diff --git a/data/Survey-Data/Identifier-Similarity-May-2-2023-11.52.csv b/data/Survey-Data/Identifier-Similarity-May-2-2023-11.52.csv new file mode 100644 index 0000000..1d46529 --- /dev/null +++ b/data/Survey-Data/Identifier-Similarity-May-2-2023-11.52.csv @@ -0,0 +1,42 @@ +StartDate,EndDate,Status,IPAddress,Progress,Duration,Finished,RecordedDate,ResponseId,RecipientLastName,RecipientFirstName,RecipientEmail,ExternalReference,LocationLatitude,LocationLongitude,DistributionChannel,UserLanguage,consentform,years_experience_1,years_experience_2,course_level_1,Q1_timing_First_Click,Q1_timing_Last_Click,Q1_timing_Page_Submit,Q1_timing_Click_Count,Q1_different,Q1_similar,Q2_timing_First_Click,Q2_timing_Last_Click,Q2_timing_Page_Submit,Q2_timing_Click_Count,Q2_different,Q2_similar,Q3_timing_First_Click,Q3_timing_Last_Click,Q3_timing_Page_Submit,Q3_timing_Click_Count,Q3_different,Q3_similar,Q4_timing_First_Click,Q4_timing_Last_Click,Q4_timing_Page_Submit,Q4_timing_Click_Count,Q4_different,Q4_similar,Q5_timing_First_Click,Q5_timing_Last_Click,Q5_timing_Page_Submit,Q5_timing_Click_Count,Q5_different,Q5_similar,Q6_timing_First_Click,Q6_timing_Last_Click,Q6_timing_Page_Submit,Q6_timing_Click_Count,Q6_different,Q6_similar,Q7_timing_First_Click,Q7_timing_Last_Click,Q7_timing_Page_Submit,Q7_timing_Click_Count,Q7_different,Q7_similar,Q37_First_Click,Q37_Last_Click,Q37_Page_Submit,Q37_Click_Count,Q8_different,Q8_similar,Q9_timing_First_Click,Q9_timing_Last_Click,Q9_timing_Page_Submit,Q9_timing_Click_Count,Q9_different,Q9_similar,Q10_timing_First_Click,Q10_timing_Last_Click,Q10_timing_Page_Submit,Q10_timing_Click_Count,Q10_different,Q10_similar,Q11_timing_First_Click,Q11_timing_Last_Click,Q11_timing_Page_Submit,Q11_timing_Click_Count,Q11_different,Q11_similar,Q12_timing_First_Click,Q12_timing_Last_Click,Q12_timing_Page_Submit,Q12_timing_Click_Count,Q12_different,Q12_similar,SC0,SC1,SC2,SC3,SC4,SC5,SC6,SC7,SC8 +2023-04-21_10:40:19,2023-04-21_10:56:07,0,137.146.139.12,100,947,1,2023-04-21_10:56:09,R_eYjuzquGDuybABP,,,,,44.5542,-69.6179,anonymous,EN,1,2,1,1,,,,,,,,,,,,1,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,,4,,,,,,1,,,,,,2,,,,,1,,,,,,1,,,,,,2,,6,3,3,1,1,0,1,2,1 +2023-04-21_11:33:09,2023-04-21_11:45:03,0,137.146.127.181,100,713,1,2023-04-21_11:45:03,R_1rctUZIH1e8qIWd,,,,,44.5542,-69.6179,anonymous,EN,1,3,3,3,,,,,2,,,,,,,,,,,,,,,,,,,4,,,,,,,,,,,1,,,,,,,4,,,,,1,,,,,,2,,,,,,,1,,,,,,,,,,,2,,4,3,1,0,0,2,0,1,1 +2023-04-21_12:11:42,2023-04-21_12:27:07,0,137.146.136.78,100,925,1,2023-04-21_12:27:08,R_24ra8HrwTNua7hw,,,,,44.5542,-69.6179,anonymous,EN,1,0,0,5,,,,,,,,,,,,1,,,,,4,,,,,,,,,,,,,4,,,,,,1,,,,,1,,,,,,2,,,,,,,,,,,,,,,,,,,1,,,,,1,,4,1,3,0,1,0,1,1,1 +2023-04-21_12:08:14,2023-04-21_12:35:31,0,137.146.133.53,100,1637,1,2023-04-21_12:35:32,R_2EhnN1RDmpfNyAP,,,,,44.5542,-69.6179,anonymous,EN,1,1,0.5,1,,,,,,2,,,,,,,,,,,,,,,,,2,,,,,,,,,,,,1,,,,,,3,,,,,,1,,,,,,,2,,,,,,1,,,,,1,,,,,,2,,7,5,2,1,0,3,0,1,2 +2023-04-21_12:03:13,2023-04-21_12:57:31,0,137.146.140.31,100,3258,1,2023-04-21_12:57:33,R_1d0SqeKEZ7SjJuU,,,,,44.5542,-69.6179,anonymous,EN,1,2,2,3,,,,,4,,,,,,1,,,,,,,,,,,,,4,,,,,,,,,,,,1,,,,,,,,,,,,1,,,,,,2,,,,,,,,,,,,,,,,,,1,6,2,4,2,0,0,2,0,2 +2023-04-21_12:15:16,2023-04-21_14:24:01,0,137.146.133.143,100,7724,1,2023-04-21_14:24:02,R_D6PdD600aSWZoI1,,,,,42.6191,-70.8589,anonymous,EN,1,0,0,5,,,,,,2,,,,,3,,,,,,,1,,,,,1,,,,,,,,,,,,2,,,,,,1,,,,,,,,,,,,3,,,,,,,1,,,,,,,,,,,4,,1,0,1,0,0,0,0,0,1 +2023-04-21_11:03:12,2023-04-21_16:30:09,0,137.146.135.85,100,19617,1,2023-04-21_16:30:11,R_323Dc7EsOkuZv5O,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,1,,,,,2,,,,,,,1,,,,,,,,,,,1,,,,,,,1,,,,,4,,,,,,,,,,,,,,,,,,,2,,,,,1,,,,,,1,,,,,,,,4,2,2,0,1,0,0,2,1 +2023-04-21_15:22:40,2023-04-21_16:32:22,0,66.66.230.166,100,4181,1,2023-04-21_16:32:23,R_svv8XD6eSsmOQVj,,,,,44.3233,-69.7687,anonymous,EN,1,40,15,5,,,,,4,,,,,,,,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,3,,,,,,,1,,,,,,,,,,,,,,,,,,1,,,,,,,5,3,2,2,0,1,1,0,1 +2023-04-21_20:40:55,2023-04-21_20:43:17,0,174.242.70.100,100,141,1,2023-04-21_20:43:17,R_28BeywDIVglLKYx,,,,,42.3498,-71.0765,anonymous,EN,1,0,0,5,,,,,,,,,,,1,,,,,,3,,,,,,,,,,,,1,,,,,,,2,,,,,,,,,,,,1,,,,,,2,,,,,,,,,,,2,,,,,,,4,3,1,2,1,0,0,1,0,1 +2023-04-23_08:16:07,2023-04-23_08:31:16,0,216.195.188.224,100,909,1,2023-04-23_08:31:17,R_sSyMlISU8KGjDzz,,,,,43.7996,-70.1779,anonymous,EN,1,1.5,1.5,3,,,,,1,,,,,,1,,,,,,,,,,,,,,,,,,,2,,,,,,,,,,,,3,,,,,,,,,,,,4,,,,,,1,,,,,,,,,,,,2,4,1,3,1,0,0,2,0,1 +2023-04-23_13:25:41,2023-04-23_14:18:06,0,137.146.157.118,100,3145,1,2023-04-23_14:18:07,R_2bUyxxxYhPJizxq,,,,,44.5542,-69.6179,anonymous,EN,1,2.5,1,1,,,,,,4,,,,,1,,,,,,4,,,,,,,2,,,,,,,,,,,1,,,,,,3,,,,,,,4,,,,,,3,,,,,,4,,,,,2,,,,,,,2,5,3,2,1,2,2,0,0,0 +2023-04-23_14:05:44,2023-04-23_14:21:40,0,137.146.157.118,100,955,1,2023-04-23_14:21:40,R_3g7685t7e87GPX1,,,,,44.5542,-69.6179,anonymous,EN,1,8,3,4,,,,,,,,,,,,2,,,,,,,,,,,,,,,,,,2,,,,,1,,,,,,,3,,,,,,,,,,,,,,,,,,1,,,,,1,,,,,,,,5,2,3,0,0,1,2,1,1 +2023-04-23_14:44:01,2023-04-23_14:45:05,1,,100,63,1,2023-04-23_14:45:06,R_3kbomm9DNbFFyJW,,,,,44.5542,-69.6179,preview,EN,1,3,2,1,1.048,1.048,2.225,1,,3,2.569,2.569,4.664,1,3,,1.225,1.225,2.398,1,,2,1.75,1.75,3.802,1,,2,1.545,1.545,2.478,1,,3,5.925,5.925,7.258,1,4,,1.894,1.894,2.841,1,1,,0.872,0.872,1.649,1,,4,1.811,1.811,2.72,1,1,,1.657,1.657,2.6,1,4,,1.69,1.69,2.764,1,,1,1.183,1.183,2.2,1,,2,3,0,3,0,2,0,0,0,1 +2023-04-23_14:51:57,2023-04-23_15:23:12,0,73.253.123.84,100,1875,1,2023-04-23_15:23:13,R_2cvLutA5QfDRgoa,,,,,42.7826,-71.1029,anonymous,EN,1,7,7,5,,,,,,4,,,,,,,,,,,,4,,,,,2,,,,,,,2,,,,,1,,,,,,3,,,,,,1,,,,,,,2,,,,,1,,,,,,1,,,,,,,1,10,6,4,1,1,3,1,2,2 +2023-04-23_20:22:01,2023-04-23_20:42:28,0,137.146.123.126,100,1226,1,2023-04-23_20:42:29,R_1P1Ns55uN84IlMj,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,1,,,,,,,,,,,1,,,,,,2,,,,,,2,,,,,,,2,,,,,,,,,,,,,,,,,1,,,,,,2,,,,,,,1,,,,,1,,,,,,2,,8,6,2,3,0,1,1,2,1 +2023-04-23_20:42:59,2023-04-23_21:07:04,0,174.242.67.221,100,1444,1,2023-04-23_21:07:05,R_1FM67wiwx8stTRP,,,,,42.9948,-71.458,anonymous,EN,1,3,2,2,,,,,,1,,,,,,1,,,,,,,,,,,4,,,,,,,2,,,,,,2,,,,,3,,,,,,,,,,,,3,,,,,,,,,,,,1,,,,,,2,,4,2,2,0,1,1,1,1,0 +2023-04-23_21:32:11,2023-04-23_21:45:36,0,137.146.126.190,100,804,1,2023-04-23_21:45:37,R_6KUHKsByMwKj2G5,,,,,44.5542,-69.6179,anonymous,EN,1,4,4,4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,3,,,,,,1,,,,,,4,,,,,,,,,,,,,,,,,,,3,1,2,0,0,1,2,0,0 +2023-04-23_22:23:23,2023-04-23_23:24:47,0,137.146.124.230,100,3684,1,2023-04-23_23:24:47,R_23ZSoOqAY54fhnw,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,3,,,,,,,,,,,,,,,,,2,,,,,,,2,,,,,,1,,,,,1,,,,,,,1,,,,,,1,,,,,,4,,,,,,1,,,,,,2,,,,,2,,5,2,3,1,1,1,1,0,1 +2023-04-26_00:21:02,2023-04-26_01:03:52,0,137.146.122.160,100,2569,1,2023-04-26_01:03:53,R_2uUk9KDkBsooqoA,,,,,42.4947,-70.8499,anonymous,EN,1,5,4,3,101.282,111.923,112.574,2,4,,100.856,169.436,175.265,2,1,,75.502,75.502,77.656,1,2,,101.558,389.377,390.652,11,,2,1.369,438.373,439.156,10,2,,100.867,302.909,304.448,6,,1,49.774,50.541,51.408,2,3,,102.053,150.462,151.166,2,1,,100.364,394.404,400.075,10,,2,82.089,82.089,86.205,1,,1,100.818,153.434,157.663,2,1,,100.915,125.352,125.823,3,2,,11,7,4,3,1,3,1,1,2 +2023-04-21_10:58:21,2023-04-21_10:58:45,0,137.146.188.253,2,23,0,2023-04-28_10:58:48,R_1zEPM20r98zwAFj,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-21_11:52:44,2023-04-21_11:53:08,0,137.146.142.17,2,23,0,2023-04-28_12:02:14,R_Om6E1stqrie3c4h,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-21_12:23:37,2023-04-21_12:24:20,0,137.146.132.179,10,43,0,2023-04-28_12:53:40,R_vkxqlHzWb5RFchb,,,,,,,anonymous,EN,1,1,0,5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-21_10:43:13,2023-04-21_13:28:58,0,137.146.132.74,17,9945,0,2023-04-28_14:03:59,R_2SjDoZXP7aQRPMH,,,,,,,anonymous,EN,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,0,0,1,0,0 +2023-04-21_14:11:23,2023-04-21_14:26:19,0,137.146.98.225,10,895,0,2023-04-28_14:49:24,R_Rt59bg2gAf6aDYJ,,,,,,,anonymous,EN,1,10,5,5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-21_15:39:23,2023-04-21_15:40:13,0,137.146.135.149,46,49,0,2023-04-28_15:40:14,R_1LO77W6639RZhw2,,,,,,,anonymous,EN,1,1,1,5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,0,0,0,1,0,0,0 +2023-04-21_16:08:20,2023-04-21_16:12:08,0,137.146.139.156,17,228,0,2023-04-28_16:12:16,R_3IQPeyvRCoA9r6t,,,,,,,anonymous,EN,1,1,0,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,,0,0,0,0,0,0,0,0,0 +2023-04-21_17:09:58,2023-04-21_17:11:29,0,137.146.141.34,17,90,0,2023-04-28_17:14:49,R_1Oq6sNNhQUOFs9E,,,,,,,anonymous,EN,1,3.5,3,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-21_18:41:29,2023-04-21_18:43:00,0,137.146.120.13,10,90,0,2023-04-28_18:43:02,R_1eyLw8stx4yY2TI,,,,,,,anonymous,EN,1,1,0.5,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-28_18:58:58,2023-04-28_20:26:35,0,137.146.128.109,100,5257,1,2023-04-28_20:26:37,R_1d7hcLb7u4xGIkt,,,,,44.5542,-69.6179,anonymous,EN,1,6,6,4,56.411,56.411,99.103,1,4,,136.628,136.628,149.409,1,,1,128.999,128.999,146.747,1,,2,369.407,371.651,373.171,3,,2,314.803,314.803,539.935,1,2,,58.928,58.928,105.71,1,1,,54.274,54.274,65.546,1,,3,47.475,47.475,53.23,1,,1,340.64,375.718,398.842,2,2,,77.12,77.12,103.427,1,1,,304.265,304.265,384.601,1,1,,9.123,152.711,169.483,2,2,,11,6,5,1,3,2,2,3,0 +2023-04-22_12:35:05,2023-04-22_12:37:35,0,2.136.98.244,24,149,0,2023-04-29_12:37:42,R_sXMS13MNdsQxDe9,,,,,,,anonymous,EN,1,3,3,3,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-23_16:30:49,2023-04-23_16:31:45,0,137.146.132.254,17,56,0,2023-04-30_16:31:48,R_21EScIS8Bj0fL1n,,,,,,,anonymous,EN,1,1.5,1,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-23_20:05:07,2023-04-23_20:05:46,0,137.146.131.16,10,39,0,2023-04-30_20:05:47,R_1iqun3PAVpddnEf,,,,,,,anonymous,EN,1,1,0.5,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-23_20:02:03,2023-04-23_20:07:10,0,137.146.123.6,24,307,0,2023-04-30_20:07:14,R_3HYPFwgPc83XWwP,,,,,,,anonymous,EN,1,3,2,5,,,,,,,,,,,4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-23_20:06:52,2023-04-23_20:08:10,0,172.101.55.140,24,77,0,2023-04-30_20:08:10,R_27wfjCSYtgT245n,,,,,,,anonymous,EN,1,12,4,4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +2023-04-23_20:23:05,2023-04-23_20:24:29,0,137.146.134.185,17,83,0,2023-04-30_20:24:31,R_2fHghSyZhoV7X6a,,,,,,,anonymous,EN,1,4,4,2,,,,,,,,,,,,,,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,1,0,0,0,0 +2023-04-23_20:32:20,2023-04-23_20:34:18,0,137.146.132.3,17,118,0,2023-04-30_20:34:26,R_2Bnip3TK9VPchKW,,,,,,,anonymous,EN,1,2,1,1,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,1,0,0,0,0 +2023-04-23_20:32:45,2023-04-23_20:34:50,0,137.146.132.234,17,125,0,2023-04-30_20:34:53,R_a3QdVl8v9vmv7O1,,,,,,,anonymous,EN,1,19,19,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,1,1,0,0,0,0,0,1,0 +2023-04-23_21:52:31,2023-04-23_21:55:24,0,137.146.133.72,32,173,0,2023-04-30_21:55:32,R_2z5SIxrhubdLDqS,,,,,,,anonymous,EN,1,0.5,0.25,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,1,0,1,0,0,0,0,0,1 +2023-04-24_06:02:05,2023-04-24_06:02:18,0,66.66.230.166,2,12,0,2023-05-01_06:02:20,R_3kLYdEsIVyZYOOO,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 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+57,2.0,2.0,3.0,1.0,,1.0,,,,,0.0,,,,1.0,,,,1.0,,1.0,,,,,,1.0 +58,1.0,0.5,1.0,,0.0,,,,,1.0,,,,1.0,,1.0,,1.0,,,1.0,,1.0,1.0,,0.0, +59,0.0,0.0,5.0,,,,1.0,0.0,,,,,0.0,,1.0,0.0,,0.0,,,,,,,1.0,1.0, +60,1.0,0.0,5.0,,,,,,,,,,,,,,,,,,,,,,,, +61,,,,,,,,,,,,,,,,,,,,,,,,,,, +62,3.0,3.0,3.0,0.0,,,,,,,0.0,,,1.0,,,0.0,1.0,,1.0,,,1.0,,,0.0, +63,,,,,,,,,,,,,,,,,,,,,,,,,,, +64,2.0,1.0,1.0,,,,1.0,1.0,,,,,,,,,0.0,,1.0,,1.0,1.0,,1.0,,0.0, diff --git a/data/Survey-Data/survey_031023_1344.csv b/data/Survey-Data/survey_031023_1344.csv new file mode 100644 index 0000000..89c161f --- /dev/null +++ b/data/Survey-Data/survey_031023_1344.csv @@ -0,0 +1,66 @@ +StartDate,EndDate,Status,IPAddress,Progress,Duration (in seconds),Finished,RecordedDate,ResponseId,RecipientLastName,RecipientFirstName,RecipientEmail,ExternalReference,LocationLatitude,LocationLongitude,DistributionChannel,UserLanguage,consent form,years_experience_1,years_experience_2,course_level_1,Q1_timing_First Click,Q1_timing_Last Click,Q1_timing_Page Submit,Q1_timing_Click Count,Q1_different,Q1_similar,Q2_timing_First Click,Q2_timing_Last Click,Q2_timing_Page Submit,Q2_timing_Click Count,Q2_different,Q2_similar,Q3_timing_First Click,Q3_timing_Last Click,Q3_timing_Page Submit,Q3_timing_Click Count,Q3_different,Q3_similar,Q4_timing_First Click,Q4_timing_Last Click,Q4_timing_Page Submit,Q4_timing_Click Count,Q4_different,Q4_similar,Q5_timing_First Click,Q5_timing_Last Click,Q5_timing_Page Submit,Q5_timing_Click Count,Q5_different,Q5_similar,Q6_timing_First Click,Q6_timing_Last Click,Q6_timing_Page Submit,Q6_timing_Click Count,Q6_different,Q6_similar,Q7_timing_First Click,Q7_timing_Last Click,Q7_timing_Page Submit,Q7_timing_Click Count,Q7_different,Q7_similar,Q37_First Click,Q37_Last Click,Q37_Page Submit,Q37_Click Count,Q8_different,Q8_similar,Q9_timing_First Click,Q9_timing_Last Click,Q9_timing_Page Submit,Q9_timing_Click Count,Q9_different,Q9_similar,Q10_timing_First Click,Q10_timing_Last Click,Q10_timing_Page Submit,Q10_timing_Click Count,Q10_different,Q10_similar,Q11_timing_First Click,Q11_timing_Last Click,Q11_timing_Page Submit,Q11_timing_Click Count,Q11_different,Q11_similar,Q12_timing_First Click,Q12_timing_Last Click,Q12_timing_Page Submit,Q12_timing_Click Count,Q12_different,Q12_similar,SC0,SC1,SC2,SC3,SC4,SC5,SC6,SC7,SC8 +5/2/23 10:04,5/2/23 11:04,0,137.146.130.49,2,3638,0,5/9/23 11:11,R_2DSVqMOyntoR0Lf,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +5/2/23 09:57,5/2/23 10:04,0,137.146.130.49,100,393,1,5/2/23 10:04,R_1H2m7tpDgB5ANXy,,,,,44.5542,-69.6179,anonymous,EN,1,6,6,4,3.177,3.177,4.664,1,,4,2.13,2.13,3.397,1,4,,2.348,2.348,4.688,1,,2,2.605,2.605,3.671,1,4,,2.075,2.075,3.119,1,,4,4.019,4.019,5.192,1,,1,1.516,1.516,2.848,1,,1,7.052,7.652,9.226,2,3,,1.994,1.994,3.695,1,3,,5.14,5.14,6.214,1,,1,21.334,21.334,23.021,1,,1,2.77,2.77,4.318,1,,4,5,0,5,0,2,0,1,0,2 +4/19/23 12:09,5/2/23 09:57,0,137.146.130.49,100,1115273,1,5/2/23 09:57,R_1LcZcm1IflNJVee,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,1,,,,,4,,,,,,2,,,,,,2,,,,,,2,,,,,,,4,,,,,,,,,,,,,,,,,,3,,,,,,,,,,,1,,,,,,,1,,,,,,4,5,4,1,3,0,0,0,1,1 +4/29/23 10:12,4/29/23 10:13,0,137.146.248.238,10,49,0,5/6/23 10:13,R_1IFA32CPNQ1Tz0j,,,,,,,anonymous,EN,1,2,2,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/26/23 14:17,4/28/23 22:01,0,73.107.244.15,54,200639,0,5/5/23 22:01,R_3IYpcvIpD57042v,,,,,,,anonymous,EN,1,6,4,4,,,,,,,,,,,,,,,,,,,12.572,252.365,253.156,38,,4,1.031,147.922,149.356,22,1,,,,,,,,,,,,,,14.772,115.692,116.549,12,,3,2.535,177.575,178.22,40,,3,0.605,95.659,96.801,10,,4,,,,,,,0.903,113.742,113.775,29,2,,0,0,0,0,0,0,0,0,0 +4/28/23 19:47,4/28/23 19:56,0,137.146.132.52,24,535,0,5/5/23 19:56,R_3fpbdwoFpafdERJ,,,,,,,anonymous,EN,1,1.5,0.5,1,,,,,,,,,,,,,,,,,,,3.346,382.392,382.423,14,1,,,,,,,,,,,,,,,,,,,,1.506,85.498,86.225,5,1,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,0,0,0,1,0,0,0 +4/28/23 19:06,4/28/23 19:08,0,137.146.125.178,10,131,0,5/5/23 19:08,R_1mOUoPT7e3hESjr,,,,,,,anonymous,EN,1,1,1,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/28/23 16:58,4/28/23 18:26,0,137.146.128.109,100,5257,1,4/28/23 18:26,R_1d7hcLb7u4xGIkt,,,,,44.5542,-69.6179,anonymous,EN,1,6,6,4,56.411,56.411,99.103,1,4,,136.628,136.628,149.409,1,,1,128.999,128.999,146.747,1,,2,369.407,371.651,373.171,3,,2,314.803,314.803,539.935,1,2,,58.928,58.928,105.71,1,1,,54.274,54.274,65.546,1,,3,47.475,47.475,53.23,1,,1,340.64,375.718,398.842,2,2,,77.12,77.12,103.427,1,1,,304.265,304.265,384.601,1,1,,9.123,152.711,169.483,2,2,,11,6,5,1,3,2,2,3,0 +4/28/23 14:08,4/28/23 14:11,0,137.146.138.47,10,179,0,5/5/23 14:11,R_3M38ScC4R4rYkjg,,,,,,,anonymous,EN,1,2,1.5,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/28/23 12:09,4/28/23 12:50,0,137.146.127.178,17,2499,0,5/5/23 12:50,R_2fv43vtIpbbflOH,,,,,,,anonymous,EN,1,4,2,2,,,,,,,132.619,132.619,133.8,1,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,1,0,0,0,0 +4/23/23 19:12,4/28/23 07:05,0,137.146.122.205,10,388401,0,5/5/23 07:05,R_PCCElk53fPyQN9L,,,,,,,anonymous,EN,1,2,2,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/26/23 08:39,4/27/23 18:35,0,137.146.130.226,39,122133,0,5/4/23 18:35,R_1iglbZOBnJDNorN,,,,,,,anonymous,EN,1,4,3,3,,,,,,,,,,,,,,,,,,,,,,,,,75.806,112.249,112.725,2,,3,,,,,,,93.578,93.578,95.468,1,3,,,,,,,,130.041,205.865,208.704,6,2,,73.744,73.744,89.565,1,1,,,,,,,,,,,,,,3,3,0,0,0,1,0,2,0 +4/23/23 09:18,4/27/23 12:53,0,137.146.122.204,24,358528,0,5/4/23 12:53,R_3MGrr9vEi26S7yN,,,,,,,anonymous,EN,1,2,1,2,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,1,0,1,1,0,0,0 +4/25/23 23:22,4/26/23 15:51,0,137.146.124.165,2,59363,0,5/3/23 15:51,R_1pyrFmUQruvcXx8,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 09:47,4/26/23 14:05,0,137.146.122.15,10,447497,0,5/3/23 14:05,R_3R4OBEsLPudzd90,,,,,,,anonymous,EN,1,3,3,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/26/23 08:21,4/26/23 09:03,0,137.146.143.26,17,2508,0,5/3/23 09:03,R_3fvNg91WEPMK93W,,,,,,,anonymous,EN,1,4,1,3,,,,,,,,,,,,,316.718,416.386,439.349,6,,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 18:28,4/26/23 08:00,0,137.146.130.70,17,221560,0,5/3/23 08:00,R_2c0couF7Fiz8Jsy,,,,,,,anonymous,EN,1,1,0.5,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/26/23 05:45,4/26/23 05:59,0,185.216.201.47,17,869,0,5/3/23 06:00,R_DMhABPhXnxOwlc5,,,,,,,anonymous,EN,1,4,3,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115.722,843.316,849.315,2,3,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/26/23 04:08,4/26/23 04:09,0,137.146.135.36,2,48,0,5/3/23 04:09,R_3MumxW8xl2Tvddv,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/25/23 22:21,4/25/23 23:03,0,137.146.122.160,100,2569,1,4/25/23 23:03,R_2uUk9KDkBsooqoA,,,,,42.4947,-70.8499,anonymous,EN,1,5,4,3,101.282,111.923,112.574,2,4,,100.856,169.436,175.265,2,1,,75.502,75.502,77.656,1,2,,101.558,389.377,390.652,11,,2,1.369,438.373,439.156,10,2,,100.867,302.909,304.448,6,,1,49.774,50.541,51.408,2,3,,102.053,150.462,151.166,2,1,,100.364,394.404,400.075,10,,2,82.089,82.089,86.205,1,,1,100.818,153.434,157.663,2,1,,100.915,125.352,125.823,3,2,,11,7,4,3,1,3,1,1,2 +4/25/23 22:15,4/25/23 22:16,0,137.146.143.37,2,23,0,5/2/23 22:16,R_1jZeOqyw9INhLIo,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 13:26,4/25/23 22:14,0,74.75.105.184,2,377306,0,5/2/23 22:14,R_0lGcJCLm9kGfTbj,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 18:23,4/25/23 22:13,0,137.146.143.32,10,186634,0,5/2/23 22:13,R_322SXmjzbbUATEp,,,,,,,anonymous,EN,1,5,5,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/24/23 00:33,4/25/23 22:12,0,104.28.39.23,61,164336,0,5/2/23 22:12,R_3lxWE0xVm02CX6x,,,,,,,anonymous,EN,1,1,1,1,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,,,,,,3,,,,,,,2,,,,,,,,,,,3,,,,,,,,2,1,1,0,0,1,0,0,1 +4/25/23 22:12,4/25/23 22:12,0,137.146.127.50,10,38,0,5/2/23 22:12,R_uwy97jLLg3acWtP,,,,,,,anonymous,EN,1,5,1,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/25/23 14:38,4/25/23 14:48,0,137.146.120.134,24,574,0,5/2/23 14:48,R_YPPzAGuGWVXf0sx,,,,,,,anonymous,EN,1,0.5,0.5,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,217.037,217.037,251.749,1,,2,,,,,,,,,,,,,129.746,129.746,131.79,1,2,,,,,,,,0,0,0,0,0,0,0,0,0 +4/24/23 15:12,4/24/23 15:13,0,137.146.126.79,10,70,0,5/1/23 15:13,R_1C48TWVZQr35T4P,,,,,,,anonymous,EN,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/24/23 08:15,4/24/23 08:18,0,137.146.127.61,10,196,0,5/1/23 08:18,R_1DI3T1Nc9KBDk5G,,,,,,,anonymous,EN,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/24/23 04:02,4/24/23 04:02,0,66.66.230.166,2,12,0,5/1/23 04:02,R_3kLYdEsIVyZYOOO,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 20:23,4/23/23 21:24,0,137.146.124.230,100,3684,1,4/23/23 21:24,R_23ZSoOqAY54fhnw,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,3,,,,,,,,,,,,,,,,,2,,,,,,,2,,,,,,1,,,,,1,,,,,,,1,,,,,,1,,,,,,4,,,,,,1,,,,,,2,,,,,2,,5,2,3,1,1,1,1,0,1 +4/23/23 19:52,4/23/23 19:55,0,137.146.133.72,32,173,0,4/30/23 19:55,R_2z5SIxrhubdLDqS,,,,,,,anonymous,EN,1,0.5,0.25,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,1,0,1,0,0,0,0,0,1 +4/23/23 19:32,4/23/23 19:45,0,137.146.126.190,100,804,1,4/23/23 19:45,R_6KUHKsByMwKj2G5,,,,,44.5542,-69.6179,anonymous,EN,1,4,4,4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,3,,,,,,1,,,,,,4,,,,,,,,,,,,,,,,,,,3,1,2,0,0,1,2,0,0 +4/23/23 18:42,4/23/23 19:07,0,174.242.67.221,100,1444,1,4/23/23 19:07,R_1FM67wiwx8stTRP,,,,,42.9948,-71.458,anonymous,EN,1,3,2,2,,,,,,1,,,,,,1,,,,,,,,,,,4,,,,,,,2,,,,,,2,,,,,3,,,,,,,,,,,,3,,,,,,,,,,,,1,,,,,,2,,4,2,2,0,1,1,1,1,0 +4/23/23 18:22,4/23/23 18:42,0,137.146.123.126,100,1226,1,4/23/23 18:42,R_1P1Ns55uN84IlMj,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,1,,,,,,,,,,,1,,,,,,2,,,,,,2,,,,,,,2,,,,,,,,,,,,,,,,,1,,,,,,2,,,,,,,1,,,,,1,,,,,,2,,8,6,2,3,0,1,1,2,1 +4/23/23 18:32,4/23/23 18:34,0,137.146.132.3,17,118,0,4/30/23 18:34,R_2Bnip3TK9VPchKW,,,,,,,anonymous,EN,1,2,1,1,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,1,0,0,0,0 +4/23/23 18:32,4/23/23 18:34,0,137.146.132.234,17,125,0,4/30/23 18:34,R_a3QdVl8v9vmv7O1,,,,,,,anonymous,EN,1,19,19,3,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,1,1,0,0,0,0,0,1,0 +4/23/23 18:23,4/23/23 18:24,0,137.146.134.185,17,83,0,4/30/23 18:24,R_2fHghSyZhoV7X6a,,,,,,,anonymous,EN,1,4,4,2,,,,,,,,,,,,,,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,1,0,0,0,0 +4/23/23 18:06,4/23/23 18:08,0,172.101.55.140,24,77,0,4/30/23 18:08,R_27wfjCSYtgT245n,,,,,,,anonymous,EN,1,12,4,4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 18:02,4/23/23 18:07,0,137.146.123.6,24,307,0,4/30/23 18:07,R_3HYPFwgPc83XWwP,,,,,,,anonymous,EN,1,3,2,5,,,,,,,,,,,4,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 18:05,4/23/23 18:05,0,137.146.131.16,10,39,0,4/30/23 18:05,R_1iqun3PAVpddnEf,,,,,,,anonymous,EN,1,1,0.5,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 14:30,4/23/23 14:31,0,137.146.132.254,17,56,0,4/30/23 14:31,R_21EScIS8Bj0fL1n,,,,,,,anonymous,EN,1,1.5,1,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/23/23 12:51,4/23/23 13:23,0,73.253.123.84,100,1875,1,4/23/23 13:23,R_2cvLutA5QfDRgoa,,,,,42.7826,-71.1029,anonymous,EN,1,7,7,5,,,,,,4,,,,,,,,,,,,4,,,,,2,,,,,,,2,,,,,1,,,,,,3,,,,,,1,,,,,,,2,,,,,1,,,,,,1,,,,,,,1,10,6,4,1,1,3,1,2,2 +4/23/23 12:44,4/23/23 12:45,1,,100,63,1,4/23/23 12:45,R_3kbomm9DNbFFyJW,,,,,44.5542,-69.6179,preview,EN,1,3,2,1,1.048,1.048,2.225,1,,3,2.569,2.569,4.664,1,3,,1.225,1.225,2.398,1,,2,1.75,1.75,3.802,1,,2,1.545,1.545,2.478,1,,3,5.925,5.925,7.258,1,4,,1.894,1.894,2.841,1,1,,0.872,0.872,1.649,1,,4,1.811,1.811,2.72,1,1,,1.657,1.657,2.6,1,4,,1.69,1.69,2.764,1,,1,1.183,1.183,2.2,1,,2,3,0,3,0,2,0,0,0,1 +4/23/23 12:05,4/23/23 12:21,0,137.146.157.118,100,955,1,4/23/23 12:21,R_3g7685t7e87GPX1,,,,,44.5542,-69.6179,anonymous,EN,1,8,3,4,,,,,,,,,,,,2,,,,,,,,,,,,,,,,,,2,,,,,1,,,,,,,3,,,,,,,,,,,,,,,,,,1,,,,,1,,,,,,,,5,2,3,0,0,1,2,1,1 +4/23/23 11:25,4/23/23 12:18,0,137.146.157.118,100,3145,1,4/23/23 12:18,R_2bUyxxxYhPJizxq,,,,,44.5542,-69.6179,anonymous,EN,1,2.5,1,1,,,,,,4,,,,,1,,,,,,4,,,,,,,2,,,,,,,,,,,1,,,,,,3,,,,,,,4,,,,,,3,,,,,,4,,,,,2,,,,,,,2,5,3,2,1,2,2,0,0,0 +4/23/23 06:16,4/23/23 06:31,0,216.195.188.224,100,909,1,4/23/23 06:31,R_sSyMlISU8KGjDzz,,,,,43.7996,-70.1779,anonymous,EN,1,1.5,1.5,3,,,,,1,,,,,,1,,,,,,,,,,,,,,,,,,,2,,,,,,,,,,,,3,,,,,,,,,,,,4,,,,,,1,,,,,,,,,,,,2,4,1,3,1,0,0,2,0,1 +4/22/23 10:35,4/22/23 10:37,0,2.136.98.244,24,149,0,4/29/23 10:37,R_sXMS13MNdsQxDe9,,,,,,,anonymous,EN,1,3,3,3,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 18:40,4/21/23 18:43,0,174.242.70.100,100,141,1,4/21/23 18:43,R_28BeywDIVglLKYx,,,,,42.3498,-71.0765,anonymous,EN,1,0,0,5,,,,,,,,,,,1,,,,,,3,,,,,,,,,,,,1,,,,,,,2,,,,,,,,,,,,1,,,,,,2,,,,,,,,,,,2,,,,,,,4,3,1,2,1,0,0,1,0,1 +4/21/23 16:41,4/21/23 16:43,0,137.146.120.13,10,90,0,4/28/23 16:43,R_1eyLw8stx4yY2TI,,,,,,,anonymous,EN,1,1,0.5,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 15:09,4/21/23 15:11,0,137.146.141.34,17,90,0,4/28/23 15:14,R_1Oq6sNNhQUOFs9E,,,,,,,anonymous,EN,1,3.5,3,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 13:22,4/21/23 14:32,0,66.66.230.166,100,4181,1,4/21/23 14:32,R_svv8XD6eSsmOQVj,,,,,44.3233,-69.7687,anonymous,EN,1,40,15,5,,,,,4,,,,,,,,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,3,,,,,,,1,,,,,,,,,,,,,,,,,,1,,,,,,,5,3,2,2,0,1,1,0,1 +4/21/23 09:03,4/21/23 14:30,0,137.146.135.85,100,19617,1,4/21/23 14:30,R_323Dc7EsOkuZv5O,,,,,44.5542,-69.6179,anonymous,EN,1,1,1,1,,,,,2,,,,,,,1,,,,,,,,,,,1,,,,,,,1,,,,,4,,,,,,,,,,,,,,,,,,,2,,,,,1,,,,,,1,,,,,,,,4,2,2,0,1,0,0,2,1 +4/21/23 14:08,4/21/23 14:12,0,137.146.139.156,17,228,0,4/28/23 14:12,R_3IQPeyvRCoA9r6t,,,,,,,anonymous,EN,1,1,0,2,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,,0,0,0,0,0,0,0,0,0 +4/21/23 13:39,4/21/23 13:40,0,137.146.135.149,46,49,0,4/28/23 13:40,R_1LO77W6639RZhw2,,,,,,,anonymous,EN,1,1,1,5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,0,0,0,1,0,0,0 +4/21/23 12:11,4/21/23 12:26,0,137.146.98.225,10,895,0,4/28/23 12:49,R_Rt59bg2gAf6aDYJ,,,,,,,anonymous,EN,1,10,5,5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 10:15,4/21/23 12:24,0,137.146.133.143,100,7724,1,4/21/23 12:24,R_D6PdD600aSWZoI1,,,,,42.6191,-70.8589,anonymous,EN,1,0,0,5,,,,,,2,,,,,3,,,,,,,1,,,,,1,,,,,,,,,,,,2,,,,,,1,,,,,,,,,,,,3,,,,,,,1,,,,,,,,,,,4,,1,0,1,0,0,0,0,0,1 +4/21/23 08:43,4/21/23 11:28,0,137.146.132.74,17,9945,0,4/28/23 12:03,R_2SjDoZXP7aQRPMH,,,,,,,anonymous,EN,1,1,1,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,1,0,1,0,0,0,1,0,0 +4/21/23 10:03,4/21/23 10:57,0,137.146.140.31,100,3258,1,4/21/23 10:57,R_1d0SqeKEZ7SjJuU,,,,,44.5542,-69.6179,anonymous,EN,1,2,2,3,,,,,4,,,,,,1,,,,,,,,,,,,,4,,,,,,,,,,,,1,,,,,,,,,,,,1,,,,,,2,,,,,,,,,,,,,,,,,,1,6,2,4,2,0,0,2,0,2 +4/21/23 10:08,4/21/23 10:35,0,137.146.133.53,100,1637,1,4/21/23 10:35,R_2EhnN1RDmpfNyAP,,,,,44.5542,-69.6179,anonymous,EN,1,1,0.5,1,,,,,,2,,,,,,,,,,,,,,,,,2,,,,,,,,,,,,1,,,,,,3,,,,,,1,,,,,,,2,,,,,,1,,,,,1,,,,,,2,,7,5,2,1,0,3,0,1,2 +4/21/23 10:11,4/21/23 10:27,0,137.146.136.78,100,925,1,4/21/23 10:27,R_24ra8HrwTNua7hw,,,,,44.5542,-69.6179,anonymous,EN,1,0,0,5,,,,,,,,,,,,1,,,,,4,,,,,,,,,,,,,4,,,,,,1,,,,,1,,,,,,2,,,,,,,,,,,,,,,,,,,1,,,,,1,,4,1,3,0,1,0,1,1,1 +4/21/23 10:23,4/21/23 10:24,0,137.146.132.179,10,43,0,4/28/23 10:53,R_vkxqlHzWb5RFchb,,,,,,,anonymous,EN,1,1,0,5,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 09:52,4/21/23 09:53,0,137.146.142.17,2,23,0,4/28/23 10:02,R_Om6E1stqrie3c4h,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 09:33,4/21/23 09:45,0,137.146.127.181,100,713,1,4/21/23 09:45,R_1rctUZIH1e8qIWd,,,,,44.5542,-69.6179,anonymous,EN,1,3,3,3,,,,,2,,,,,,,,,,,,,,,,,,,4,,,,,,,,,,,1,,,,,,,4,,,,,1,,,,,,2,,,,,,,1,,,,,,,,,,,2,,4,3,1,0,0,2,0,1,1 +4/21/23 08:58,4/21/23 08:58,0,137.146.188.253,2,23,0,4/28/23 08:58,R_1zEPM20r98zwAFj,,,,,,,anonymous,EN,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0,0,0,0,0,0,0,0,0 +4/21/23 08:40,4/21/23 08:56,0,137.146.139.12,100,947,1,4/21/23 08:56,R_eYjuzquGDuybABP,,,,,44.5542,-69.6179,anonymous,EN,1,2,1,1,,,,,,,,,,,,1,,,,,2,,,,,,,,,,,,,,,,,,,,,,,,,4,,,,,,1,,,,,,2,,,,,1,,,,,,1,,,,,,2,,6,3,3,1,1,0,1,2,1 \ No newline at end of file diff --git a/data/bar_graph_all_participants_051023.png b/data/bar_graph_all_participants_051023.png new file mode 100644 index 0000000..a224d3d Binary files /dev/null and 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b/images/semantic_3_different.png new file mode 100644 index 0000000..1ccc689 Binary files /dev/null and b/images/semantic_3_different.png differ diff --git a/images/semantic_3_similar.png b/images/semantic_3_similar.png new file mode 100644 index 0000000..2d69bea Binary files /dev/null and b/images/semantic_3_similar.png differ diff --git a/images/semantic_4_different.png b/images/semantic_4_different.png new file mode 100644 index 0000000..1e3489d Binary files /dev/null and b/images/semantic_4_different.png differ diff --git a/images/semantic_4_similar.png b/images/semantic_4_similar.png new file mode 100644 index 0000000..8793462 Binary files /dev/null and b/images/semantic_4_similar.png differ diff --git a/snippets/different/phonological_2.py b/snippets/different/phonological_2.py index 12ec2a9..fef8ce6 100644 --- a/snippets/different/phonological_2.py +++ b/snippets/different/phonological_2.py @@ -9,7 +9,7 @@ def function(points, func): return err/len(points) -def f(x): return 2*x +def func(x): return 2*x points = [(0, 1), diff --git a/snippets/similar/phonological_2.py b/snippets/similar/phonological_2.py index fe8ab31..6337441 100644 --- a/snippets/similar/phonological_2.py +++ b/snippets/similar/phonological_2.py @@ -9,7 +9,7 @@ def function(points, func): return pare/len(points) -def f(x): return 2*x +def func(x): return 2*x points = [(0, 1), diff --git a/survey_analysis.ipynb b/survey_analysis.ipynb new file mode 100644 index 0000000..83fb6ac --- /dev/null +++ b/survey_analysis.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df = pd.read_csv('data/Survey-Data/survey_031023_1344.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "correct_answers = [4, 1, 2, 2, 2, 1, 3, 1, 2, 1, 1, 1]\n", + "similarity_type = ['orthographic', 'orthographic', 'orthographic', 'orthographic',\n", + " 'phonological', 'phonological', 'phonological', 'phonological',\n", + " 'semantic', 'semantic', 'semantic', 'semantic']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "scored_df = pd.DataFrame()\n", + "scored_df['years_experience'] = survey_df['years_experience_1']\n", + "scored_df['python_experience'] = survey_df['years_experience_2']\n", + "scored_df['course_level'] = survey_df['course_level_1']\n", + "\n", + "Q_df = pd.DataFrame()\n", + "Q_df['type'] = similarity_type\n", + "\n", + "dif_percent = []\n", + "sim_percent = []\n", + "dif_attempts = []\n", + "sim_attempts = []\n", + "\n", + "for i in range(12):\n", + "\n", + " similar_header = 'Q' + str(i + 1) + '_similar'\n", + " different_header = 'Q' + str(i + 1) + '_different'\n", + "\n", + " dif_data = np.array(survey_df[different_header])\n", + " sim_data = np.array(survey_df[similar_header])\n", + "\n", + " dif_scored = np.asarray([dif_data[j]==correct_answers[i] if not np.isnan(dif_data[j]) else np.nan for j in range(65)], dtype=float)\n", + " sim_scored = np.asarray([sim_data[j]==correct_answers[i] if not np.isnan(sim_data[j]) else np.nan for j in range(65)], dtype=float)\n", + "\n", + " scored_df[different_header] = dif_scored\n", + " scored_df[similar_header] = sim_scored\n", + "\n", + " dif_percent.append(scored_df[different_header].sum()/scored_df[different_header].count())\n", + " sim_percent.append(scored_df[similar_header].sum()/scored_df[similar_header].count())\n", + " dif_attempts.append(scored_df[different_header].count())\n", + " sim_attempts.append(scored_df[similar_header].count())\n", + "\n", + "Q_df['score_different'] = dif_percent\n", + "Q_df['n_different'] = dif_attempts\n", + "Q_df['score_similar'] = sim_percent\n", + "Q_df['n_similar'] = sim_attempts\n", + "\n", + "scored_df.to_csv('data/Survey-Data/scored-survey-051023.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " type score_different n_different score_similar n_similar\n", + "0 orthographic 0.555556 9 0.375000 8\n", + "1 orthographic 0.545455 11 0.888889 9\n", + "2 orthographic 0.666667 9 0.571429 7\n", + "3 orthographic 0.444444 9 0.625000 8\n", + "4 phonological 0.500000 4 0.416667 12\n", + "5 phonological 0.750000 12 0.666667 6\n", + "6 phonological 0.750000 12 0.500000 8\n", + "7 phonological 0.666667 9 0.615385 13\n", + "8 semantic 0.444444 9 0.571429 14\n", + "9 semantic 0.857143 7 0.833333 12\n", + "10 semantic 0.692308 13 0.833333 6\n", + "11 semantic 0.153846 13 0.250000 8\n" + ] + } + ], + "source": [ + "print(Q_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.553030303030303\n" + ] + } + ], + "source": [ + "print(Q_df[Q_df['type'] == 'orthographic']['score_different'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Q_df.to_csv(\"processed_data_051023.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=300)\n", + "\n", + "count = 0\n", + "width = 0.25\n", + "x = np.array([0, 1, 2])\n", + "\n", + "means = {'similar': [],\n", + " 'different': []}\n", + "\n", + "for version in ('similar', 'different'):\n", + " for sim_type in ('orthographic', 'phonological', 'semantic'):\n", + " score = Q_df[Q_df['type'] == sim_type]['score_' + version].mean()\n", + " means[version].append(score)\n", + "\n", + "for sim_type, data in means.items():\n", + " offset = width*count + 0.13\n", + " ax.bar(x + offset, data, width, label=sim_type.capitalize())\\\n", + " \n", + " count += 1\n", + "\n", + "ax.set_xticks(x + width, ('Orthographic', 'Phonological', 'Semantic'))\n", + "ax.set_ylim(0, 1)\n", + "\n", + "ax.set_title('Accuracy by Similarity Type')\n", + "\n", + "fig.legend(bbox_to_anchor=(0.9, 0.88))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r^2: nan, p: nan\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/02/8sm7hf812xn2pq_yy1kvs60m0000gn/T/ipykernel_34845/191689273.py:4: RuntimeWarning: invalid value encountered in double_scalars\n", + " scores.append(scored_df.iloc[r, 3::].sum()/scored_df.iloc[r, 3::].count())\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scores = []\n", + "\n", + "for r in range(65):\n", + " scores.append(scored_df.iloc[r, 3::].sum()/scored_df.iloc[r, 3::].count())\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(scored_df['python_experience'], scores)\n", + "\n", + "lr = stats.linregress(scored_df['python_experience'], scores)\n", + "\n", + "print(f'r^2: {lr.rvalue}, p: {lr.pvalue}')\n", + "\n", + "ax.set_xlabel('Years of Python Experience')\n", + "ax.set_ylabel('Accuracy')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(28, 27) 28\n", + "r^2: nan, p: nan\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_53834/2867861670.py:9: RuntimeWarning: invalid value encountered in double_scalars\n", + " no_outlier_scores.append(no_outlier.iloc[r, 3:].sum()/no_outlier.iloc[r, 3:].count())\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "no_outlier = scored_df[scored_df['years_experience'] < 6]\n", + "no_outlier = no_outlier[no_outlier['years_experience'] > 0]\n", + "\n", + "print(no_outlier.shape, len(no_outlier))\n", + "\n", + "no_outlier_scores = []\n", + "\n", + "for r in range(len(no_outlier)):\n", + " no_outlier_scores.append(no_outlier.iloc[r, 3:].sum()/no_outlier.iloc[r, 3:].count())\n", + "\n", + "\n", + "fig, ax = plt.subplots(dpi=300)\n", + "ax.scatter(no_outlier['python_experience'], no_outlier_scores)\n", + "\n", + "lr = scipy.stats.linregress(no_outlier['python_experience'], no_outlier_scores)\n", + "\n", + "x = np.linspace(0, 4, 10)\n", + "y = lr.slope*x + lr.intercept\n", + "\n", + "plt.plot(x, y)\n", + "\n", + "print(f'r^2: {lr.rvalue}, p: {lr.pvalue}')\n", + "\n", + "ax.set_xlabel('Years of Python Experience')\n", + "ax.set_ylabel('Accuracy')\n", + "ax.set_xticks([0, 1, 2, 3, 4])\n", + "ax.set_yticks([0, 0.25, 0.5, 0.75, 1])\n", + "ax.set_title('Accuracy vs Python Experience')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/survey_analysis_milo.ipynb b/survey_analysis_milo.ipynb new file mode 100644 index 0000000..0f03714 --- /dev/null +++ b/survey_analysis_milo.ipynb @@ -0,0 +1,536 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n", + "44\n" + ] + } + ], + "source": [ + "survey_df = pd.read_csv('data/Survey-Data/survey_031023_1344.csv')\n", + "\n", + "pilot_df = survey_df[survey_df[\"StartDate\"] < \"4/22/23 10:35\"]\n", + "main_df = survey_df[survey_df[\"StartDate\"] >= \"4/22/23 10:35\"]\n", + "print(len(pilot_df.index))\n", + "print(len(main_df.index))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "correct_answers = [4, 1, 2, 2, 2, 1, 3, 1, 2, 1, 1, 1]\n", + "similarity_type = ['orthographic', 'orthographic', 'orthographic', 'orthographic',\n", + " 'phonological', 'phonological', 'phonological', 'phonological',\n", + " 'semantic', 'semantic', 'semantic', 'semantic']" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "scored_df_pilot = pd.DataFrame()\n", + "scored_df_pilot['years_experience'] = pilot_df['years_experience_1']\n", + "scored_df_pilot['python_experience'] = pilot_df['years_experience_2']\n", + "scored_df_pilot['course_level'] = pilot_df['course_level_1']\n", + "\n", + "scored_df_main = pd.DataFrame()\n", + "scored_df_main['years_experience'] = main_df['years_experience_1']\n", + "scored_df_main['python_experience'] = main_df['years_experience_2']\n", + "scored_df_main['course_level'] = main_df['course_level_1']\n", + "\n", + "Q_df_pilot = pd.DataFrame()\n", + "Q_df_pilot['type'] = similarity_type\n", + "Q_df_main = pd.DataFrame()\n", + "Q_df_main['type'] = similarity_type\n", + "\n", + "for dataframe, source, length, scdf in [(Q_df_pilot, pilot_df, 21, scored_df_pilot), (Q_df_main, main_df, 44, scored_df_main)]:\n", + " dif_percent = []\n", + " sim_percent = []\n", + " dif_attempts = []\n", + " sim_attempts = []\n", + "\n", + " for i in range(12):\n", + "\n", + " # create strings for column names\n", + " similar_header = 'Q' + str(i + 1) + '_similar'\n", + " different_header = 'Q' + str(i + 1) + '_different'\n", + "\n", + " # get the corresponding column and turn into a np array\n", + " dif_data = np.array(source[different_header])\n", + " sim_data = np.array(source[similar_header])\n", + "\n", + " # get a binary list of correct/incorrect answers\n", + " dif_scored = np.asarray([dif_data[j]==correct_answers[i] if not np.isnan(dif_data[j]) else np.nan for j in range(length)], dtype=float)\n", + " sim_scored = np.asarray([sim_data[j]==correct_answers[i] if not np.isnan(sim_data[j]) else np.nan for j in range(length)], dtype=float)\n", + "\n", + " # add the binary list to scored_df as a column\n", + " scdf[different_header] = dif_scored\n", + " scdf[similar_header] = sim_scored\n", + "\n", + " # add the average of each binary list and the frequency of the question to the lists\n", + " dif_percent.append(scdf[different_header].sum()/scdf[different_header].count())\n", + " sim_percent.append(scdf[similar_header].sum()/scdf[similar_header].count())\n", + " dif_attempts.append(scdf[different_header].count())\n", + " sim_attempts.append(scdf[similar_header].count())\n", + "\n", + " dataframe['score_different'] = dif_percent\n", + " dataframe['n_different'] = dif_attempts\n", + " dataframe['score_similar'] = sim_percent\n", + " dataframe['n_similar'] = sim_attempts\n", + "\n", + "# scored_df.to_csv('data/Survey-Data/scored-survey-051023.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " type score_different n_different score_similar n_similar\n", + "0 orthographic 0.600000 5 0.000000 2\n", + "1 orthographic 0.500000 4 1.000000 3\n", + "2 orthographic 0.600000 5 0.000000 1\n", + "3 orthographic 0.500000 4 0.000000 2\n", + "4 phonological 0.000000 1 0.000000 3\n", + "5 phonological 0.600000 5 0.666667 3\n", + "6 phonological 0.500000 4 0.000000 2\n", + "7 phonological 0.666667 3 0.833333 6\n", + "8 semantic 0.500000 2 1.000000 5\n", + "9 semantic 1.000000 3 1.000000 3\n", + "10 semantic 0.750000 4 1.000000 3\n", + "11 semantic 0.166667 6 0.333333 3\n", + " type score_different n_different score_similar n_similar\n", + "0 orthographic 0.500000 4 0.500000 6\n", + "1 orthographic 0.571429 7 0.833333 6\n", + "2 orthographic 0.750000 4 0.666667 6\n", + "3 orthographic 0.400000 5 0.833333 6\n", + "4 phonological 0.666667 3 0.555556 9\n", + "5 phonological 0.857143 7 0.666667 3\n", + "6 phonological 0.875000 8 0.666667 6\n", + "7 phonological 0.666667 6 0.428571 7\n", + "8 semantic 0.428571 7 0.333333 9\n", + "9 semantic 0.750000 4 0.777778 9\n", + "10 semantic 0.666667 9 0.666667 3\n", + "11 semantic 0.142857 7 0.200000 5\n" + ] + } + ], + "source": [ + "print(Q_df_pilot)\n", + "print(Q_df_main)\n", + "scored_df_pilot.to_csv('data/Survey-Data/scored_pilot.csv')\n", + "scored_df_main.to_csv('data/Survey-Data/scored_main.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.553030303030303\n" + ] + } + ], + "source": [ + "print(Q_df[Q_df['type'] == 'orthographic']['score_different'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Q_df.to_csv(\"processed_data_051023.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=300)\n", + "\n", + "count = 0\n", + "width = 0.25\n", + "x = np.array([0, 1, 2])\n", + "\n", + "means = {'similar': [],\n", + " 'different': []}\n", + "\n", + "for version in ('similar', 'different'):\n", + " for sim_type in ('orthographic', 'phonological', 'semantic'):\n", + " score = Q_df_pilot[Q_df_pilot['type'] == sim_type]['score_' + version].mean()\n", + " means[version].append(score)\n", + "\n", + "for sim_type, data in means.items():\n", + " offset = width*count + 0.13\n", + " ax.bar(x + offset, data, width, label=sim_type.capitalize())\\\n", + " \n", + " count += 1\n", + "\n", + "ax.set_xticks(x + width, ('Orthographic', 'Phonological', 'Semantic'))\n", + "ax.set_ylim(0, 1)\n", + "\n", + "ax.set_title('Accuracy by Similarity Type — PILOT')\n", + "\n", + "fig.legend(bbox_to_anchor=(0.9, 0.88))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(dpi=300)\n", + "\n", + "count = 0\n", + "width = 0.25\n", + "x = np.array([0, 1, 2])\n", + "\n", + "means = {'similar': [],\n", + " 'different': []}\n", + "\n", + "for version in ('similar', 'different'):\n", + " for sim_type in ('orthographic', 'phonological', 'semantic'):\n", + " score = Q_df_main[Q_df_main['type'] == sim_type]['score_' + version].mean()\n", + " means[version].append(score)\n", + "\n", + "for sim_type, data in means.items():\n", + " offset = width*count + 0.13\n", + " ax.bar(x + offset, data, width, label=sim_type.capitalize())\\\n", + " \n", + " count += 1\n", + "\n", + "ax.set_xticks(x + width, ('Orthographic', 'Phonological', 'Semantic'))\n", + "ax.set_ylim(0, 1)\n", + "\n", + "ax.set_title('Accuracy by Similarity Type — MAIN')\n", + "\n", + "fig.legend(bbox_to_anchor=(0.9, 0.88))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/02/8sm7hf812xn2pq_yy1kvs60m0000gn/T/ipykernel_53026/702798567.py:4: RuntimeWarning: invalid value encountered in double_scalars\n", + " scores.append(scored_df_pilot.iloc[r, 3::].sum()/scored_df_pilot.iloc[r, 3::].count())\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r^2: nan, p: nan\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scores = []\n", + "\n", + "for r in range(21):\n", + " scores.append(scored_df_pilot.iloc[r, 3::].sum()/scored_df_pilot.iloc[r, 3::].count())\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(scored_df_pilot['python_experience'], scores)\n", + "\n", + "lr = stats.linregress(scored_df_pilot['python_experience'], scores)\n", + "\n", + "print(f'r^2: {lr.rvalue}, p: {lr.pvalue}')\n", + "\n", + "ax.set_xlabel('Years of Python Experience')\n", + "ax.set_ylabel('Accuracy')\n", + "plt.title(\"Accuracy vs experience - PILOT\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r^2: nan, p: nan\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/02/8sm7hf812xn2pq_yy1kvs60m0000gn/T/ipykernel_53026/3040855304.py:4: RuntimeWarning: invalid value encountered in double_scalars\n", + " scores.append(scored_df_main.iloc[r, 3::].sum()/scored_df_main.iloc[r, 3::].count())\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scores = []\n", + "\n", + "for r in range(44):\n", + " scores.append(scored_df_main.iloc[r, 3::].sum()/scored_df_main.iloc[r, 3::].count())\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(scored_df_main['python_experience'], scores)\n", + "\n", + "lr = stats.linregress(scored_df_main['python_experience'], scores)\n", + "\n", + "print(f'r^2: {lr.rvalue}, p: {lr.pvalue}')\n", + "\n", + "ax.set_xlabel('Years of Python Experience')\n", + "ax.set_ylabel('Accuracy')\n", + "plt.title(\"Accuracy vs experience - MAIN\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(13, 27) 13\n", + "r^2: nan, p: nan\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/02/8sm7hf812xn2pq_yy1kvs60m0000gn/T/ipykernel_53026/591440040.py:9: RuntimeWarning: invalid value encountered in double_scalars\n", + " no_outlier_scores.append(no_outlier.iloc[r, 3:].sum()/no_outlier.iloc[r, 3:].count())\n" + ] + }, + { + "data": { + "image/png": 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saQYAAACHiIqK0ujRo011AwcOVHJycqF9x4wZo3379lnKDRo00H//+99ijxFlT1JSkuVnf39/xwWSjzvvvFO//PKLvLy8TPUfffSRgyICAAAAAFxD0gwAAAAOM3z4cDVu3NhSPnbsmIYPH15gn61bt+qDDz6wlN3d3TVjxgz2hIIkmfYzc3FxcWAk+atfv7569+5tqvvtt99MMycBAAAAAKWP5RkBAADgMNcSXk2aNFFmZqYkafLkyerRo4fatGlj1T4rK0tPP/20rly5YqnLnXjLzTAM/fXXX9q7d6/OnDmjlJQUBQUFKTQ0VC1btpSfn991ncOlS5e0e/du7du3T+fOnVNaWpoCAgIUFBSkRo0aqU6dOtc1fn527dqlvXv36tSpU7p06ZKqVq2q3r1757unW1pamnbs2KE9e/YoMTFRaWlp8vb2lr+/vyIiIlSvXj1Vr169RGLNT2pqqtasWaPjx4/r7NmzKleunKpUqaL69esXea+lsrIfUOfOnTVlyhRLOSkpScePH1d4eLgDoyoZaWlp+uOPP7Rv3z5dunRJFSpUUEREhO677z75+vpe9/hJSUlau3atTp48qYSEBPn5+alKlSq64447SuT9d+bMGa1evVpHjhxRVlaWgoKCFBUVpbvvvltubm7FfjxndfnyZW3YsEG7du3S+fPn5e/vr+rVq6tVq1YKCAgolmMkJSVp3bp1OnXqlOUaVLlyZd1xxx2KiooqlmPkZO91u6gyMzO1YcMGHT16VGfPnlV2drYqV66s2rVrl8jr9NixY1q3bp2OHTsmwzBUuXJlNWzYUHfccUexfHnhypUr2rJliw4dOqSEhAQlJyfLz89P1apVU1RUlCIjI4t8nNTUVK1du1bx8fE6c+aM3NzcVKVKFUVFRalRo0Y37JcvAABACTIAAAAAB3vjjTcMSZZHjRo1jEuXLlm1GzNmjKldVFSUkZ6enueYZ86cMYYOHWrccsstpj45H56enkaXLl2MHTt22BVvbGysMWbMGOPuu+823N3d8x1fkhEcHGy8/vrrxvnz520ePyYmxjTG6NGjDcMwjKysLGPixIlG/fr18zxWYmKi1VgHDhwwnnzyScPX17fAOCUZISEhxlNPPWWsX7/ert+HvbZv32507drVKFeuXL6xVKtWzRg1apSRnJxc4FijR48u9LzyehSHGTNmmMa87777bO67c+dOq5i2bNliGIZhpKamGoGBgZb6gIAAIzU11e74duzYYRq/adOmluf69OlTpN9bnz59rI6Te6wjR44YhmEYSUlJxtChQ/N97Xl6ehqDBw82zp49a/e5GYZh/P7770abNm0KfA/eeuutxocffmhkZGTYPG54eLilf3h4uKU+NjbWeOSRRwxXV9c8j1WpUiVj/PjxRmZmZpHOpyjxSTJiYmKKdfwffvjBNL6fn58RGxtrc/+hQ4ea+kdHRxtpaWlW7fK7zmVmZhrvvfeeUbly5Tx/z15eXkbPnj2NEydOFPkcf/zxR+Pee+8t8LUTFhZmfPjhh/l+xth6PvZet4t6TTGMq9eV7t27G35+fvmeV2BgoPH8888bf//9t83j5hfTpk2bjDZt2uR7rOrVqxszZsyw6xxy2rVrl/HEE08YAQEBBV6XgoODjUGDBhnbt2+3eew1a9YYDz74oOHl5ZXvuFWqVDFGjRplXLx4scjnAAAAyh6SZgAAAHC4zMxMo2HDhqabVc8//7ypzc6dOw1PT0/L825ubsbGjRvzHG/q1KlG+fLlbU4EuLq6Gq+//rpNsf70009FSjZUq1bN2LRpk03HyOvm6/nz54177rmnwGPkvvn61VdfFXhDML/HP//5T5vitFd2drYxYsSIfJMO+d0MXb16db5jltWk2b59+6xi2rBhg+X5F154wfTcl19+aXd8zz//vGmMqVOnWp4r6aTZ7t27rZI7+T1q1aplSbTZIiMjw3jyySftirt27drG3r17bRo/r6TZvHnzDG9vb5uO1alTpzyTRMWlpJNmhmH9+mvYsKFNyaPc10dfX998f+95XecSExONFi1a2PR79vf3N5YsWWLXef39999Gq1at7Hrt1KlTxzh48GChYxfXdbso15SsrCzj+eeft+vaWr58eWPx4sU2jZ9XTB9//LHh5uZm07EGDBhgXLlyxaZjGcbVfxMMHjzYrvPJ+X4tyKVLl4zu3bvbNW5wcLDNn98AAKDsY08zAAAAOJyHh4dmzJghd/f/Wz180qRJWr16taSrSzM99dRTliUcJWnYsGFq2rSp1VijRo1S//79lZycbKr39/dX/fr11bRpU0VERJiey87O1ujRozVkyJBCY825Z9Y13t7eqlOnjho3bqwmTZqoRo0acnU1/1M7Pj5erVq10t69ews9Rm6XL19Wly5dtGbNGktdhQoVFB0drejo6DyXKlu+fLn69OmjjIwMU72Pj49lKbk77rhDERERVrGWBMMw1LdvX40dO9Zq765rS1lGRkaqXLlypudOnz6t+++/X0uXLi3xGEvTyZMnreoqVapk+XnQoEGm53Iu5WiLtLQ0zZ4921IuX768evbsaWeURXP06FG1bdtWcXFxlrrw8HDdeeedioyMtFoa7tChQ+rWrZsuX75c6NgZGRnq1KmTZs2aZfXcLbfcoiZNmqhOnTpWy90dOHBA99xzj/7880+7z+fnn39Wz549lZaWJunq9apOnTp5XkuutR8xYoTdx7mRjBs3To0aNbKUt2/frn//+98F9omPj1ffvn1NdZ9++qnq1atn0zGzs7PVvXt3rV271lJXqVIl3XHHHXleGy5evKhu3bopJibGpvEPHDigu+++W6tWrTLVu7i4KCIiQk2aNFGDBg2sluzdv3+/mjVrpv3799t0nGuKct0uitTUVHXu3Fmffvqp1bU1ODhYDRs2VKNGjVSlShXTc8nJyeratavmzZtn9zE///xzDRkyxLJUsre3tyIjI3XnnXcqJCTEqv2UKVM0YcIEm8a+cOGC2rVrp88++8zqfK591t51112KjIy0e3nlM2fO6L777svznENDQ9W4cWM1bNhQFSpUMD13+vRptWrVyvS3BAAANzFHZ+0AAACAa0aNGmU1OyQ1NdV47733TPV169bNcybH9OnTTe1cXFyM3r17G5s3b7b6lnt8fLwxcuRIw8PDw9Tn22+/LTDGefPmGW5ubkanTp2ML774wjhw4ECe36C/ePGiMXv2bKNOnTpWMzays7MLPEbuGQtVq1a1/NyuXTtj7dq1pmNmZ2cby5cvN80EiYqKMo3RunVrY9WqVcbly5etjpeammqsW7fOePXVV42aNWuWyEyziRMnWn17v2XLlsbatWtNv4/k5GRjypQpRsWKFU1tK1asaMTHx1uNe+jQIWP58uWWR87fVdWqVU3P5XwUh+uZafbaa6+Z+np6elotwdiyZUtTG1tnShnG1VmGOfsOHDjQ9Pzu3bvt/p0tX77c2L17t9Wxcs80q1mzpiHJ8Pb2Nl577TWrv9v58+eNl19+2XBxcTH1+9///lfoeQ0bNszqdfTwww9bLbF67tw5Y+zYsVazw2rVqlXokp85Z3IFBgYaFSpUMCQZoaGhxvTp062Watu/f7/RqVMn03FcXV2NXbt2FXo+RVEaM80M4+rSrrln7C5YsCDPtpcvXzbuvfdeU9vCriO5r3M5z6tRo0ZGTEyM1bXhiy++MC1dqv8/CyivpWlzSklJMSIjI039atSoYXzxxRdWS+dmZWUZv/zyi9GoUSOra3dBs+2K47ptGPbPNOvdu7epj5+fnzFq1Cjj8OHDVm23b99uPProo6b25cuXNw4cOFDgMXK2r1atmmXWd2RkpDF//nyrz+OtW7cazZo1M/Xz8fExEhISCj2fRx55xOo9ft999xm//vqr1e/qypUrxq5du4y3337biIyMLHCm2ZUrV4zWrVubxq1cubIxbtw449SpU1Zt16xZY7X0ZGhoqE3nAAAAyjaSZgAAALhhZGRkGA0aNDDdpOrRo4dp7ytXV1dj7dq1Vn0PHTpk+Pj4WNp5e3vbtHTX77//brqxXqVKlQKXVjt8+LBx9OhRm88pLS3N6Nixo+mcCosr983Xa48XX3zRpmPu3r3bKmFm69JYV65csWv/IlscP37cKnnRp0+fApOHR44cMapVq2bq07Vr10KPld9+VCWhqEmzpKQk0w11Sca9995r1e7rr782tRk2bJjNseVOYGzevDnfttf7O8trqceKFSsWupzZW2+9ZZWUKMimTZusEm2vvfZagX22bNli+Pv7m/oMGTKkwD55LSvZqFEj48yZM/n2uXz5svHAAw8U6f1qr9zxffDBBwUmOvN72LKXY+7XYIUKFfK8/uVOAteuXbvQ5GR+17mOHTsWuC/c/v37jSpVqpj6PPvsswUe65lnnjG1f+ihhwqNLz093SqB8+GHH9p9Pva+Duy5psydO9fUvlatWjYtJTl27FhTvy5dutgc07VHhw4djJSUlHz7pKSkGLfffrupz4QJEwo8zueff25q7+LiYowbN67Q8zGMq0nIlStX5vt87i/f3HXXXYXu63blyhWrZW5feOEFm+IBAABlF0kzAAAA3FC2bNliuLu753mTTpIxdOjQPPsNGjTI1G7OnDk2H3PSpEmmvlOmTCmu0zEM4+qsl4CAAMv43bt3L7B9XjdfmzdvXugMtWsWLlxo6jt37tziOI0iGzlypCme22+/3cjKyiq037p160xJEhcXF2P//v0F9rnRk2YXL1407r//fqu/b879xq5JT083goKCLG2CgoKMjIyMQo+Re7+0wpJRJZE0W7hwYaH9srKyjNDQUFO/06dP59v+8ccfN7Xt3LmzTfHlTiz4+voaSUlJ+bbPnZTy9/e3KVG+d+9eU7+6devaFJ+9bN0rrrCHLUlowzCMp59+2tSvWbNmpvdvTEyMae8pT09PY+vWrYWOm9d17pZbbrGayZeXX3/91dTP29s737/psWPHTJ8p0dHRNu3PZhhXEz8RERGm90des3XzOx97rtvX2HpNyc7ONurVq2dp6+PjY1PC7Jqc+3q5uLgU+GWJ3OcVFhZW4Hvoml9++cUq0ZafrKwsIywszNT+lVdesfl8CpKSkmKavXzLLbcY586ds6nv5cuXjbvvvtvS18/Pr9CZjQAAoGxjTzMAAADcUBo3bqzhw4fn+dytt96qt99+26r+/Pnz+uqrryzlZs2a2bV/04ABA0z7vcyfP9+OiAtXsWJFPfjgg5byunXr7B7jjTfekIuLi01tr+29dE3u/Z1Kk2EYmjZtmqnugw8+MO1fl59mzZrpscceM401derUYo+xpKWmpmrv3r36+OOP1aBBAy1btsz0fGRkpPr06WPVz8vLy7RHVEJCghYuXFjo8XL/jgYMGFC0wIuoadOmevjhhwtt5+7urm7dupnqtm7dmmfbpKQk0/vSxcVF48ePtymexx57THfffbelnJKSom+++camvpL0zDPPKDw8vNB29erVU3R0tKV84MABXbp0yebj3Kg++eQTRUZGWsrr16/Xq6++Kunqa/Kf//ynae+psWPHmvZDs8err76q8uXLF9quQ4cOatOmjaWclpamOXPm5Nl20qRJpv3yxo0bJy8vL5vi8fHx0dChQy3luLg4bdmyxaa+kn3XbXstXbpU+/bts5SHDBmiWrVq2dz/2t9QunptteXacs1//vMfm/Zka9++vWl/sG3btuXbdsGCBTp27JilXLNmTY0ZM8bmmAry1Vdf6fz585bymDFjVLFiRZv6urm5aeTIkZbypUuXbro9NgEAgBlJMwAAANxwRo8eraioKFOdi4uLpk+fLm9vb6v2q1atMiWKnnzySbuO5+HhodatW1vK69atM90ELg41atSw/BwfH6+zZ8/a3Ldq1aqmG8SFCQkJMZW//vprm/sWt3379unMmTOWclhYmNq2bWtz/6efftpU/uOPP4ottuL2+++/y8XFxerh6+urqKgovfjii4qLizP1qVy5sn766ad8k4gDBw403XSfMmVKgTFkZWWZEsg+Pj765z//eR1nZb+cic7CNGzY0FQ+fvx4nu3Wr1+vzMxMS/mee+5RnTp1bD7O9byOino+2dnZio+Pt7nvjcrHx0ffffed6do7duxY/frrr+rTp49OnjxpqX/ooYc0ZMiQIh3Hw8PDri875E40r1q1Ks92S5YssfwcHBysdu3a2RXX/fffbyqvXr3apn72XrftlfO8JPs/96KjoxUcHGwp23peLi4u6tGjh01t3dzc1KBBA0v57NmzysjIyLNt7i8TDB48uNi+8JHzd+Xu7m7X60yS2rZtK1fX/7t9ZuvvCgAAlE0kzQAAAHDD8fLyMn27X5KaN2+uli1b5tk+9w2sJk2a2H3MsLAwy88XL1606WZ3UlKSpk2bpqeeekqNGjXSLbfcIl9f3zwTJ++++66pb0JCgs2xNWnSxK7ZCnfddZf8/f0t5QULFqhHjx7auXOnzWMUl40bN5rKrVu3tutc7r33XlNC6c8//zQlT8qyVq1aafPmzQXODqldu7bpxvvKlSt1+PDhfNv/8MMPpiRl9+7dbZoRUpzsef/lnOEpSRcuXMizXe7Xkb3JiNyJ2g0bNtjUz8PDQ7fffrvNx7H1fIpTTEyMjKtbL9j1WLRokc3HuO222zRhwgRL2TAMde3a1ZSMCA0N1cyZM4t8HtHR0TbP/pGuvn9y2rRpk1WbxMRE7dq1y1Ju1KiRKflhi5yfDZK0d+9em/rZe922V87PPV9fX9WrV8/uMapXr2752dbzioiIUKVKlWw+hq3vidyf4x07drT5GAUxDENr1661lOvUqWP6fLSFr6+v6Zxt/V0BAICyiaQZAAAAbki5Z94UtJxf7htYTZs2zTNxVdBj3LhxpjFyLuWUW0pKikaMGKHg4GD1799fM2fO1J9//qnTp08rNTXVpvNLSkqyqZ1knqVmi3Llyunll1821c2bN0/R0dGWGU8LFy7U6dOn7Rq3KHLPrMq5fJ0tvLy8TDeDMzIy9PfffxdLbI4QFhamXr16KSYmRjExMTYt+/fMM89Yfi5sicrcM9FKe2lGyfomeUF8fX1N5dxLi15zva+jmjVrmpb9O378uAzDKLRfxYoV5ebmZvNxbD2fsmjgwIGmGUY5k9dubm765ptv7Ep65XbbbbfZ1T4sLMyU/IiLi7P6m8bGxprqlixZYvdnQ+6/aUGfDTnZe922V87PvZSUFLm6utp9bps3b7aMYet52fP+lmx/T+T8MoCvr69pSdDr8ffff5vObc+ePXb/nlxcXEyzw239XQEAgLKp8I0EAAAAgBvcuXPnin3M/L4Nn5CQoDZt2lz3rK38lqjKi73fipekkSNHKi4uTpMnTzbV792717K/liTVrVtXHTp00BNPPKG77rrL7uMUJjEx0VQOCgqye4zcfRITE00zJG4U0dHRVvtsubi4qFy5cgoICFBwcHCRzr9r164KDg62JDlnzpypN954wyqRHBcXpxUrVljKkZGRatGiRRHO5PqUK1euyH3zS2QVx+uoUqVKSk5OliRdhYGeEQAAFNpJREFUuXJFycnJhb63rudcpPzPp6yaMmWKtmzZYjXbcfTo0fnOBLaVPbOXrqlYsaIuXrwo6epymBcvXjTNrCzNz4bcinLdtlVKSopdnyG2sPW8SuI9cfHiRdO+c0FBQcU2S8+RrwEAAFA2kTQDAABAmWfPrC1b5benWffu3a0SZtWrV1fr1q0VFRWl0NBQ+fn5ydvb27QM2FdffaVZs2YVKZai7Ovi4uKiL774Qt26ddNbb72lNWvW5NkuNjZWsbGxmjhxolq0aKEJEyYUaXnL/Fy6dMlUzj3rwBa5+1xLfNxoKlSoYPd+Sbbw8PDQ008/rXfeeUeSdOrUKf3888/q2rWrqd20adNMr1tHzDIrKSX1OirJxMbNKCsrK89kTc49IYvKx8fH7j65/6aXLl0yJc1K87Mht+LajysvJXFejkzw5r6m+/n5FdvYjnwNAACAsomkGQAAAMq83DdbZ8yYodDQ0OsaM699jH788UetWrXKUi5fvrw+++wzPf7444Xuk/Pbb79dVzxF1aFDB3Xo0EFHjhzRsmXLtGrVKv3xxx86efKkVdu1a9eqRYsWmj17trp3714sx8998zMlJcXuMXL3ybnMnrMYMGCA3nvvPcvN2ilTppiSZleuXNGMGTMsZS8vL/Xu3bvU4ywpvI5uDH379s1zv8devXpp+/btCgwMLPLYti5tm1Puv2nu10nuz4bWrVvrlVdesT+4HCpUqHBd/YtD7vOqWLGivv32WwdFc/1yvxdzJ8mvR+7fVVRUlGWmdVF5e3tfV38AAHBjI2kGAACAMi/3Um1RUVFq2rRpsR9n7ty5pvIXX3yhxx9/3Ka+jt4DpUaNGho0aJAGDRok6er+Mb/99psWLFigZcuWWZIxmZmZ6t27t+666y6FhYVd93Fz32AuylJZCQkJBY7pDCIiItShQwf98ssvkqRff/1VJ06csCSHr5WveeSRR4q03N2NqjheRzn7uLm5kTSz00cffaTFixfn+VxcXJz69eun+fPnF3n83O9zW+S8rrq6ulrNHMz92VCuXLkSmQ1a2gIDA+Xu7m5Z0jAtLa1Mn5e/v7/pfBISEmQYRrEs0Zj7NWAYRpn+XQEAgJJX8NdhAQAAgDKgRo0apvLBgwdL5DgbNmyw/FypUiX16NHD5r67d+8uiZCKrGbNmhowYIB++eUX7dixQzVr1rQ8l56erkmTJhXLccLDw03lHTt22NU/IyNDsbGxlrKXl5eqVq1aLLGVNdcSntLVmWXTp0+3lKdMmWJqO3DgwFKLqzRc7+vo8OHDpiXgwsLCim3PJGewdetW/ec//7GU3dzcNG/ePFWpUsVSt2DBAn322WdFPsauXbvsah8XF2fZz0y6+hrJ/Tctrc+G0ubi4mJ6T6SlpeU5e7gsufXWWy0/p6SkaO/evcUybnBwsGlmWFxcnLKysoplbAAAcHMiaQYAAIAyL/d+OitXriyR4/z999+Wn2+99Va5ubnZ1O/ixYvaunVricRUHG677TZNnjzZVJffHmj2uvvuu03lVatW2bV3zurVq003OBs1aiRPT89iia2s6dy5s6pVq2YpT58+XdnZ2ZY9zq659dZb1apVK5vHzbm0qCP3NSpI7teRve/x3O1zj4f8JScnq2fPnsrMzLTUjRkzRo8++qhmzZplSlQNGzbM7uTXNTt37rRrRu7vv/9uKuc1uzg0NNSUjDlw4ICOHz9epPhuNKX1uVdaWrZsaSovWbKkWMb18PBQixYtLOXU1FRt3LixWMYGAAA3J5JmAAAAKPPatWsnd/f/W3l87ty5RVq+rTA5Ewo5byAXZvr06UpPTy/2eIpTzpuKUtGWSstL3bp1TTPD4uLiFBMTY3P/nLOpJOm+++4rlrjKIjc3N/Xv399SjouL07JlyzRz5kzLsmaS1L9/f7tmUfn6+lp+Lsq+UqXh7rvvNiVL16xZY9esIV5HRTd48GDT77pNmzaWfcHuv/9+DR8+3PJcenq6HnvssSK9jrKysqyWwC3Il19+aSrn9zd94IEHTOVPP/3U7thuRDfbeeU+n88++6zYZoTlHvuTTz4plnEBAMDNiaQZAAAAyryqVavqySeftJRTUlL03HPPFftxgoODLT/v3r1bSUlJhfaJj4/X66+/XuyxFLeS2jfMxcVF/fr1M9UNHz5cV65cKbTvpk2bTDfRXVxcTEkjZzRgwADTDMfJkydr6tSplrKHh4f69u1r15gVK1a0/Hzu3DnTMoY3isDAQD366KOWsmEYeumll2zq+/3332v9+vWWsp+fn817ETq7GTNm6Ouvv7aUK1eurNmzZ5tmJ7799tu66667LOU9e/bohRdeKNLx3nrrLZtef0uXLjXNrPL29s73bzp06FDTlyo++eQTbdu2rUjx3Ugefvhh0yy6jRs3XtfymI7WtWtXRUREWMqHDx/WmDFjimXs/v37KzAw0FL+/vvvTbNzAQAAciJpBgAAgJvCq6++Kh8fH0v522+/1aBBg+yaEXb+/Hm99dZb+umnn/J8vnnz5pafMzMzNXLkyALHO3v2rDp37mxTcq04ffzxx5o0aZJdsz3GjRtnKjdu3LjY4hk8eLBpT5lt27bpmWeeKXApwGPHjunRRx81tenatatq1apVbHGVRdWqVVPnzp0t5YULF+rw4cOW8kMPPWT3nm/169e3/GwYhubPn3/9gZaAoUOHmpI1P/zwg956660C+2zfvt0q0dq/f3/5+/uXSIw3k3379un555+3lF1cXPTll1/qlltuMbVzd3fX3LlzFRAQYKmbNm2avv32W7uPeerUKfXs2bPAGUYHDx5U7969TXV9+vQxJUVyqlmzpilxn5aWps6dO5sSqbZYuXLlDbVXoJubm958801T3ZAhQ6z2NyzM/v37NXDgQMXHxxdneHZzc3PTq6++aqp79913NX78eJv6G4aR7yzmgIAAvfzyy5Zydna2Hn/8cf344492xbh161Y99thjdvUBAABlD0kzAAAA3BRq1qypadOmmeomT56sBg0aaMqUKab9yK4xDEOHDh3SrFmz1K1bN4WGhmrUqFH5Lu2Y+0bt559/rj59+iguLs5Un5ycrGnTpik6Olrbt2+XJEVGRl7H2dnnyJEjev755xUSEqI+ffpo4cKFOnXqVJ5tt2/frp49e2rixImWOldXVz399NPFFk9oaKjGjh1rqps6daratGmjDRs2mOpTUlI0bdo0NW7c2LT3UMWKFTVp0qRii6ksGzRoUL7PDRgwwO7x7r//flN58ODB+te//qU5c+Zo6dKlWrFiheWxZ88eu8cvLk2aNNHQoUNNdaNGjdKjjz5qtY9WYmKiPvjgA7Vo0UIXLlyw1NeqVavQRFtZs3XrVtPfyJ7HX3/9leeYeS2zOHToUD344IN5to+IiLDaF3HQoEE6cuSIzecRHh4u6epeVs2aNdPvv/9uSpqnpKRoypQpatq0qc6cOWOpr1q1qt55550Cx/7oo490xx13WMqnTp3Svffeq6eeekobNmwwLW96zaVLl7RmzRr997//Vb169dS2bVstW7bM5vMpDT179jRdD7KysjRw4EC1bdtWixcvVkpKilWfrKws7dixQxMmTFDLli1Vr149TZkypdiWQrwe/fr1y3NGaZs2bbRs2TJlZGSY2mdnZ2v37t165513FBkZqaeeeirfsUeMGKGOHTtaysnJyXr44YfVrVs3rVy50mps6er7YNOmTXrnnXfUuHFjNWnSRN99910xnCkAALiRuRfeBAAAACgbevbsqZMnT2r48OHKzs6W9H/foh84cKCqV6+uoKAgubu7KykpSadPn7ZrKbr7779fHTt21JIlSyx1X331lb766ivVrFlTlStXVlJSko4cOWKa4fbEE0+odu3apb5M44ULFyzxSVeXVqtSpYrKly+v9PR0HT16NM9ZcCNGjCjWmWaS9Nxzz2nTpk2aNWuWpW7VqlVq1qyZKleurOrVqys9PV1HjhxRWlqaqa+3t7e++eYbhYSEFGtMZVWHDh0UERGho0ePmurDwsKsEmC26Ny5s+rWravY2FhJV28Uf/rpp3nukdSnTx/NnDmzKGEXi7fffls7duzQihUrLHXz58/X/PnzFRISopCQECUnJ+vw4cNWSYBKlSrpu+++M+3hdjOwdZnKvHTt2lWLFi2yqh82bJgpodakSRO99957BY7Vo0cPrVixwjLT6cKFC+rZs6fWrFkjDw+PQmPp3bu3NmzYoOXLl2vr1q1q1aqVKlWqpPDw8HyvDV5eXpo9e3ahy8l6e3vrxx9/VMeOHbVz505J0uXLlzVz5kzNnDlTvr6+ql69ugICApSamqrExETFx8cXOBv2RvHJJ58oMTHRlMxZuXKlVq5cKXd3d4WHh6tixYq6fPmykpKSFB8fb9cM7NI2bdo0nT17Vr///rulLiYmRjExMfLx8VH16tUVGBio5ORkHTt2TJcuXbK0u5Z4zYurq6u++eYbPfzww1q1apWkq0m5hQsXauHChfLy8lJ4eLgqVKig9PR0JSUl6cSJEzYtJQwAAG4uzDQDAADATWXYsGFasmSJ1RJiknT8+HH9+eef2rx5sw4cOJBnwszLy0tVqlTJd/yvv/5aTZs2tao/fPiwNm7cqNjYWNMNyZ49e2rGjBlFPJvidfbsWe3evVsbNmzQ9u3brRJmbm5ueu211/Tuu+8W+7GvLe02fPhw0xJ71+Latm2b9uzZY3VTPDg4WMuWLVOHDh2KPaayytXVNc8ZZf369bP63drC3d1d8+fPLxNLX3p5eennn39Wr169rJ47efKktmzZotjYWKuEWe3atbVmzRo1atSotEItsxYsWGDaG8vf319z5861KfH18ccfm5b73LRpk/773//adFxXV1fNmzdP99xzj6Xu3Llz+V4bypcvr++//17t2rWzafzQ0FCtX79evXr1kouLi+m5lJQU7du3Txs3btTOnTt14sSJPBNmYWFhNh2rNHl4eOjbb7/V+++/b1oGV7qaGDx06JA2b96sP//80+oLHdcEBQVZ9XUUf39/LVu2TP3797f6O6Wmpio2NlYbN27Unj17TAkzWwQEBGj58uUaNmyYaZ87ScrIyND+/fu1ceNG7dixQ3FxcXkmzKpXr27/SQEAgDKFpBkAAABuOh06dNDhw4c1ceJERUdHW914y83Pz0+dOnXSZ599plOnTpmWcMotMDBQf/zxh0aNGmXawye3+vXr65tvvtGcOXPk6elZ5HMpijfeeENz585Vr169bLrB5+fnp169eunPP/8s0dlwLi4uGjt2rLZt26YuXbrIy8sr37YhISEaNWqUDhw4YLqJjqtyL0Pm5uZ2XUtq1q9fX3/99ZdmzJihRx99VHXq1FFAQIDc3NyuN9Ri5+npqVmzZmnVqlVq06aN1c3vnGrVqqXx48dr165dqlevXilGWTbFxcVZ7QH3+eef25xQ9fb21rfffmtKwHzwwQdaunSpTf0DAgK0cuVKvfvuuwoKCsqzjaenp3r06KE9e/aY9vezha+vr2bNmqXt27fr8ccfz3cftJzq1aunIUOGaN26dfrjjz/sOl5pGjFihI4cOaKXXnrJpuRecHCwevXqpQULFujkyZN274VYkjw9PTVlyhRt27ZN3bp1K3R2aHh4uIYMGaJff/210LHd3d01fvx4xcbGauDAgQV+SeaaiIgIDRw4UMuWLbOa4QsAAG4+LkZZWG8AAAAAuA5nz57Vxo0bdfr0aZ07d07Z2dny9/dXcHCwIiMjVbt2bZtmUeSWnp6u9evXa+/evUpMTJSnp6dCQkJ05513qk6dOiVwJkUTHx+vffv26ciRI0pMTFRGRoZ8fHxUqVIl1a9fXw0aNCgwgVVSUlNTtWbNGh07dkwJCQmWWX7169dXw4YNSz2esmTlypVq27atpdypUyctXrzYgRE5TlJSktasWaOTJ0/q3Llz8vX1VdWqVdWwYUPVrVvX0eEhH6tWrVLr1q0t5dGjR2vMmDGW8uXLl7V+/Xrt3LlTiYmJ8vf3V2hoqFq3bm1TsssW2dnZ2rZtm/bv36+EhARdvHhRPj4+CgwMVK1atRQVFaXKlSsXy7FK28GDB7V9+3adPXtWiYmJcnd3V0BAgMLCwhQZGamIiAhHh2izjIwMrV+/XnFxcTp79qwyMzNVvnx5hYWF6bbbbruuWbKGYWj37t3avXu3EhISlJSUJC8vLwUEBKhGjRqKiopiaWAAAJwMSTMAAAAAKGOeeOIJzZkzx1JetGiRunbt6sCIAPsUljQDAAAAHIHlGQEAAACgDElISNCCBQss5WrVqqlTp04OjAgAAAAAbg4kzQAAAACgDJkwYYIyMjIs5UGDBhW4rxcAAAAAwDYkzQAAAACgjNi7d68+/PBDS9nHx0eDBg1yYEQAAAAAcPPg64gAAAAAcANKT0/XmjVrJEkXL17Utm3b9OmnnyotLc3S5vnnn1eVKlUcFSIAAAAA3FRImgEAAADADej06dNq3759vs9HRETotddeK8WIAAAAAODmxvKMAAAAAFDGVKlSRT/++KN8fX0dHQoAAAAA3DSYaQYAAAAAZYCPj49q1qypzp07a9iwYapcubKjQwIAAACAm4qLYRiGo4MAAAAAAAAAAAAAHInlGQEAAAAAAAAAAOD0SJoBAAAAAAAAAADA6ZE0AwAAAAAAAAAAgNMjaQYAAAAAAAAAAACnR9IMAAAAAAAAAAAATo+kGQAAAAAAAAAAAJweSTMAAAAAAAAAAAA4PZJmAAAAAAAAAAAAcHokzQAAAAAAAAAAAOD0SJoBAAAAAAAAAADA6ZE0AwAAAAAAAAAAgNMjaQYAAAAAAAAAAACnR9IMAAAAAAAAAAAATo+kGQAAAAAAAAAAAJweSTMAAAAAAAAAAAA4PZJmAAAAAAAAAAAAcHokzQAAAAAAAAAAAOD0SJoBAAAAAAAAAADA6ZE0AwAAAAAAAAAAgNMjaQYAAAAAAAAAAACnR9IMAAAAAAAAAAAATo+kGQAAAAAAAAAAAJweSTMAAAAAAAAAAAA4PZJmAAAAAAAAAAAAcHokzQAAAAAAAAAAAOD0SJoBAAAAAAAAAADA6ZE0AwAAAAAAAAAAgNMjaQYAAAAAAAAAAACn9/8AORm3NJ0jeXIAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "no_outlier = scored_df_pilot[scored_df_pilot['years_experience'] < 6]\n", + "no_outlier = no_outlier[no_outlier['years_experience'] > 0]\n", + "\n", + "print(no_outlier.shape, len(no_outlier))\n", + "\n", + "no_outlier_scores = []\n", + "\n", + "for r in range(len(no_outlier)):\n", + " no_outlier_scores.append(no_outlier.iloc[r, 3:].sum()/no_outlier.iloc[r, 3:].count())\n", + "\n", + "\n", + "fig, ax = plt.subplots(dpi=300)\n", + "ax.scatter(no_outlier['python_experience'], no_outlier_scores)\n", + "\n", + "lr = scipy.stats.linregress(no_outlier['python_experience'], no_outlier_scores)\n", + "\n", + "x = np.linspace(0, 4, 10)\n", + "y = lr.slope*x + lr.intercept\n", + "\n", + "plt.plot(x, y)\n", + "\n", + "print(f'r^2: {lr.rvalue}, p: {lr.pvalue}')\n", + "\n", + "ax.set_xlabel('Years of Python Experience')\n", + "ax.set_ylabel('Accuracy')\n", + "ax.set_xticks([0, 1, 2, 3, 4])\n", + "ax.set_yticks([0, 0.25, 0.5, 0.75, 1])\n", + "ax.set_title('Accuracy vs Python Experience - PILOT')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(32, 27) 32\n", + "r^2: nan, p: nan\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/02/8sm7hf812xn2pq_yy1kvs60m0000gn/T/ipykernel_53026/1888671336.py:9: RuntimeWarning: invalid value encountered in double_scalars\n", + " no_outlier_scores.append(no_outlier.iloc[r, 3:].sum()/no_outlier.iloc[r, 3:].count())\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "no_outlier = scored_df_main[scored_df_main['years_experience'] < 6]\n", + "no_outlier = no_outlier[no_outlier['years_experience'] > 0]\n", + "\n", + "print(no_outlier.shape, len(no_outlier))\n", + "\n", + "no_outlier_scores = []\n", + "\n", + "for r in range(len(no_outlier)):\n", + " no_outlier_scores.append(no_outlier.iloc[r, 3:].sum()/no_outlier.iloc[r, 3:].count())\n", + "\n", + "\n", + "fig, ax = plt.subplots(dpi=300)\n", + "ax.scatter(no_outlier['python_experience'], no_outlier_scores)\n", + "\n", + "lr = scipy.stats.linregress(no_outlier['python_experience'], no_outlier_scores)\n", + "\n", + "x = np.linspace(0, 4, 10)\n", + "y = lr.slope*x + lr.intercept\n", + "\n", + "plt.plot(x, y)\n", + "\n", + "print(f'r^2: {lr.rvalue}, p: {lr.pvalue}')\n", + "\n", + "ax.set_xlabel('Years of Python Experience')\n", + "ax.set_ylabel('Accuracy')\n", + "ax.set_xticks([0, 1, 2, 3, 4])\n", + "ax.set_yticks([0, 0.25, 0.5, 0.75, 1])\n", + "ax.set_title('Accuracy vs Python Experience - MAIN')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}