- "md": "# Bartlett's Test\r\nThis function tests if k sample are from populations with equal variances.\r\n\r\n## Description\r\n\"In statistics, Bartlett's test (see Snedecor and Cochran, 1989) is used to test if k samples are from populations with equal variances. Equal variances across populations is called homoscedasticity or homogeneity of variances. Some statistical tests, for example the analysis of variance, assume that variances are equal across groups or samples. The Bartlett test can be used to verify that assumption.Bartlett's test is sensitive to departures from normality. That is, if the samples come from non-normal distributions, then Bartlett's test may simply be testing for non-normality. Levene's test and the Brown Forsythe test are alternatives to the Bartlett test that are less sensitive to departures from normality. The test is named after Maurice Stevenson Bartlett.\" \r\n\r\nReference:\r\n+ <https://en.wikipedia.org/wiki/Bartlett%27s_test>\r\n\r\n## Properties\r\n### VA\r\n#### INPUT\r\n1. **table**<b style=\"color:red\">*</b>: (Table) Data in a Table.\r\n#### OUTPUT\r\n1. **model**: (Model) Result of Bartlett's Test with estimate and p_value responding to `Factor Column`.\r\n#### PARAMETER\r\n1. **Response Columns**<b style=\"color:red\">*</b>: Response columns. It should contain numeric values.\r\n\r\n2. **Factor Column**<b style=\"color:red\">*</b>: Factor column. It should contain categorical values as in forms of *int* or *str*\r\n\r\n\r\n### Python\r\n#### USAGE\r\n\r\n```\r\nbartletts_test(table, response_cols, factor_col)\r\n```\r\n\r\n#### INPUT\r\n1. **table**<b style=\"color:red\">*</b>: (Table) Data in a Table.\r\n#### OUTPUT\r\n1. **model**: (Model) Result of Bartlett's Test with estimate and p_value responding to `factor_col`.\r\n#### PARAMETER\r\n1. **response_cols**<b style=\"color:red\">*</b>: Response columns.\r\n\t* Type: *list[str]*\r\n2. **factor_col**<b style=\"color:red\">*</b>: Factor column.\r\n\t* Type: *str*\r\n\r\n## Example\r\n### VA\r\n\r\n**<a href=\"https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/24_sns_usage_time?type=insight\" target=\"_blank\">[Related Tutorial]</a>**\r\n\r\n\r\n**<a href=\"/api/va/v2/help/downloads/brightics.function.statistics$bartletts_test19124/example-model/brightics.function.statistics$bartletts_test.json\" download>[Sample Model]</a>**\r\n\r\n\r\n<img src=\"/api/va/v2/help/images/brightics.function.statistics$bartletts_test19124/brightics.function.statistics$bartletts_test.PNG\" width=\"800px\" style=\"border: 1px solid gray\" >\r\n\r\nIn this tutorial workflow, sample_iris data is used for Bartlett's Test. The generated model shows estimate and p_value of sepal_length, and sepal_width respect to species. The parameter settings used in the function are shown below.\r\n\r\n\r\n\r\n++Parameters++\r\n1. **Response Columns**<b style=\"color:red\">*</b>: sepal_length, sepal_width\r\n2. **Factor Column**<b style=\"color:red\">*</b>: species\r\n\r\n\r\n### Python\r\n\r\n```\r\nfrom brightics.function.statistics import anova\r\ninput_table=inputs[0]\r\nresult = bartletts_test(table=input_table , \r\n response_cols=['sepal_length', 'sepal_width'], \r\n factor_col='species')\r\noutput=result['result']\r\n```\r\nIn this python script, sample_iris data is used for Bartlett's Test. The generated model shows estimate and p_value of sepal_length, and sepal_width respect to species.\r\n",
0 commit comments