@@ -157,7 +157,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio
157157 :widths: auto
158158
159159 ====== ===== ============= ======= ============= ========= ====== =======
160- T dof alternative p-val CI95% cohen-d BF10 power
160+ T dof alternative p_val CI95 cohen_d BF10 power
161161 ====== ===== ============= ======= ============= ========= ====== =======
162162 -3.401 58 two-sided 0.001 [-1.68 -0.43] 0.878 26.155 0.917
163163 ====== ===== ============= ======= ============= ========= ====== =======
@@ -175,7 +175,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio
175175 :widths: auto
176176
177177 === ===== =========== ======= ====== =======
178- n r CI95% p-val BF10 power
178+ n r CI95 p_val BF10 power
179179 === ===== =========== ======= ====== =======
180180 30 0.595 [0.3 0.79] 0.001 69.723 0.950
181181 === ===== =========== ======= ====== =======
@@ -196,7 +196,7 @@ Click on the link below and navigate to the notebooks/ folder to run a collectio
196196 :widths: auto
197197
198198 === ===== =========== ======= =======
199- n r CI95% p-val power
199+ n r CI95 p_val power
200200 === ===== =========== ======= =======
201201 30 0.576 [0.27 0.78] 0.001 0.933
202202 === ===== =========== ======= =======
@@ -244,7 +244,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
244244 :widths: auto
245245
246246 ======== ======= ==== ===== ======= ======= =======
247- Source SS DF MS F p-unc np2
247+ Source SS DF MS F p_unc np2
248248 ======== ======= ==== ===== ======= ======= =======
249249 Group 5.460 1 5.460 5.244 0.023 0.029
250250 Within 185.343 178 1.041 nan nan nan
@@ -263,7 +263,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
263263 :widths: auto
264264
265265 ======== ======= ==== ===== ======= ======= ======= =======
266- Source SS DF MS F p-unc ng2 eps
266+ Source SS DF MS F p_unc ng2 eps
267267 ======== ======= ==== ===== ======= ======= ======= =======
268268 Time 7.628 2 3.814 3.913 0.023 0.04 0.999
269269 Error 115.027 118 0.975 nan nan nan nan
@@ -287,7 +287,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
287287 :widths: auto
288288
289289 ========== ======= ======= ======== ============ ====== ====== ============= ======= ======== ========== ====== ========
290- Contrast A B Paired Parametric T dof alternative p-unc p-corr p-adjust BF10 hedges
290+ Contrast A B Paired Parametric T dof alternative p_unc p_corr p_adjust BF10 hedges
291291 ========== ======= ======= ======== ============ ====== ====== ============= ======= ======== ========== ====== ========
292292 Time August January True True -1.740 59.000 two-sided 0.087 0.131 fdr_bh 0.582 -0.328
293293 Time August June True True -2.743 59.000 two-sided 0.008 0.024 fdr_bh 4.232 -0.483
@@ -310,7 +310,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
310310 :widths: auto
311311
312312 =========== ===== ===== ===== ===== ===== ======= ===== =======
313- Source SS DF1 DF2 MS F p-unc np2 eps
313+ Source SS DF1 DF2 MS F p_unc np2 eps
314314 =========== ===== ===== ===== ===== ===== ======= ===== =======
315315 Group 5.460 1 58 5.460 5.052 0.028 0.080 nan
316316 Time 7.628 2 116 3.814 4.027 0.020 0.065 0.999
@@ -334,7 +334,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
334334 :widths: auto
335335
336336 === === ======== ============= === ===== ============= ======= ====== =======
337- X Y method alternative n r CI95% p-unc BF10 power
337+ X Y method alternative n r CI95 p_unc BF10 power
338338 === === ======== ============= === ===== ============= ======= ====== =======
339339 X Y pearson two-sided 30 0.366 [0.01 0.64] 0.047 1.500 0.525
340340 X Z pearson two-sided 30 0.251 [-0.12 0.56] 0.181 0.534 0.272
@@ -374,7 +374,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
374374 :widths: auto
375375
376376 ========= ====== ===== ====== ====== ===== ======== ========== ===========
377- names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]
377+ names coef se T pval r2 adj_r2 CI2.5 CI97.5
378378 ========= ====== ===== ====== ====== ===== ======== ========== ===========
379379 Intercept 4.650 0.841 5.530 0.000 0.139 0.076 2.925 6.376
380380 X 0.143 0.068 2.089 0.046 0.139 0.076 0.003 0.283
@@ -394,7 +394,7 @@ The `pingouin.normality` function works with lists, arrays, or pandas DataFrame
394394 :widths: auto
395395
396396 ======== ====== ===== ====== ========== =========== =====
397- path coef se pval CI[2.5%] CI[97.5%] sig
397+ path coef se pval CI2.5 CI97.5 sig
398398 ======== ====== ===== ====== ========== =========== =====
399399 Z ~ X 0.103 0.075 0.181 -0.051 0.256 No
400400 Y ~ Z 0.018 0.171 0.916 -0.332 0.369 No
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