iVBORw0KGgoAAAANSUhEUgAAAXoAAAA0CAIAAAAL52RvAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnEAABJxAfPceo4AAAASdEVYdFNvZnR3YXJlAEdyZWVuc2hvdF5VCAUAAA0cSURBVHhe7Z3fjhXHEYf9Bs4bxG+A38B+A/sN7DdI3sBccYcSKXBhCQkuLPliibGEfcNeJGKvFiFhNgoKijFai+BVEPJCIrAlS+TbU72V2uo/02dn5uyedX1qjU7XVPd0D1O/qZ4zZ3nrTRAEwUoIuQmCYEWE3ARBsCJORm7+8uQ/Nx7tp0qwDjx58uTu3bu7u7upHgTLs2q5efzi5/e/+PatP31DSaZgHQi5CcazUrm59+zVbz79m2jNsnLz40+/nN/eQ6oQrGQKVkjITTCelcrNJ9s/qNYsJTdWp1iIJWuwQkJugvGsh9y8/emOtgq5ORFCboLxrIfc2FYhNydCyE0wnpCboIuQm2A8ITdBFyE3wXhCboIuQm6C8YTcBF2E3ATjGZCbH3/65Y/fPPt48/v3v/jWvjJDeffzh9jZe+/Zq+Q9xMnKDeM8v733+9v/Yi5s+fzloxdpXzBEyE0wngG5+eCr72yo18o71x4QwIMv4C0lN3+49290gYKu2VZoHEphy6AAXX3w3GmlFuz0gKom1xLs/euT/0pJpsXvMES5RLySdQG71L/ds6JNrHbzWftJpgp2hDO9BhlyE4xnQG4+2tx18dkohG5qVmEpubGe7fLen/+Z2mQQh06tigWfhi5YzUUE8URi1CIluS6wdk5gstZBDdX/t9f+nqxH3zb63e0nyZrh5thzxGMQchOMZ0BuuOty0RPPhBxiYQsWGw9S2okGraxzslawnu3SkJtzn/8jd5bi7ERsapNhnZl1nii5AVh5GpRgsIPkFCXrIr9TO6V2blEi67bW2c3+/j6HAD4kU5PXr1+LPyRTnU43RQbDIVJ9wbKd7O3tuU7EwjbVSxR9BluBjNmdPSzWKNUexH9Cxj4qdgoyodzkSlErNkQt7nBUbQrDZxeotX5ybXLFJRQ3Hu3bve3fvqMO1tmJhT0JrFjzFIwTrg6U2hTGM6vcEAk3b968fPnyBQPVzc1NF+0Cxq2trStXriTXQ7Ds7OwkpwzxuXXrVqo3efjwofi7KYuR0aZ6k9u3b4u/7eSzzz7DwjbVSxR9Fj1duH//fqqXkCOyTfUF0lCNUu1BTsLFixcbMscucb5z504y1RkrN+5yn1BulKUOobgwJlNIO45iFYd4Ttaj5HJDWofEMJJ7z16xzVWANZE6t1c3NoVxWRLQv+6luCUVx2XMuhdtSjtmYD65IX7kei3Ctc5Fn1wXcH1jTLsXEJbWgugUwyPtvnBhMEeAS5cuiXNRbmDwVKCJOqoJ5aYd/NPKDc6Dox10sJxZubE6Yh+IOIhYdaMUO3dyQ1TbB7pFXN6U65Fihenqg+fJanAnzY7QHWVwVGOYSW64JS4u7AOIBxtIHIskAvvGxkYyHdUa9lp/UiR6kL3FmJRWMBgbVgFrcjPYiUS+MKHcAHpaTPpADqrKIkgrZ1QYmzgU/3E5q7K3mFXpiWoooOXMyo19rlRLbQSrJowwWQ3WgW57otrlVkUdAZu80HNNlYpLKndaiiOfkDnkhmtULtbGHRu7DS1dQLmUR8FfFAfPZDoEo0pVYyKalUiC4zwXrVM/jXVNo5ORciPd1lZzk8sNSJ+sbZ3G6Rw716dwNuXm2KP64KvvktVg5SZf79SwGlHsFmx60lhzuenIksr2T4rUSKAmYQ65kaCCznuj3ktrkSNo/Dg5ECOxwZbgSdYMiS7cZHhuygddLAbAttGJ5GVs806KUuIo+mABPQlFsZOBufMj/rWTpqer8Y8roul6kGOhOE6GGpxNuXHf6SRrBTuqopocT27s19uUohzYFKw9L7du+vDrx7bac05GMrncaJZeu1HnSGrTc303wlXvycVwlb1yCOmkKDfaSTGGZWo48CHvpDg2R9EHC/BBtAxymRYJcKMS5+JQoUdu1IcZiUX/+WppZpGzKTdkCrZJslaYSW7QF21FyddT9gusxtMlgd7sUx5bJNmZm8nlRu/SndcrES7+PfKkj4RSfYFaGrdliWSJzFwp4KCLRScy/mN0UpQSR9EHC/CBI4ry5gscmZpTlkW7UXIDGxsb+Fy/fl2qPbPI6ZIbrvUvH704v73HTVXeo9XiXqI7JXJjBYLixuyK/XJnQrkBq3ocKFkPsXt7JMOdBykrWEYJk8uNBAZ0rqQ0KmphYymGkFjkc3F1IHdsdkkMS0S5KR90cbQT9+RCU5taJz2BWvTBAvJZH1Fp/AtyVt28Fu3Gyg3zkiPipk002elkQG4ev/j5483v3SXeKKdTbvrLtHLjXsCx79S43Me9blPDLakoK/sPLeaTm1QfQi/xWthY1NkOWCzyWXMTGzByA9dFVq4UcNDFYSd6FNuJtNKXUPJOilLiKPpggVSpPMmSs+pOUe5mKZ6rIpIzMioZXs8/hKMlN+T/tZ8a1coccuMegswqN8XntceWG7ArIPsFmZ3Uue73ZZzy9ryyPBXzyU3nHVKjoucqL4aQWFLlMKR1aSZNSFikCuLgpnzQRdaJphg9nYiFbaqXKPpggVRZII+9QfuXs+pOkfjUzpuMGdxMi+g3g5oDLkVVbrjfOq0hKtALCndUrnst1odqal/ieHLjWrUPIViBYNg61MGS2h9ljNzYwdvfSdg+88c6NZiLtpJC/2nfzMwnN519snwQ/x65kddhwWqZWFLFhJms5mq64Ia3aPH/TnRU4iZNND+CWrdsU71E0QcLpMoh7vH53HKjJ3apJ8RKVW7sD3/aL5uoG6UWscKJyM34FGCM3LgXcGTR5IydD1/cedAy69t9yuRyoxduj3wI8qwkf6EmR277NsuAxdGOhKusnohqWZi48JaYd1M+6OJoJ/JgGGeJ28FO9KCpXkJaOR8skCqHoKfySEVOy9xys5RzTlVu7AXdfk3Oep5CuaEk63EZIzdgm8uZtN/TN163sdgXAl1p/Lh0QiaXG5A4aby94pDAHrzWuc9Lz7pQEqRtqiwgVsUo/u6hda4UsHD3nehE2Dr/vBNRBJqkegkR1qJqpIpBhRuRXUu5IaTtBd2O8H7PlcmNazLyu5uRcmMf08jPsuyaqPNZr21CsuleLGK+yW825pAbiQ2oRYJD1YE7eePBgaqSXUmBGFPlEH384bQJOuUGdCIuH4G8E11/2TWXRUPayZ8YU+UoOgtZW6233CRrBeu5lNwcbxHRIzdu/P0PR4qMlBumaV/ns+oz+LqN4MRFUiQ7KsrcS6o55EbTEKj9nphAskKggU1cOTUBOrx+/bo45B2KPVUOkTFA3lu/3CzbiRjxz88nEoOdvblyHRy4IjcgfQrrLTftCO/3PIZwgAu2nnTAfc2cv/OyFCPlBtxrh1p6Xrdxz+x1ANitis29pJpDbkCjC1iMoBEcQuCzLE9cGGvyAojL1tYWqwm22LWrPFUB2ZUqBsQCUsXQLzewVCdMR4fKFHZ2dhYz3t3c3BQje11qA7IrVTI4uva5ZnLjnhQ0sgP31LOtIPbGTul8bOG0r7OVi/AxCc54uXFT0CJPjtvYo6Mvtkkx65mJmeQGiCtZAhQhXPPAYxkiTzdysNcWKeKQKh0sJTc1ip1AY9ZMIZ8yyN5UKaFasGZyA/bO+c61B3lgIEn5G4DcY+XvB5NQ5BGeP+/EU3omH5EXl/NFgVM0iv5dZLb9rXArLt8ahxbGyw3kP0E41/G6jROU/BmNHRtJkJyWOZhPbgQyFFISgpNbNPHGB5ZR7cPRhBDa2NjAmS2fsaR9JXCDVOmAAeDvIn+SThSUkZGL7jBrPte0EnoOzRnIO5GGtZ4ZmzjUBmlZyjmnKjf5C6woiPwHBh9+/di++F8rxW+gaz/80TL4m+xiKS4l3NpNCp4yEflBhp1IbT0yidzkgxlMuNAOu4wqypNT1ZHLxgZzy03wa6AqN9zzB6WBgk9DC/J8obas0FIUqTwtyktyPUrtoUmtFNOfSeQmz7aKx7K4E1tLvpyQzbSkCrkJxlOVG+D6zl9jtYUMiJghkOzKS4t70KBwVy/6S6n9aZgbj/YbrRoLkzxNa5TigCeRG7D9DD6Bcs+50JS0o4T9Z5ppSRVyE4ynJTcC1z0SoJkOMU/YcAu1N2c+Ez/qw9VPkDcuevzpgW7pSjqkEFG1G7hQbMWBBr+rYiQMr6FWjLwxYAYmbdkOroAaMHI94uCYmalMUOaYrBU4b+pMaZ/G4xFyE4xnWG7OEsQhcY58SCGkWdzNkQvkcBTVGmQrWdeHkJtgPL8uuTlB7JpuMFs5hYTcBOMJuVkFLAPtd0yryaemJeQmGE/IzSpg4aZa896Ih80nSMhNMJ6Qm9m59+yVTW3aL16fWkJugvGE3MwIa6jz23tWa9Y0tYGQm2A8ITcTg6AgK+8e/YvxUt6u/8d1p5+Qm2A8ITcT4yRGC1ozx+swKyPkJhhPyM3EOJWR8tHm7vrmNULITTCekJuJ0fee2X6y/cPVB8/X8WvvnJcvXz59+jT/81FB0E/ITRAEKyLkJgiCFRFyEwTBSnjz5n+Bsg0wC+hiuAAAAABJRU5ErkJggg==
Weekly Challenge #44
Following their intial data preparation, the Leeds government would like you to use the advanced analytics tools inside Alteryx to answer several questions. Complete the following 3 steps to obtain the results at the accident level (Reference number):
1) What is the average number of vehicles involved in an accident?.
2) Which time bucket had the most accident?
3) How many accidents involved pedestrians?
Next, you have been asked to build a logistic regression to determine which factors affect whether a casualty has a severity of "fatal." Since there are relatively few casualties of this severity, please use the oversample tool to get a 50/50 split.
Hint: the name of the field you oversample cannot contain spaces
1) Which casuality class has the lowest p-value in the model? (Pr(>|z|))
What is the coefficient? (Estimate)
2) According to the model, which gender increases the likelihood of being a fatal casualty?
Hint: look at whether the "Sex of Casualtiy" variable is positive or negative.
_externals\1\Race1_Result.yxdb
Race1_Result
Race1_Result.yxdb
C:\Users\samma\AppData\Local\Temp\Engine_5600_746e107e8433477ea1654f0cc7407cff_\Engine_10812_d48a404d666145729669feb461fb4985_.yxdb
C:\Users\samma\AppData\Local\Temp\Engine_5600_746e107e8433477ea1654f0cc7407cff_\Engine_10812_765c72d816fd483387be78949d1d3a17_.yxdb
Single
Profile
Count - Descending
C:\Users\samma\AppData\Local\Temp\Engine_5600_746e107e8433477ea1654f0cc7407cff_\Engine_10812_f1fa45b61fe34a6b97afbf0a00fd2da4_.yxdb
Single
Profile
Simple
Casualty Class
=
Pedestrian
[Casualty Class] = "Pedestrian"
C:\Users\samma\AppData\Local\Temp\Engine_5600_746e107e8433477ea1654f0cc7407cff_\Engine_10812_e5b78477e66d4407900deab7a595ba9b_.yxdb
Single
Profile
Unique: Reference Number
Fatal
yes
50
Home
advanced
True
False
Logistic_Regression_234
Fatal
Number of Vehicles,Road Surface,Weather Conditions,Casualty Class,Sex of Casualty,Age of Casualty,Type of Vehicle,Lighting Conditions
False
False
0.5
False
False
5
lambda_1se
False
1
0.5
logit
False
5
3
False
False
1
1x
Logistic_Regression_234
C:\Users\samma\AppData\Local\Temp\Engine_5600_746e107e8433477ea1654f0cc7407cff_\Engine_10812_249bb5c333a64d30a260816efd869eb7_.yxdb
Single
Report
Model Build Details
Horizontal
challenge_44