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- FTest(data=None, alpha=None, first_sample_variance=None, first_sample_column=None, df1=None, second_sample_variance=None, second_sample_column=None, df2=2, alternate_hypothesis='two-tailed', **generic_arguments)
- DESCRIPTION:
The FTest() function performs an F-test, for which the test statistic follows an
F-distribution under the Null hypothesis.
Function compares the variances of two independent populations.
If the variances are significantly different, the FTest() function rejects the
Null hypothesis, indicating that the variances may not come from the same
underlying population.
Use the function to compare statistical models that have been fitted to a
data set, to identify the model that best fits the population from which the data
were sampled.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
alpha:
Optional Argument.
Specifies the probability of rejecting the null hypothesis when it is true
(value below which null hypothesis is rejected).
"alpha" must be a numeric value in the range [0, 1].
Default Value: 0.05
Types: float
first_sample_column:
Required if "first_sample_variance" is omitted, disallowed otherwise.
Specifies the name of the input column that contains the data for the
first sample population.
Types: str
first_sample_variance:
Required if "first_sample_column" is omitted, disallowed otherwise.
Specifies the variance of the first sample population.
Types: float
df1:
Required if "first_sample_column" is omitted, disallowed otherwise.
Specifies the degrees of freedom of the first sample.
Types: integer
second_sample_column:
Required if "second_sample_variance" is omitted, disallowed otherwise.
Specifies the name of the input column that contains the data for the
second sample population.
Types: str
second_sample_variance:
Required if "second_sample_column" is omitted, disallowed otherwise.
Specifies the variance of the second sample population.
Types: float
df2:
Required if "second_sample_column" is omitted, disallowed otherwise.
Specifies the degrees of freedom of the second sample.
Types: integer
alternate_hypothesis:
Optional Argument.
Specifies the alternative hypothesis.
Permitted Values:
* lower-tailed - Alternate hypothesis (H 1): μ < μ0.
* upper-tailed - Alternate hypothesis (H 1): μ > μ0.
* two-tailed - Rejection region is on two sides of sampling distribution
of test statistic.
Two-tailed test considers both lower and upper tails of
distribution of test statistic.
Alternate hypothesis (H 1): μ ≠ μ0
Default Value: two-tailed
Types: str
**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 table or
not. When set to True, results are persisted in table; otherwise,
results are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in volatile table
or not. When set to True, results are stored in volatile table,
otherwise not.
Default Value: False
Types: boolean
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
SQLE Engine function supports, else an exception is raised.
RETURNS:
Instance of FTest.
Output teradataml DataFrames can be accessed using attribute
references, such as FTestObj.<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", "titanic")
# Create teradataml DataFrame object.
titanic_data = DataFrame.from_table("titanic")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1: Run FTest() with first_sample_variance, second_sample_variance,
# df1 and df2.
obj = FTest(data=titanic_data, alpha=0.5,
second_sample_column="parch",
alternate_hypothesis="two-tailed",
first_sample_variance=5,
second_sample_variance=8,
df1=1, df2=2
)
# Print the result DataFrame.
print(obj.result)
# Example 2: Run FTest() with only required arguments.
obj = FTest(data=titanic_data,
second_sample_column="parch",
second_sample_variance=8,
df2=2
)
# Print the result DataFrame.
print(obj.result)
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