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November 13, 2012 07:09
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{ | |
"metadata": { | |
"name": "Study #1 OU Group C" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Behaviour Change Analysis Sales vs Expected probability\n", | |
"\n", | |
"### Move to working directory" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"cd /Users/gonzillaaa/Dropbox/Code/thesis-analysis/2012-08-07thesis-analysis/analysis" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"/Users/gonzillaaa/Dropbox/Code/thesis-analysis/2012-08-07thesis-analysis/analysis\n" | |
] | |
} | |
], | |
"prompt_number": 19 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### import and run sales module code written so far" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"import pandas as pd\n", | |
"from behavior import change\n", | |
"pd.set_printoptions(notebook_repr_html=True, max_columns=45)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 20 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%run behavior/change.py" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 21 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### access ou and arup data under ou_ arup_ + (tab)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"sales_data = ou_sales_analysis\n", | |
"organisation_name = \"OU\"" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 22 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 2 - We focus on one group, create an easy access dataframe (df) for it and look at overall change" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"group = \"df_group_c_sales\"\n", | |
"df = sales_data.sales_per_group[group]\n", | |
"print df.keys()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"array([participant_id, experiment_id, sale_bottle, vote_time, sale_color,\n", | |
" sale_order, voted_ingroup, questionnaire_user_id,\n", | |
" questionnaire_phase, questionnaire_user_name,\n", | |
" questionnaire_user_surname, questionnaire_experiment_id,\n", | |
" questionnaire_user_age, questionnaire_user_gender,\n", | |
" how_often_buy_wine, where_buy_wine, how_many_bottles, average_spend,\n", | |
" wine_knowledge, change_discover, change_quantity, change_type,\n", | |
" change_amount, bottle_id, wine_type, table_id, bottle_display_id,\n", | |
" pixel, probability_score, pink, green, blue, nocolor], dtype=object)\n" | |
] | |
} | |
], | |
"prompt_number": 23 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"#we get rid of data columns we don't want\n", | |
"observed = df[[\"experiment_id\", \"participant_id\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"experiment_id\")\n", | |
"observed = observed.aggregate(np.sum)\n", | |
"observed = observed.reset_index()\n", | |
"\n", | |
"print\n", | |
"print \"group size :\", len(df[\"participant_id\"].unique())\n", | |
"testChangeInGroup(group, observed)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"group size : 25\n", | |
"Chi-square test p-value: 1.39439385104e-05 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 24 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"\n", | |
"### Wine knowledge" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": true, | |
"input": [ | |
"#we get rid of data columns we don't want\n", | |
"observed = df[[\"experiment_id\", \"participant_id\", \"wine_knowledge\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"wine_knowledge\")\n", | |
" \n", | |
"reportChangeInColumn(group, observed, \"Wine knowledge\")\n", | |
" " | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Wine knowledge: 0.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 6.52311220302e-05 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine knowledge: 1.0\n", | |
"group size : 7\n", | |
"Chi-square test p-value: 2.74566262007e-05 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine knowledge: 2.0\n", | |
"group size : 8\n", | |
"Chi-square test p-value: 0.000194534471021 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine knowledge: 3.0\n", | |
"group size : 8\n", | |
"Chi-square test p-value: 0.504566717012 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine knowledge: 4.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.416957464434 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 25 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###Gender" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"#we get rid of data columns we don't want\n", | |
"#print df\n", | |
"observed = df[[\"experiment_id\", \"participant_id\", \"questionnaire_user_gender\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"questionnaire_user_gender\")\n", | |
"\n", | |
"reportChangeInColumn(group, observed, \"Gender\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Gender: 1.0\n", | |
"group size : 16\n", | |
"Chi-square test p-value: 0.0614677954937 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Gender: 2.0\n", | |
"group size : 9\n", | |
"Chi-square test p-value: 4.11008904541e-05 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 26 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###Age" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"#we get rid of data columns we don't want\n", | |
"#print df\n", | |
"observed = df[[\"experiment_id\", \"participant_id\", \"questionnaire_user_age\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"questionnaire_user_age\")\n", | |
"reportChangeInColumn(group, observed, \"Age\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Age: 20.0\n", | |
"group size : 8\n", | |
"Chi-square test p-value: 0.000387513774371 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Age: 30.0\n", | |
"group size : 6\n", | |
"Chi-square test p-value: 0.0641329719938 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Age: 40.0\n", | |
"group size : 5\n", | |
"Chi-square test p-value: 0.46967107284 \n", | |
"\n", | |
"\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"---\n", | |
"Age: 50.0\n", | |
"group size : 4\n", | |
"Chi-square test p-value: 0.896085870503 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Age: 60.0\n", | |
"group size : 2\n", | |
"Chi-square test p-value: 3.40026554706e-05 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 27 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###How often buy wine" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": true, | |
"input": [ | |
"\n", | |
"observed = df[[\"experiment_id\", \"participant_id\", \"how_often_buy_wine\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"how_often_buy_wine\")\n", | |
"reportChangeInColumn(group, observed, \"How often buy wine\")\n" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"How often buy wine: 0.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.418978846474 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"How often buy wine: 1.0\n", | |
"group size : 12\n", | |
"Chi-square test p-value: 0.000203819520233 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"How often buy wine: 2.0\n", | |
"group size : 4\n", | |
"Chi-square test p-value: 0.0185576792801 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"How often buy wine: 3.0\n", | |
"group size : 6\n", | |
"Chi-square test p-value: 0.112467262178 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"\n", | |
"\n", | |
"---\n", | |
"How often buy wine: 4.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 8.18655595555e-07 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"How often buy wine: 5.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.193779733631 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 28 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###How many bottles" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": true, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"how_many_bottles\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"how_many_bottles\")\n", | |
"reportChangeInColumn(group, observed, \"How many bottles\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"How many bottles: 1.0\n", | |
"group size : 11\n", | |
"Chi-square test p-value: 0.001289195089 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"How many bottles: 2.0\n", | |
"group size : 12\n", | |
"Chi-square test p-value: 0.0264578617612 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"How many bottles: 4.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.193779733631 \n", | |
"\n", | |
"\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"---\n", | |
"How many bottles: 5.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.416957464434 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 29 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###Average Spend" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": true, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"average_spend\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"average_spend\")\n", | |
"reportChangeInColumn(group, observed, \"Average spend\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Average spend: 0.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.833703116189 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Average spend: 1.0\n", | |
"group size : 5\n", | |
"Chi-square test p-value: 0.000293529397163 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Average spend: 2.0\n", | |
"group size : 13\n", | |
"Chi-square test p-value: 0.00179266113524 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Average spend: 3.0\n", | |
"group size : 3\n", | |
"Chi-square test p-value: 0.0315546964851 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Average spend: 4.0\n", | |
"group size : 2\n", | |
"Chi-square test p-value: 0.0883571486689 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Average spend: 5.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.0718977724965 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 30 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###Wine type" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"#we get rid of data columns we don't want\n", | |
"#print df\n", | |
"observed = df[[\"experiment_id\", \"participant_id\", \"wine_type\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"wine_type\")\n", | |
"reportChangeInColumn(group, observed, \"Wine type\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Wine type: 1\n", | |
"group size : 22\n", | |
"Chi-square test p-value: 0.000337546498453 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine type: 2\n", | |
"group size : 12\n", | |
"Chi-square test p-value: 2.92019120478e-05 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 31 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###Wine price" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"table_id\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"table_id\")\n", | |
"reportChangeInColumn(group, observed, \"Wine price\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Wine price: 1\n", | |
"group size : 6\n", | |
"Chi-square test p-value: 0.233898012605 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine price: 2\n", | |
"group size : 8\n", | |
"Chi-square test p-value: 0.439791727846 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine price: 3\n", | |
"group size : 14\n", | |
"Chi-square test p-value: 0.000132125207844 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine price: 4\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.28388613076 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine price: 5\n", | |
"group size : 6\n", | |
"Chi-square test p-value: 0.943846505205 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Wine price: 6\n", | |
"group size : 7\n", | |
"Chi-square test p-value: 5.45303414624e-09 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 32 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###change_discover" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"change_discover\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"change_discover\")\n", | |
"reportChangeInColumn(group, observed, \"Change Discover\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Change Discover: 0.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.28388613076 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Discover: 1.0\n", | |
"group size : 2\n", | |
"Chi-square test p-value: 0.12065673634 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Discover: 2.0\n", | |
"group size : 5\n", | |
"Chi-square test p-value: 0.0514871680084 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Discover: 3.0\n", | |
"group size : 7\n", | |
"Chi-square test p-value: 0.0380980353744 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Discover: 4.0\n", | |
"group size : 6\n", | |
"Chi-square test p-value: 0.01044446374 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Discover: 5.0\n", | |
"group size : 4\n", | |
"Chi-square test p-value: 0.0116471981457 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 33 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###change_quantity" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"change_quantity\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"change_quantity\")\n", | |
"reportChangeInColumn(group, observed, \"Change Quantity\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Change Quantity: 0.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.28388613076 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Quantity: 1.0\n", | |
"group size : 10\n", | |
"Chi-square test p-value: 0.00105111880859 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Quantity: 2.0\n", | |
"group size : 8\n", | |
"Chi-square test p-value: 0.171209461002 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Quantity: 3.0\n", | |
"group size : 3\n", | |
"Chi-square test p-value: 0.026325488078 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Quantity: 4.0\n", | |
"group size : 2\n", | |
"Chi-square test p-value: 0.0676870282371 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Quantity: 5.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.933378895053 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 34 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###change_type" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"change_type\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"change_type\")\n", | |
"reportChangeInColumn(group, observed, \"Change Type\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Change Type: 0.0\n", | |
"group size : 2\n", | |
"Chi-square test p-value: 0.176744402129 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Type: 1.0\n", | |
"group size : 5\n", | |
"Chi-square test p-value: 0.285631923849 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Type: 2.0\n", | |
"group size : 7\n", | |
"Chi-square test p-value: 0.126345696396 \n", | |
"\n", | |
"\n" | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"---\n", | |
"Change Type: 3.0\n", | |
"group size : 6\n", | |
"Chi-square test p-value: 0.00433414724892 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Type: 4.0\n", | |
"group size : 3\n", | |
"Chi-square test p-value: 0.00263113048644 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Type: 5.0\n", | |
"group size : 2\n", | |
"Chi-square test p-value: 0.097011103381 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 35 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"###change_amount" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"observed = df[[\"experiment_id\", \"participant_id\", \"change_amount\", \"pink\", \"green\", \"blue\", \"nocolor\"]]\n", | |
"observed = observed.groupby(\"change_amount\")\n", | |
"reportChangeInColumn(group, observed, \"Change Amount\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"---\n", | |
"Change Amount: 0.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.28388613076 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Amount: 1.0\n", | |
"group size : 10\n", | |
"Chi-square test p-value: 0.00282470145503 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Amount: 2.0\n", | |
"group size : 9\n", | |
"Chi-square test p-value: 0.00208534275674 \n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Amount: 3.0\n", | |
"group size : 3\n", | |
"Chi-square test p-value: 5.08651483595e-06 " | |
] | |
}, | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"\n", | |
"change is significant for group, df_group_c_sales\n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Amount: 4.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.193779733631 \n", | |
"\n", | |
"\n", | |
"---\n", | |
"Change Amount: 5.0\n", | |
"group size : 1\n", | |
"Chi-square test p-value: 0.933378895053 \n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 36 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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