|
@@ 257-283 (lines=27) @@
|
| 254 |
|
+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
| 255 |
|
print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
| 256 |
|
|
| 257 |
|
def test_confidence_interval_with_sample_simulation(self): |
| 258 |
|
grp1_sample = Sample() |
| 259 |
|
grp2_sample = Sample() |
| 260 |
|
|
| 261 |
|
for i in range(10): |
| 262 |
|
if random() <= 0.6: |
| 263 |
|
grp1_sample.add_category("OK") |
| 264 |
|
else: |
| 265 |
|
grp1_sample.add_category("CANCEL") |
| 266 |
|
|
| 267 |
|
for i in range(9): |
| 268 |
|
if random() <= 0.61: |
| 269 |
|
grp2_sample.add_category("OK") |
| 270 |
|
else: |
| 271 |
|
grp2_sample.add_category("CANCEL") |
| 272 |
|
|
| 273 |
|
sampling_distribution = ProportionDiffSamplingDistribution( |
| 274 |
|
grp1_sample_distribution=SampleDistribution(grp1_sample, |
| 275 |
|
categorical_value="OK"), |
| 276 |
|
grp2_sample_distribution=SampleDistribution( |
| 277 |
|
grp2_sample, |
| 278 |
|
categorical_value="OK") |
| 279 |
|
) |
| 280 |
|
self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.simulation) |
| 281 |
|
print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
| 282 |
|
+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
| 283 |
|
print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
| 284 |
|
|
| 285 |
|
|
| 286 |
|
if __name__ == '__main__': |
|
@@ 217-240 (lines=24) @@
|
| 214 |
|
+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
| 215 |
|
print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
| 216 |
|
|
| 217 |
|
def test_confidence_interval_with_sample_normal(self): |
| 218 |
|
grp1_sample = Sample() |
| 219 |
|
grp2_sample = Sample() |
| 220 |
|
|
| 221 |
|
for i in range(100): |
| 222 |
|
if random() <= 0.6: |
| 223 |
|
grp1_sample.add_category("OK") |
| 224 |
|
else: |
| 225 |
|
grp1_sample.add_category("CANCEL") |
| 226 |
|
|
| 227 |
|
for i in range(100): |
| 228 |
|
if random() <= 0.61: |
| 229 |
|
grp2_sample.add_category("OK") |
| 230 |
|
else: |
| 231 |
|
grp2_sample.add_category("CANCEL") |
| 232 |
|
|
| 233 |
|
sampling_distribution = ProportionDiffSamplingDistribution(grp1_sample_distribution=SampleDistribution( |
| 234 |
|
grp1_sample, categorical_value="OK"), |
| 235 |
|
grp2_sample_distribution=SampleDistribution( |
| 236 |
|
grp2_sample, categorical_value="OK")) |
| 237 |
|
self.assertEqual(sampling_distribution.distribution_family, DistributionFamily.normal) |
| 238 |
|
print('sampling distribution: (point_estimate = ' + str(sampling_distribution.point_estimate) |
| 239 |
|
+ ', standard_error = ' + str(sampling_distribution.standard_error) + ')') |
| 240 |
|
print('confidence level for 95% confidence level: ' + str(sampling_distribution.confidence_interval(0.95))) |
| 241 |
|
|
| 242 |
|
def test_confidence_interval_with_sample_stats_simulation(self): |
| 243 |
|
grp1_sample_proportion = 0.6 |
chen0040 /
pysie
