1.8.1.15

• bug fix in z_normal test
• readme revision

1.8.1.14

Deal with capitalizing issue in article

1.8.1.13

---no update in functions---
update for the article

1.8.1.12

• little bug fixes
• Add Unittest files

1.8.1.10

Improvement

Fixed a case where the bottom and top of the y-axis were being set in an inappropriate way in some figure methods.

Fixed a bug in the results table of a linear regression where some numbers were too small and were being displayed as 0.000 due to .round(). Now, .round() is not used.

What's Changed

• merge for dev (start to dev 1.8.2.0) by @ckdckd145 in #10
• Merge for update 1.8.1.10 by @ckdckd145 in #11

Full Changelog: 1.8.1.9...1.8.1.10

1.8.1.9

Bug fix

• Fixed a bug that prevented t-statistic from being printed in linear regression and hierarchical linear regression coefficient tables

What's Changed

• merging for dev 1.8.1.9 by @ckdckd145 in #8
• Merge for version update : 1.8.1.9 by @ckdckd145 in #9

Full Changelog: 1.8.1.8...1.8.1.9

1.8.1.8

Bug fix

• I found and fixed some typos in the printed sentence when the language is set to "eng".

Improvement

• The result of the linear regression will now show the standardized regression coefficient beta as well as the unstandardized regression coefficient. This improvement is also available in Hierarchical Linear Regression and Multivariate Linear Regression.
• The revised results table is shown below:

	unstandadrized coefficient	standard error	standardized coefficient beta	p-value	95% CI Low	95% CI High
const	382.914	90.805	0.0000000	0.000	196.261	569.567
age	-0.666	2.889	-0.0435420	0.819	-6.604	5.272
prescore	-5.272	6.151	-0.1663700	0.399	-17.917	7.372
dummy_male	-45.919	34.491	-0.2586170	0.195	-116.816	24.977

What's Changed

• forgot to merge article file by @ckdckd145 in #6
• merge for update : 1.8.1.8 by @ckdckd145 in #7

Full Changelog: 1.8.1.7...1.8.1.8

1.8.1.7

Improvement

• Now, the nominal variables provided as covariates in One-way ANCOVA and Repeated-Measures ANCOVA are automatically dummy-coded.
• Add docstrings for classes and methods.

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개선

• ANCOVA 분석에서 투입된 공변량 중 명목변수들이 이제 자동으로 더미코딩됩니다.
• 각종 클래스와 메소드에 독스트링이 추가되었습니다.

1.8.1.6

Bug fix

I found a bug where if you run an analysis by applying a selector parameter in the .progress() method, and then immediately change the selector parameter again and run it once more, the filter is applied twice, rather than applying the new selector on the original data. This has now been fixed, and the selector works fine.

Improvement

In some analysis, I've changed the format of the result printed from str to pd.DataFrame, which should make it easier to see the results more clearly.

The following analyses are affected by this change:

• independent samples t-test
• dependent samples t-test
• Welch’s two sample t-test
• Yuen’s two sample t-test
• Mann-Whitney U test
• Brunner-Munzel Test
• Wilcoxon-Signed Rank Test
• Kruskal Wallis Test
• Friedman Test

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버그 픽스

.progress() 메소드에서 selector 파라미터를 한 번 적용한 후, 다시 selector 파라미터를 변경하여 적용할 때 이중으로 필터가 걸리는 현상을 발견했습니다.

수정하였으며, 이제 정상 작동합니다.

개선

일부 분석에서 결과가 출력되는 방식을 str에서 pd.DataFrame으로 바꿨습니다.

이제 조금 더 보기 편할 것으로 보입니다.

해당 변경이 적용된 분석은 아래와 같습니다.

• independent samples t-test
• dependent samples t-test
• Welch’s two sample t-test
• Yuen’s two sample t-test
• Mann-Whitney U test
• Brunner-Munzel Test
• Wilcoxon-Signed Rank Test
• Kruskal Wallis Test
• Friedman Test

1.8.1.5

Improvement

Readability of tables printed as results in Linear or Logistic Regression were improved.

Columns were renamed, and DataFrame were restructured.

The mapping logic applied to the dependent variable in multinomial logistic regression is printed alongside.

See the examples as below:

	Summary
Model:	OLS
Dependent Variable:	postscore
Date:	2024-01-10 15:08
No. Observations:	30
Df Model:	4
Df Residuals:	25
R-squared:	0.209
Adj. R-squared:	0.083
AIC:	151.1306
BIC:	158.1366
Log-Likelihood:	-70.565
F-statistic:	1.656
Prob (F-statistic):	0.192
Scale:	7.7586
Omnibus:	2.238
Prob(Omnibus):	0.327
Skew:	0.535
Kurtosis:	2.34
Durbin-Watson:	1.752
Jarque-Bera (JB):	1.973
Prob(JB):	0.373
Condition No.:	2323

	coefficient	standard error	t	p-value	95% CI Low	95% CI High
const	10.828	3.582	3.023	0.006	3.451	18.205
age	-0.168	0.088	-1.908	0.068	-0.349	0.013
income	-0.002	0.006	-0.252	0.803	-0.014	0.011
prescore	-0.116	0.19	-0.614	0.545	-0.507	0.274
dummy__male	-1.769	1.084	-1.633	0.115	-4.001	0.463

New analysis

Welch’s T-test is available. This is a t-test similar to Yuen's t-test that is applied to data that meets the normality assumption but does not meet the homoskedasticity assumption. Args for method in .progress() is ttest_ind_welch.

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개선 사항

선형 회귀 및 로지스틱 회귀에서 출력되는 결과 테이블의 가독성을 개선했습니다.

데이터프레임이 재구성되었으며, 이해를 돕기 위해 열의 이름도 변경했습니다.

그리고, 다항로지스틱회귀에서 종속변수에 적용되는 맵핑 로직이 함께 출력됩니다.

아래 예시를 참조하세요:

	Summary
Model:	OLS
Dependent Variable:	postscore
Date:	2024-01-10 15:08
No. Observations:	30
Df Model:	4
Df Residuals:	25
R-squared:	0.209
Adj. R-squared:	0.083
AIC:	151.1306
BIC:	158.1366
Log-Likelihood:	-70.565
F-statistic:	1.656
Prob (F-statistic):	0.192
Scale:	7.7586
Omnibus:	2.238
Prob(Omnibus):	0.327
Skew:	0.535
Kurtosis:	2.34
Durbin-Watson:	1.752
Jarque-Bera (JB):	1.973
Prob(JB):	0.373
Condition No.:	2323

	coefficient	standard error	t	p-value	95% CI Low	95% CI High
const	10.828	3.582	3.023	0.006	3.451	18.205
age	-0.168	0.088	-1.908	0.068	-0.349	0.013
income	-0.002	0.006	-0.252	0.803	-0.014	0.011
prescore	-0.116	0.19	-0.614	0.545	-0.507	0.274
dummy__male	-1.769	1.084	-1.633	0.115	-4.001	0.463

추가된 분석

Welch’s T-test가 추가되었습니다. Yuen’s T-test와 유사하게 정규성 가정은 충족하지만, 등분산성 가정을 충족하지 못하는 데이터 세트에 적용하는 T-test입니다. .progress() 메소드에서 method 파라미터에 ttest_ind_welch를 제공하면 됩니다.