hana_ml.algorithms.pal.stats.ttest_ind(data, col1=None, col2=None, mu=0, test_type='two_sides', var_equal=False, conf_level=0.95)

Perform the T-test for the mean difference of two independent samples.


DataFrame containing the data.

col1str, optional

Name of the column for sample1.

If not given, it defaults to the first column.

col2str, optional

Name of the column for sample2.

If not given, it defaults to the first non-col1 column.

mufloat, optional

Hypothesized difference between the two underlying population means.

Defaults to 0.

test_type{'two_sides', 'less', 'greater'}, optional

The alternative hypothesis type.

Defaults to 'two_sides'.

var_equalbool, optional

Controls whether to assume that the two samples have equal variance.

Defaults to False.

conf_levelfloat, optional

Confidence level for alternative hypothesis confidence interval.

Defaults to 0.95.


Statistics results.


Original data:

>>> df.collect()
    X1    X2
0  1.0  10.0
1  2.0  12.0
2  4.0  11.0
3  7.0  15.0
4  NaN  10.0

Perform Independent Sample T-Test:

>>> ttest_ind(data=df).collect()
0            t-value   -5.013774
1  degree of freedom    5.649757
2            p-value    0.002875
3      _PAL_MEAN_X1_    3.500000
4      _PAL_MEAN_X2_   11.600000
5   confidence level    0.950000
6         lowerLimit  -12.113278
7         upperLimit   -4.086722