1.97590089612408,1.59635244541715,1.39660188957439,1.28097272778693,1.2927984330076,1.40574470357451,1.44868773932807,1.33329020418637,1.55500198589288,1.57528282031599,1.47893327550302,1.32195395124178,1.27136460061848,1.48120194993785,1.58134093965889,1.69397556826892,1.71841063206859,1.87797543056625,2.02006709565591,8
+"9",9,-0.874609852753958,-0.875707530453613,-0.741945832058633,-0.709713777963263,-0.689920016585612,-0.654017255080304,-0.590865837555902,-0.554113620199828,-0.536059619022236,-0.603212841219983,-0.666118886948698,-0.741289320662443,-0.7937786204277,-0.813918874865709,-0.794006279221252,-0.86605512498833,-0.88628075258807,-0.867939245261711,-0.849378074413293,-0.866445792675864,-0.936625875336109,-0.937114151831938,-0.911394628160763,-0.895141592802352,-0.861451085954121,-0.808586947297541,9
+"10",10,0.847519921801438,0.84879426683372,-0.789726970273222,-0.746574615734838,-0.723016787317173,-0.695402071587711,-0.624437046279456,-0.608777723822252,-0.560552793654951,-0.643237569015321,-0.754155730612376,-0.869493834836585,-0.927672433548504,-0.942700917950979,-0.934112228633349,-1.00418844064255,-0.996930611935108,-0.977592352259554,-0.958304386798906,-0.981036643517441,-1.00277838393974,-0.997854927007457,-0.987226907371147,-0.979419597979699,-0.935895631365996,-0.875895814844145,10
--- a/2015_2016/results/cer_fullMeans.csv
+++ b/2015_2016/results/cer_fullMeans.csv
+"","Year","Tariff","Stimulus","Missing","Mean_kWh","StdErr_kWh","n_half_hours"
+"1",109,"Control","Control","OK",0.792605731266801,0.00286734039464853,95232
+"2",110,"Control","Control","OK",0.753597488239247,0.00279872885076352,95232
+"3",109,"A","Bi-monthly detailed bill +OLR","OK",0.841016459545214,0.00506969687239806,30256
+"4",110,"A","Bi-monthly detailed bill +OLR","OK",0.759483738762559,0.00491104177589471,30256
+"5",109,"A","Monthly detailed bill","OK",0.805723330210146,0.00521307538760273,29884
+"6",110,"A","Monthly detailed bill","OK",0.730354838709677,0.00487598464649467,29884
+"7",109,"D","Bi-monthly detailed bill +OLR","OK",0.82943026026393,0.00845668110433582,10912
+"8",110,"D","Bi-monthly detailed bill +OLR","OK",0.728580003665689,0.00777483091209132,10912
+"9",109,"C","Bi-monthly detailed bill +IHD","Survey missing",0.774743186874305,0.010353735166602,7192
+"10",110,"C","Bi-monthly detailed bill +IHD","Survey missing",0.737337180200222,0.0099285473383744,7192
+"11",109,"D","Bi-monthly detailed bill","OK",0.837362296219216,0.00858403976369715,11532
+"12",110,"D","Bi-monthly detailed bill","OK",0.758946409989594,0.00818754492590344,11532
+"13",109,"A","Monthly detailed bill","Survey missing",0.764795918367347,0.0118694247993312,6076
+"14",110,"A","Monthly detailed bill","Survey missing",0.767972514812377,0.0119940752398139,6076
+"15",109,"Control","Control","Survey missing",0.652140853536365,0.00577535221817785,19964
+"16",110,"Control","Control","Survey missing",0.643026898417151,0.00576658204113446,19964
+"17",109,"C","Bi-monthly detailed bill +IHD","OK",0.817714142323134,0.00540758957561863,28892
+"18",110,"C","Bi-monthly detailed bill +IHD","OK",0.690192959988924,0.00462124932806625,28892
+"19",109,"B","Monthly detailed bill","OK",0.857359117840685,0.00860665894681493,12152
+"20",110,"B","Monthly detailed bill","OK",0.738417709019092,0.00772086476862988,12152
+"21",109,"B","Bi-monthly detailed bill +OLR","OK",0.886094827586207,0.0097003429300021,10788
+"22",110,"B","Bi-monthly detailed bill +OLR","OK",0.773242120875046,0.0086131448154524,10788
+"23",109,"C","Monthly detailed bill","OK",0.827427748518762,0.00523715365934304,30380
+"24",110,"C","Monthly detailed bill","OK",0.73307340355497,0.00485731302322532,30380
+"25",109,"C","Bi-monthly detailed bill","OK",0.819112225806452,0.00508107054216979,31000
+"26",110,"C","Bi-monthly detailed bill","OK",0.765124064516129,0.00486078210821787,31000
+"27",109,"A","Bi-monthly detailed bill +IHD","OK",0.836185449110122,0.00516190960087007,28768
+"28",110,"A","Bi-monthly detailed bill +IHD","OK",0.735730985817575,0.00466532716423584,28768
+"29",109,"A","Bi-monthly detailed bill +OLR","Survey missing",0.759525033602151,0.0111140347665396,5952
+"30",110,"A","Bi-monthly detailed bill +OLR","Survey missing",0.692656754032258,0.0106628109205964,5952
+"31",109,"D","Monthly detailed bill","OK",0.844352150537634,0.00856030314444027,11904
+"32",110,"D","Monthly detailed bill","OK",0.686279821908602,0.0076814450404015,11904
+"33",109,"C","Bi-monthly detailed bill +OLR","OK",0.831476856871781,0.0053427830219318,29512
+"34",110,"C","Bi-monthly detailed bill +OLR","OK",0.730103415559772,0.00481266947972141,29512
+"35",109,"D","Bi-monthly detailed bill +IHD","OK",0.784601344086022,0.008461867555735,11160
+"36",110,"D","Bi-monthly detailed bill +IHD","OK",0.675509318996416,0.00740100603484247,11160
+"37",109,"A","Bi-monthly detailed bill","OK",0.826730980588067,0.00538634145274984,28024
+"38",110,"A","Bi-monthly detailed bill","OK",0.732113153011704,0.00485238230793711,28024
+"39",109,"Weekend tariff","Weekend tariff","Survey missing",0.907037903225806,0.0264268426501743,1240
+"40",110,"Weekend tariff","Weekend tariff","Survey missing",1.00897258064516,0.0284636231095795,1240
+"41",109,"D","Bi-monthly detailed bill +OLR","Survey missing",0.772259274193548,0.0170825278437382,2480
+"42",110,"D","Bi-monthly detailed bill +OLR","Survey missing",0.610490322580645,0.0142145793050403,2480
+"43",109,"B","Bi-monthly detailed bill +IHD","OK",0.835541294209094,0.0090037193798588,10292
+"44",110,"B","Bi-monthly detailed bill +IHD","OK",0.717710066070735,0.00791734603743218,10292
+"45",109,"C","Bi-monthly detailed bill +OLR","Survey missing",0.780576544559869,0.0107954221170829,7316
+"46",110,"C","Bi-monthly detailed bill +OLR","Survey missing",0.70567140513942,0.00971246028167309,7316
+"47",109,"C","Monthly detailed bill","Survey missing",0.70799,0.0100861959114441,6200
+"48",110,"C","Monthly detailed bill","Survey missing",0.65418,0.0097355301877383,6200
+"49",109,"D","Bi-monthly detailed bill +IHD","Survey missing",0.715748759305211,0.0215756742926282,1612
+"50",110,"D","Bi-monthly detailed bill +IHD","Survey missing",0.593577543424318,0.0164635982013086,1612
+"51",109,"","Bi-monthly detailed bill +IHD","OK",1.01708548387097,0.035397764045651,1240
+"52",110,"","Bi-monthly detailed bill +IHD","OK",0.871264516129032,0.0273466417669102,1240
+"53",109,"B","Bi-monthly detailed bill","OK",0.81274229390681,0.0082421123644544,11160
+"54",110,"B","Bi-monthly detailed bill","OK",0.727904659498208,0.00779205259760356,11160
+"55",109,"D","Monthly detailed bill","Survey missing",0.667501792114695,0.0189711274637967,2232
+"56",110,"D","Monthly detailed bill","Survey missing",0.599977150537634,0.0154668851302248,2232
+"57",109,"B","Bi-monthly detailed bill","Survey missing",0.54570564516129,0.0140348077458592,2232
+"58",110,"B","Bi-monthly detailed bill","Survey missing",0.465838261648746,0.0125595643135494,2232
+"59",109,"B","Bi-monthly detailed bill +OLR","Survey missing",1.35060752688172,0.0304933548255906,2232
+"60",110,"B","Bi-monthly detailed bill +OLR","Survey missing",1.15069534050179,0.0294447087967653,2232
+"61",109,"A","Bi-monthly detailed bill","Survey missing",0.780924340175953,0.0113259643513415,6820
+"62",110,"A","Bi-monthly detailed bill","Survey missing",0.659956744868035,0.00981413511436017,6820
+"63",109,"C","Bi-monthly detailed bill","Survey missing",0.779654048716261,0.0115223844409837,6076
+"64",110,"C","Bi-monthly detailed bill","Survey missing",0.662915569453588,0.0106342020779726,6076
+"65",109,"Weekend tariff","Weekend tariff","OK",0.837390280135823,0.00970369074187192,9424
+"66",110,"Weekend tariff","Weekend tariff","OK",0.678444397283531,0.00824836011240436,9424
+"67",109,"B","Bi-monthly detailed bill +IHD","Survey missing",0.792142857142857,0.0191678894248966,2604
+"68",110,"B","Bi-monthly detailed bill +IHD","Survey missing",0.808532258064516,0.0190431195439701,2604
+"69",109,"A","Bi-monthly detailed bill +IHD","Survey missing",0.823217351716962,0.0104651801389828,7688
+"70",110,"A","Bi-monthly detailed bill +IHD","Survey missing",0.714272372528616,0.0097489978523962,7688
+"71",109,"B","Monthly detailed bill","Survey missing",0.683937965260546,0.0209481695673297,1612
+"72",110,"B","Monthly detailed bill","Survey missing",0.596232630272953,0.0172480402911136,1612
+"73",109,"D","Bi-monthly detailed bill","Survey missing",0.612046774193548,0.0159264722699766,1860
+"74",110,"D","Bi-monthly detailed bill","Survey missing",0.593579032258065,0.0165365740129367,1860
--- a/2015_2016/results/cer_fullMeans.xlsx
+++ b/2015_2016/results/cer_fullMeans.xlsx
--- a/2015_2016/results/kmeansmtcars_s_cluster_m.csv
+++ b/2015_2016/results/kmeansmtcars_s_cluster_m.csv
+"","Group.1","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb","kmeansmtcars_s.cluster"
+"1",1,1.37396299086371,-1.22485776671113,-1.13702888616628,-0.96431308790515,1.0324123465762,-1.21514154165089,0.476372154864502,1.11603571371427,1.18990141802394,0.617178956175256,-0.856815620724337,1
+"2",2,-0.778275332632758,1.01488214956065,0.768752050345354,2.22879385543506,0.530108045969453,0.156113074814239,-1.84602953992279,-0.868027777333319,1.18990141802394,1.77892757956397,1.97343984902315,2
+"3",3,0.108219307221515,-0.584932076347762,-0.448670125258607,-0.649690478806049,-0.0496793648372699,-0.0234698870950564,1.18548413533828,1.11603571371427,-0.814143075490065,-0.157320126083889,-0.414588203576292,3
+"4",4,-0.836347842462784,1.01488214956065,1.02385129085922,0.692490960673007,-0.889747683183571,0.906358615479071,-0.3952279992635,-0.868027777333319,-0.814143075490065,-0.931819208343033,0.167677895668634,4
+"5",5,0.304362172055582,-0.384955298109211,-0.680956627756797,-0.367363567991955,0.684406308522856,-0.62956238353893,-0.799549740102975,-0.868027777333319,1.18990141802394,1.10124088258722,0.735203081008625,5
--- a/2015_2016/results/kmeansmtcars_s_table.csv
+++ b/2015_2016/results/kmeansmtcars_s_table.csv
+Car,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb,kmeansmtcars_s.cluster,,,,
Mazda RX4,0.150884825,-0.104987809,-0.570619819,-0.53509284,0.567513685,-0.610399567,-0.777165145,-0.868027777,1.189901418,0.423554186,0.735203081,5,,,,
Mazda RX4 Wag,0.150884825,-0.104987809,-0.570619819,-0.53509284,0.567513685,-0.349785269,-0.46378082,-0.868027777,1.189901418,0.423554186,0.735203081,5,,,Count of kmeansmtcars_s.cluster,
Datsun 710,0.449543447,-1.224857767,-0.990182091,-0.783040459,0.473999587,-0.917004624,0.426006817,1.116035714,1.189901418,0.423554186,-1.122152071,1,,,Row Labels,Total
Hornet 4 Drive,0.217253407,-0.104987809,0.220093694,-0.53509284,-0.96611753,-0.002299538,0.890487156,1.116035714,-0.814143075,-0.931819208,-1.122152071,3,,,1,7
Hornet Sportabout,-0.230734526,1.01488215,1.043081228,0.412942174,-0.835197792,0.227654255,-0.46378082,-0.868027777,-0.814143075,-0.931819208,-0.503033687,4,,,2,2
Valiant,-0.3302874,-0.104987809,-0.046166978,-0.60801861,-1.564607761,0.248094592,1.326986752,1.116035714,-0.814143075,-0.931819208,-1.122152071,3,,,3,7
Duster 360,-0.960788935,1.01488215,1.043081228,1.433902959,-0.722980874,0.360516446,-1.124126363,-0.868027777,-0.814143075,-0.931819208,0.735203081,4,,,4,12
Merc 240D,0.715017777,-1.224857767,-0.677930938,-1.235180235,0.174754472,-0.027849959,1.203871481,1.116035714,-0.814143075,0.423554186,-0.503033687,3,,,5,4
Merc 230,0.449543447,-1.224857767,-0.725535119,-0.753870151,0.604919325,-0.068730634,2.826754593,1.116035714,-0.814143075,0.423554186,-0.503033687,3,,,Grand Total,32
Merc 280,-0.147773797,-0.104987809,-0.509299179,-0.345485837,0.604919325,0.227654255,0.252526208,1.116035714,-0.814143075,0.423554186,0.735203081,3,,,,
Merc 280C,-0.380063837,-0.104987809,-0.509299179,-0.345485837,0.604919325,0.227654255,0.588295128,1.116035714,-0.814143075,0.423554186,0.735203081,3,,,,
Merc 450SE,-0.612353876,1.01488215,0.363713088,0.485867945,-0.98482035,0.871524874,-0.251127171,-0.868027777,-0.814143075,-0.931819208,0.116084697,4,,,,
Merc 450SL,-0.463024565,1.01488215,0.363713088,0.485867945,-0.98482035,0.524039143,-0.139204198,-0.868027777,-0.814143075,-0.931819208,0.116084697,4,,,,
Merc 450SLC,-0.811459624,1.01488215,0.363713088,0.485867945,-0.98482035,0.575139986,0.084641749,-0.868027777,-0.814143075,-0.931819208,0.116084697,4,,,,
Cadillac Fleetwood,-1.607882616,1.01488215,1.946753815,0.850496796,-1.246659826,2.077504765,0.073449451,-0.868027777,-0.814143075,-0.931819208,0.735203081,4,,,,
Lincoln Continental,-1.607882616,1.01488215,1.849931752,0.996348337,-1.115740088,2.255335698,-0.016088927,-0.868027777,-0.814143075,-0.931819208,0.735203081,4,,,,
Chrysler Imperial,-0.894420352,1.01488215,1.688561647,1.215125648,-0.685575235,2.174596366,-0.239934874,-0.868027777,-0.814143075,-0.931819208,0.735203081,4,,,,
Fiat 128,2.042389431,-1.224857767,-1.226589294,-1.176839619,0.90416444,-1.039646647,0.907275602,1.116035714,1.189901418,0.423554186,-1.122152071,1,,,,
Honda Civic,1.710546517,-1.224857767,-1.25079481,-1.381031775,2.493904115,-1.637526508,0.375641479,1.116035714,1.189901418,0.423554186,-0.503033687,1,,,,
Toyota Corolla,2.291271616,-1.224857767,-1.287909934,-1.191424773,1.166003916,-1.4126828,1.147909994,1.116035714,1.189901418,0.423554186,-1.122152071,1,,,,
Toyota Corona,0.233845553,-1.224857767,-0.892553178,-0.724699843,0.193457291,-0.76881218,1.20946763,1.116035714,-0.814143075,-0.931819208,-1.122152071,3,,,,
Dodge Challenger,-0.761683187,1.01488215,0.704204008,0.048313323,-1.564607761,0.309415603,-0.54772305,-0.868027777,-0.814143075,-0.931819208,-0.503033687,4,,,,
AMC Javelin,-0.811459624,1.01488215,0.591244935,0.048313323,-0.835197792,0.22254417,-0.307088658,-0.868027777,-0.814143075,-0.931819208,-0.503033687,4,,,,
Camaro Z28,-1.126710392,1.01488215,0.962396176,1.433902959,0.24956575,0.636460997,-1.364760755,-0.868027777,-0.814143075,-0.931819208,0.735203081,4,,,,
Pontiac Firebird,-0.147773797,1.01488215,1.365821438,0.412942174,-0.96611753,0.641571082,-0.446992374,-0.868027777,-0.814143075,-0.931819208,-0.503033687,4,,,,
Fiat X1-9,1.196190002,-1.224857767,-1.224168743,-1.176839619,0.90416444,-1.310481114,0.588295128,1.116035714,1.189901418,0.423554186,-1.122152071,1,,,,
Porsche 914-2,0.980492108,-1.224857767,-0.890939476,-0.812210767,1.55876313,-1.100967659,-0.642857578,-0.868027777,1.189901418,1.77892758,-0.503033687,5,,,,
Lotus Europa,1.710546517,-1.224857767,-1.094265808,-0.491337378,0.324377029,-1.741772228,-0.530934604,1.116035714,1.189901418,1.77892758,-0.503033687,1,,,,
Ford Pantera L,-0.71190675,1.01488215,0.970464681,1.711020886,1.166003916,-0.048290296,-1.874010283,-0.868027777,1.189901418,1.77892758,0.735203081,2,,,,
Ferrari Dino,-0.064813069,-0.104987809,-0.691647397,0.412942174,0.043834734,-0.457097039,-1.314395417,-0.868027777,1.189901418,1.77892758,1.973439849,5,,,,
Maserati Bora,-0.844643915,1.01488215,0.567039419,2.746566825,-0.105787824,0.360516446,-1.818048797,-0.868027777,1.189901418,1.77892758,3.211676617,2,,,,
Volvo 142E,0.217253407,-1.224857767,-0.885291523,-0.549677994,0.960272899,-0.44687687,0.420410668,1.116035714,1.189901418,0.423554186,-0.503033687,1,,,,
\ No newline at end of file
--- a/2015_2016/results/mtcars.csv
+++ b/2015_2016/results/mtcars.csv
+"","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
+"Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4
+"Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4
+"Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
+"Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
+"Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
+"Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
+"Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
+"Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
+"Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
+"Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
+"Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
+"Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
+"Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
+"Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
+"Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
+"Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4
+"Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
+"Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
+"Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
+"Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
+"Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
+"Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
+"AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
+"Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
+"Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
+"Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
+"Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
+"Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
+"Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
+"Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
+"Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8
+"Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2
--- a/2015_2016/results/mtcarsMDSfit2_points.csv
+++ b/2015_2016/results/mtcarsMDSfit2_points.csv
+"","V1","V2"
+"Mazda RX4",-0.646862741991579,-1.708114157382
+"Mazda RX4 Wag",-0.619483146032673,-1.52562193917473
+"Datsun 710",-2.73562427480533,0.144150068596965
+"Hornet 4 Drive",-0.306860626843517,2.32580380903322
+"Hornet Sportabout",1.94339268444143,0.742521100774161
+"Valiant",-0.0552534228148635,2.74212285210061
+"Duster 360",2.9553851233226,-0.329613273076663
+"Merc 240D",-2.02295932440032,1.44210564884825
+"Merc 230",-2.25138395353752,1.95228789731055
+"Merc 280",-0.518091221728407,0.15946104182836
+"Merc 280C",-0.501186007879971,0.318793441307613
+"Merc 450SE",2.21240963394367,0.672709937189782
+"Merc 450SL",2.01557156931526,0.672460628132507
+"Merc 450SLC",2.11470473715854,0.789112883929225
+"Cadillac Fleetwood",3.83837251175931,0.814908706545935
+"Lincoln Continental",3.89184956261245,0.721831428268482
+"Chrysler Imperial",3.53638621577644,0.414502411607565
+"Fiat 128",-3.79555108306374,0.292078339457293
+"Honda Civic",-4.18703567835539,-0.677572148195182
+"Toyota Corolla",-4.16753593437767,0.274888954211298
+"Toyota Corona",-1.87417908700134,2.08645294508711
+"Dodge Challenger",2.1504414942378,0.99824422602129
+"AMC Javelin",1.83403697968866,0.892188609878278
+"Camaro Z28",2.84349575230407,-0.670103706296245
+"Pontiac Firebird",2.21054791484527,0.86005044739097
+"Fiat X1-9",-3.51768181338584,0.119294980251512
+"Porsche 914-2",-2.60950039650561,-2.01414254497138
+"Lotus Europa",-3.33238445122355,-1.35688767437804
+"Ford Pantera L",1.35133469570774,-3.44487804193946
+"Ferrari Dino",-0.000974330453736058,-3.16837497071242
+"Maserati Bora",2.62708976050828,-4.31070158074423
+"Volvo 142E",-2.38247114122046,-0.229960320900628
--- a/2015_2016/results/mtcarsMDSfit3_points.csv
+++ b/2015_2016/results/mtcarsMDSfit3_points.csv
+"","V1","V2","V3"
+"Mazda RX4",-0.646862741991579,-1.708114157382,-0.591730913752821
+"Mazda RX4 Wag",-0.619483146032673,-1.52562193917473,-0.376301265386768
+"Datsun 710",-2.73562427480533,0.144150068596965,-0.237439060356257
+"Hornet 4 Drive",-0.306860626843517,2.32580380903322,-0.13362125717306
+"Hornet Sportabout",1.94339268444143,0.742521100774161,-1.11653660016158
+"Valiant",-0.0552534228148635,2.74212285210061,0.161245576607979
+"Duster 360",2.9553851233226,-0.329613273076663,-0.357046141420443
+"Merc 240D",-2.02295932440032,1.44210564884825,0.929029456609806
+"Merc 230",-2.25138395353752,1.95228789731055,1.76893636971294
+"Merc 280",-0.518091221728407,0.15946104182836,1.4692603018852
+"Merc 280C",-0.501186007879971,0.318793441307613,1.65707006160945
+"Merc 450SE",2.21240963394367,0.672709937189782,-0.369470744036155
+"Merc 450SL",2.01557156931526,0.672460628132507,-0.476834146154911
+"Merc 450SLC",2.11470473715854,0.789112883929225,-0.290461986407637
+"Cadillac Fleetwood",3.83837251175931,0.814908706545935,0.637097197062243
+"Lincoln Continental",3.89184956261245,0.721831428268482,0.709261183724563
+"Chrysler Imperial",3.53638621577644,0.414502411607565,0.540246776077895
+"Fiat 128",-3.79555108306374,0.292078339457293,-0.416168080348116
+"Honda Civic",-4.18703567835539,-0.677572148195182,-0.203583064003279
+"Toyota Corolla",-4.16753593437767,0.274888954211298,-0.458912415137241
+"Toyota Corona",-1.87417908700134,2.08645294508711,0.154326541711034
+"Dodge Challenger",2.1504414942378,0.99824422602129,-1.15036394999768
+"AMC Javelin",1.83403697968866,0.892188609878278,-0.94728723351474
+"Camaro Z28",2.84349575230407,-0.670103706296245,-0.16055925208039
+"Pontiac Firebird",2.21054791484527,0.86005044739097,-1.027957684948
+"Fiat X1-9",-3.51768181338584,0.119294980251512,-0.446471584885807
+"Porsche 914-2",-2.60950039650561,-2.01414254497138,-0.81725188020575
+"Lotus Europa",-3.33238445122355,-1.35688767437804,-0.446716729755045
+"Ford Pantera L",1.35133469570774,-3.44487804193946,-0.134394339458656
+"Ferrari Dino",-0.000974330453736058,-3.16837497071242,0.395761012093747
+"Maserati Bora",2.62708976050828,-4.31070158074423,1.3315940460601
+"Volvo 142E",-2.38247114122046,-0.229960320900628,0.405279806029363
--- a/2015_2016/results/week8_gasModel1_results.csv
+++ b/2015_2016/results/week8_gasModel1_results.csv
+"","OddsRatio","50 %","50 %"
+"(Intercept)",111.369016649159,111.366455625941,111.371577672378
+"prop_age1930-1949",-3.20258403795005,-3.20385268397853,-3.20131539192157
+"prop_age1950-1966",-8.31445860947953,-8.31567988488476,-8.31323733407429
+"prop_age1967-1982",-9.59364692948673,-9.59486587322928,-9.59242798574418
+"prop_age1983-1995",-14.4241346148481,-14.4256760059026,-14.4225932237937
+"prop_age1996 onwards",-12.8764882420108,-12.878165846501,-12.8748106375205
+"prop_typeSemi-detached",-7.53218924791386,-7.53358430295424,-7.53079419287348
+"prop_typeEnd terrace",-12.2977381586256,-12.2994518556928,-12.2960244615585
+"prop_typeMid terrace",-19.4603626905966,-19.4618857870659,-19.4588395941274
+"prop_typeBungalow",-3.42968669693332,-3.43144332509886,-3.42793006876778
+"prop_typeFlat (incl. maisonette)",-28.1492344523041,-28.1513335737764,-28.1471353308318
+"floor_area_band51-100 m2",11.4046278707182,11.4026499466648,11.4066057947716
+"floor_area_band101-150 m2",31.3412367108227,31.3390737448121,31.3433996768333
+"floor_area_band> 151 m2",50.8782258941442,50.8756304919348,50.8808212963536
--- a/2015_2016/results/week8_gasModel2_results.csv
+++ b/2015_2016/results/week8_gasModel2_results.csv
+"","OddsRatio","50 %","50 %"
+"(Intercept)",113.976109816914,113.97374820059,113.978471433237
+"prop_age1930-1949",-2.99755501489621,-2.99866681919929,-2.99644321059312
+"prop_age1950-1966",-7.93247840246769,-7.93355106925767,-7.93140573567771
+"prop_age1967-1982",-9.3298898894968,-9.33096153983288,-9.32881823916071
+"prop_age1983-1995",-14.274678759709,-14.2760318296024,-14.2733256898157
+"prop_age1996 onwards",-12.4026770221799,-12.4041470104029,-12.4012070339568
+"prop_typeSemi-detached",-7.68077289594848,-7.68199283495863,-7.67955295693833
+"prop_typeEnd terrace",-12.3766541238562,-12.3781522971202,-12.3751559505922
+"prop_typeMid terrace",-19.6903895661003,-19.6917254540757,-19.6890536781248
+"prop_typeBungalow",-3.48139608649064,-3.48293153220956,-3.47986064077171
+"prop_typeFlat (incl. maisonette)",-29.8982625688398,-29.9002013139808,-29.8963238236988
+"floor_area_band51-100 m2",11.2995964218123,11.2978677320216,11.301325111603
+"floor_area_band101-150 m2",30.9938720057691,30.9919802003798,30.9957638111584
+"floor_area_band> 151 m2",50.4310549880891,50.4287845284008,50.4333254477773
+"loft_depth> 150 mm",-3.5620850012661,-3.56299880240246,-3.56117120012975
+"loft_depthUnknown",-0.532380213178095,-0.533555082898312,-0.531205343457879
--- a/2015_2016/results/week9_boilerModel1Results.csv
+++ b/2015_2016/results/week9_boilerModel1Results.csv
+"","","0.132 %","99.868 %"
+"(Intercept)",-4913.0155293699,-5429.67092674015,-4396.36013199964
+"boiler_yearf2004",795.544286303857,-3838.5452749491,5429.63384755683
+"boiler_yearf2005",546.66338455974,-2105.79707804992,3199.12384716941
+"boiler_yearf2006",-622.557182861713,-2734.76783287011,1489.65346714669
+"boiler_yearf2007",-202.241653256282,-2431.56874553648,2027.08543902393
+"boiler_yearf2008",370.278607709473,-2408.33324636389,3148.89046178285
+"boiler_yearf2009",-1768.36041055549,-3915.74428258874,379.023461477756
+"boiler_yearf2010",-909.689733839375,-2935.12936111473,1115.74989343599
+"boiler_yearf2011",-909.330109862095,-2805.05455115919,986.394331435012
+"boiler_yearf2012",-746.475331740926,-2588.92801916034,1095.9773556785
+"li_yearf2004",548.256454806045,-2152.36150511861,3248.87441473071
+"li_yearf2005",-782.557316525845,-4755.52889478449,3190.41426173282
+"li_yearf2006",-1214.38440723037,-4065.03293316442,1636.26411870369
+"li_yearf2007",-118.583865671197,-3107.07136330864,2869.90363196625
+"li_yearf2008",-1595.13584132707,-4228.7784590755,1038.50677642137
+"li_yearf2009",-934.894489174123,-3554.92201511556,1685.13303676733
+"li_yearf2010",-2781.33228708492,-5181.86072892754,-380.803845242283
+"li_yearf2011",-1747.54956406576,-3963.34040107085,468.241272939332
+"li_yearf2012",-745.733281843282,-2386.77686466358,895.310300977019