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- ANOVA(data=None, group_columns=None, alpha=0.05, group_name_column=None, group_value_column=None, group_names=None, num_groups=None, **generic_arguments)
- DESCRIPTION:
The ANOVA() function performs one-way ANOVA (Analysis of Variance) on
a data set with two or more groups. ANOVA is a statistical test that
analyzes the difference between the means of more than two groups.
The null hypothesis (H0) of ANOVA is that there is no difference among
group means. However, if any one of the group means is significantly
different from the overall mean, then the null hypothesis is rejected.
You can use one-way Anova when you have data on an independent variable
with at least three levels and a dependent variable.
For example, assume that your independent variable is insect spray type,
and you have data on spray type A, B, C, D, E, and F. You can use one-way
ANOVA to determine whether there is any difference in the dependent variable,
insect count based on the spray type used.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
group_columns:
Optional Argument.
Specifies the names of the columns in "data" to use in the computation.
Note:
Users must specify at least two columns in "group_columns" list.
Types: list of Strings (str)
alpha:
Optional Argument.
Specifies the probability of rejecting the null hypothesis when the null
hypothesis is true.
Default Value: 0.05
Types: float
group_name_column:
Optional Argument.
Specifies the column name in "data" containing the names of the groups
included in the computation.
Note:
* This argument is used when data contains group names in a column
and group values in another column.
* This argument must be used in conjunction with "group_value_column".
Types: str
group_value_column:
Optional Argument.
Specifies the column name in "data" containing the values for each group member.
Note:
* This argument is used when data contains group values in a column
and group names in another column.
* This argument must be used in conjunction with "group_name_column".
Types: str
group_names:
Optional Argument.
Specifies the names of the groups included in the computation.
Note:
* This argument is used when data contains group values in a column
and group names in another column.
Types: list of Strings (str)
num_groups:
Optional Argument.
Specifies the number of different groups in the "data" included
in the computation.
Note:
* This argument is used when data contains group values in a column
and group names in another column.
Types: int
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept. Below
are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile table or not. When set to
True, results are stored in a volatile table,
otherwise not.
Default Value: False
Types: bool
Function allows the user to partition, hash, order or local
order the input data. These generic arguments are available
for each argument that accepts teradataml DataFrame as
input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or
list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list
of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list
of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if
the underlying SQL Engine function supports, else an
exception is raised.
RETURNS:
Instance of ANOVA.
Output teradataml DataFrames can be accessed using attribute
references, such as ANOVAObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Load the example data.
load_example_data("teradataml", ["insect_sprays"])
load_example_data("ztest", 'insect2Cols')
# Create teradataml DataFrame objects.
insect_sprays = DataFrame.from_table("insect_sprays")
insect_gp = DataFrame.from_table("insect2Cols")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1 : Perform one-way anova analysis on a data set with
# two or more groups.
ANOVA_out_1 = ANOVA(data = insect_sprays,
alpha = 0.05
)
# Print the result DataFrame.
print(ANOVA_out_1.result)
# Example 2 : Perform one-way anova analysis on a data set with more
# than two groups and group_columns argument specified.
ANOVA_out_2 = ANOVA(data = insect_sprays,
group_columns=insect_sprays.columns[2:5],
alpha = 0.05
)
# Print the result DataFrame.
print(ANOVA_out_2.result)
# Example 3 : Perform one-way anova analysis on a data set with more
# than two groups and group_name_column, group_value_column,
# group_names.
ANOVA_out_3 = ANOVA(data = insect_gp,
group_name_column='groupName',
group_value_column='groupValue',
group_names=['groupA', 'groupB', 'groupC'])
# Print the result DataFrame.
print(ANOVA_out_3.result)
# Example 4 : Perform one-way anova analysis on a data set with more
# than two groups and num_groups.
ANOVA_out_4 = ANOVA(data = insect_gp,
group_name_column='groupName',
group_value_column='groupValue',
num_groups=6)
# Print the result DataFrame.
print(ANOVA_out_4.result)
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