diff --git a/1-Introduction/04-stats-and-probability/DiabetesData/diabetes.tab.txt b/1-Introduction/04-stats-and-probability/DiabetesData/diabetes.tab.txt
new file mode 100644
index 000000000..980b3830e
--- /dev/null
+++ b/1-Introduction/04-stats-and-probability/DiabetesData/diabetes.tab.txt
@@ -0,0 +1,443 @@
+AGE	SEX	BMI	BP	S1	S2	S3	S4	S5	S6	Y
+59	2	32.1	101	157	93.2	38	4	4.8598	87	151
+48	1	21.6	87	183	103.2	70	3	3.8918	69	75
+72	2	30.5	93	156	93.6	41	4	4.6728	85	141
+24	1	25.3	84	198	131.4	40	5	4.8903	89	206
+50	1	23	101	192	125.4	52	4	4.2905	80	135
+23	1	22.6	89	139	64.8	61	2	4.1897	68	97
+36	2	22	90	160	99.6	50	3	3.9512	82	138
+66	2	26.2	114	255	185	56	4.55	4.2485	92	63
+60	2	32.1	83	179	119.4	42	4	4.4773	94	110
+29	1	30	85	180	93.4	43	4	5.3845	88	310
+22	1	18.6	97	114	57.6	46	2	3.9512	83	101
+56	2	28	85	184	144.8	32	6	3.5835	77	69
+53	1	23.7	92	186	109.2	62	3	4.3041	81	179
+50	2	26.2	97	186	105.4	49	4	5.0626	88	185
+61	1	24	91	202	115.4	72	3	4.2905	73	118
+34	2	24.7	118	254	184.2	39	7	5.037	81	171
+47	1	30.3	109	207	100.2	70	3	5.2149	98	166
+68	2	27.5	111	214	147	39	5	4.9416	91	144
+38	1	25.4	84	162	103	42	4	4.4427	87	97
+41	1	24.7	83	187	108.2	60	3	4.5433	78	168
+35	1	21.1	82	156	87.8	50	3	4.5109	95	68
+25	2	24.3	95	162	98.6	54	3	3.8501	87	49
+25	1	26	92	187	120.4	56	3	3.9703	88	68
+61	2	32	103.67	210	85.2	35	6	6.107	124	245
+31	1	29.7	88	167	103.4	48	4	4.3567	78	184
+30	2	25.2	83	178	118.4	34	5	4.852	83	202
+19	1	19.2	87	124	54	57	2	4.1744	90	137
+42	1	31.9	83	158	87.6	53	3	4.4659	101	85
+63	1	24.4	73	160	91.4	48	3	4.6347	78	131
+67	2	25.8	113	158	54.2	64	2	5.2933	104	283
+32	1	30.5	89	182	110.6	56	3	4.3438	89	129
+42	1	20.3	71	161	81.2	66	2	4.2341	81	59
+58	2	38	103	150	107.2	22	7	4.6444	98	341
+57	1	21.7	94	157	58	82	2	4.4427	92	87
+53	1	20.5	78	147	84.2	52	3	3.989	75	65
+62	2	23.5	80.33	225	112.8	86	2.62	4.8752	96	102
+52	1	28.5	110	195	97.2	60	3	5.2417	85	265
+46	1	27.4	78	171	88	58	3	4.8283	90	276
+48	2	33	123	253	163.6	44	6	5.425	97	252
+48	2	27.7	73	191	119.4	46	4	4.852	92	90
+50	2	25.6	101	229	162.2	43	5	4.7791	114	100
+21	1	20.1	63	135	69	54	3	4.0943	89	55
+32	2	25.4	90.33	153	100.4	34	4.5	4.5326	83	61
+54	1	24.2	74	204	109	82	2	4.1744	109	92
+61	2	32.7	97	177	118.4	29	6	4.9972	87	259
+56	2	23.1	104	181	116.4	47	4	4.4773	79	53
+33	1	25.3	85	155	85	51	3	4.5539	70	190
+27	1	19.6	78	128	68	43	3	4.4427	71	142
+67	2	22.5	98	191	119.2	61	3	3.989	86	75
+37	2	27.7	93	180	119.4	30	6	5.0304	88	142
+58	1	25.7	99	157	91.6	49	3	4.4067	93	155
+65	2	27.9	103	159	96.8	42	4	4.6151	86	225
+34	1	25.5	93	218	144	57	4	4.4427	88	59
+46	1	24.9	115	198	129.6	54	4	4.2767	103	104
+35	1	28.7	97	204	126.8	64	3	4.1897	93	182
+37	1	21.8	84	184	101	73	3	3.912	93	128
+37	1	30.2	87	166	96	40	4.15	5.0106	87	52
+41	1	20.5	80	124	48.8	64	2	4.0254	75	37
+60	1	20.4	105	198	78.4	99	2	4.6347	79	170
+66	2	24	98	236	146.4	58	4	5.0626	96	170
+29	1	26	83	141	65.2	64	2	4.0775	83	61
+37	2	26.8	79	157	98	28	6	5.0434	96	144
+41	2	25.7	83	181	106.6	66	3	3.7377	85	52
+39	1	22.9	77	204	143.2	46	4	4.3041	74	128
+67	2	24	83	143	77.2	49	3	4.4308	94	71
+36	2	24.1	112	193	125	35	6	5.1059	95	163
+46	2	24.7	85	174	123.2	30	6	4.6444	96	150
+60	2	25	89.67	185	120.8	46	4.02	4.5109	92	97
+59	2	23.6	83	165	100	47	4	4.4998	92	160
+53	1	22.1	93	134	76.2	46	3	4.0775	96	178
+48	1	19.9	91	189	109.6	69	3	3.9512	101	48
+48	1	29.5	131	207	132.2	47	4	4.9345	106	270
+66	2	26	91	264	146.6	65	4	5.5683	87	202
+52	2	24.5	94	217	149.4	48	5	4.585	89	111
+52	2	26.6	111	209	126.4	61	3	4.6821	109	85
+46	2	23.5	87	181	114.8	44	4	4.7095	98	42
+40	2	29	115	97	47.2	35	2.77	4.3041	95	170
+22	1	23	73	161	97.8	54	3	3.8286	91	200
+50	1	21	88	140	71.8	35	4	5.112	71	252
+20	1	22.9	87	191	128.2	53	4	3.8918	85	113
+68	1	27.5	107	241	149.6	64	4	4.92	90	143
+52	2	24.3	86	197	133.6	44	5	4.5747	91	51
+44	1	23.1	87	213	126.4	77	3	3.8712	72	52
+38	1	27.3	81	146	81.6	47	3	4.4659	81	210
+49	1	22.7	65.33	168	96.2	62	2.71	3.8918	60	65
+61	1	33	95	182	114.8	54	3	4.1897	74	141
+29	2	19.4	83	152	105.8	39	4	3.5835	83	55
+61	1	25.8	98	235	125.8	76	3	5.112	82	134
+34	2	22.6	75	166	91.8	60	3	4.2627	108	42
+36	1	21.9	89	189	105.2	68	3	4.3694	96	111
+52	1	24	83	167	86.6	71	2	3.8501	94	98
+61	1	31.2	79	235	156.8	47	5	5.0499	96	164
+43	1	26.8	123	193	102.2	67	3	4.7791	94	48
+35	1	20.4	65	187	105.6	67	2.79	4.2767	78	96
+27	1	24.8	91	189	106.8	69	3	4.1897	69	90
+29	1	21	71	156	97	38	4	4.654	90	162
+64	2	27.3	109	186	107.6	38	5	5.3083	99	150
+41	1	34.6	87.33	205	142.6	41	5	4.6728	110	279
+49	2	25.9	91	178	106.6	52	3	4.5747	75	92
+48	1	20.4	98	209	139.4	46	5	4.7707	78	83
+53	1	28	88	233	143.8	58	4	5.0499	91	128
+53	2	22.2	113	197	115.2	67	3	4.3041	100	102
+23	1	29	90	216	131.4	65	3	4.585	91	302
+65	2	30.2	98	219	160.6	40	5	4.5218	84	198
+41	1	32.4	94	171	104.4	56	3	3.9703	76	95
+55	2	23.4	83	166	101.6	46	4	4.5218	96	53
+22	1	19.3	82	156	93.2	52	3	3.989	71	134
+56	1	31	78.67	187	141.4	34	5.5	4.0604	90	144
+54	2	30.6	103.33	144	79.8	30	4.8	5.1417	101	232
+59	2	25.5	95.33	190	139.4	35	5.43	4.3567	117	81
+60	2	23.4	88	153	89.8	58	3	3.2581	95	104
+54	1	26.8	87	206	122	68	3	4.382	80	59
+25	1	28.3	87	193	128	49	4	4.382	92	246
+54	2	27.7	113	200	128.4	37	5	5.1533	113	297
+55	1	36.6	113	199	94.4	43	4.63	5.7301	97	258
+40	2	26.5	93	236	147	37	7	5.5607	92	229
+62	2	31.8	115	199	128.6	44	5	4.8828	98	275
+65	1	24.4	120	222	135.6	37	6	5.5094	124	281
+33	2	25.4	102	206	141	39	5	4.8675	105	179
+53	1	22	94	175	88	59	3	4.9416	98	200
+35	1	26.8	98	162	103.6	45	4	4.2047	86	200
+66	1	28	101	195	129.2	40	5	4.8598	94	173
+62	2	33.9	101	221	156.4	35	6	4.9972	103	180
+50	2	29.6	94.33	300	242.4	33	9.09	4.8122	109	84
+47	1	28.6	97	164	90.6	56	3	4.4659	88	121
+47	2	25.6	94	165	74.8	40	4	5.5255	93	161
+24	1	20.7	87	149	80.6	61	2	3.6109	78	99
+58	2	26.2	91	217	124.2	71	3	4.6913	68	109
+34	1	20.6	87	185	112.2	58	3	4.3041	74	115
+51	1	27.9	96	196	122.2	42	5	5.0689	120	268
+31	2	35.3	125	187	112.4	48	4	4.8903	109	274
+22	1	19.9	75	175	108.6	54	3	4.1271	72	158
+53	2	24.4	92	214	146	50	4	4.4998	97	107
+37	2	21.4	83	128	69.6	49	3	3.8501	84	83
+28	1	30.4	85	198	115.6	67	3	4.3438	80	103
+47	1	31.6	84	154	88	30	5.1	5.1985	105	272
+23	1	18.8	78	145	72	63	2	3.912	86	85
+50	1	31	123	178	105	48	4	4.8283	88	280
+58	2	36.7	117	166	93.8	44	4	4.9488	109	336
+55	1	32.1	110	164	84.2	42	4	5.2417	90	281
+60	2	27.7	107	167	114.6	38	4	4.2767	95	118
+41	1	30.8	81	214	152	28	7.6	5.1358	123	317
+60	2	27.5	106	229	143.8	51	4	5.1417	91	235
+40	1	26.9	92	203	119.8	70	3	4.1897	81	60
+57	2	30.7	90	204	147.8	34	6	4.7095	93	174
+37	1	38.3	113	165	94.6	53	3	4.4659	79	259
+40	2	31.9	95	198	135.6	38	5	4.804	93	178
+33	1	35	89	200	130.4	42	4.76	4.9273	101	128
+32	2	27.8	89	216	146.2	55	4	4.3041	91	96
+35	2	25.9	81	174	102.4	31	6	5.3132	82	126
+55	1	32.9	102	164	106.2	41	4	4.4308	89	288
+49	1	26	93	183	100.2	64	3	4.5433	88	88
+39	2	26.3	115	218	158.2	32	7	4.9345	109	292
+60	2	22.3	113	186	125.8	46	4	4.2627	94	71
+67	2	28.3	93	204	132.2	49	4	4.7362	92	197
+41	2	32	109	251	170.6	49	5	5.0562	103	186
+44	1	25.4	95	162	92.6	53	3	4.4067	83	25
+48	2	23.3	89.33	212	142.8	46	4.61	4.7536	98	84
+45	1	20.3	74.33	190	126.2	49	3.88	4.3041	79	96
+47	1	30.4	120	199	120	46	4	5.1059	87	195
+46	1	20.6	73	172	107	51	3	4.2485	80	53
+36	2	32.3	115	286	199.4	39	7	5.4723	112	217
+34	1	29.2	73	172	108.2	49	4	4.3041	91	172
+53	2	33.1	117	183	119	48	4	4.382	106	131
+61	1	24.6	101	209	106.8	77	3	4.8363	88	214
+37	1	20.2	81	162	87.8	63	3	4.0254	88	59
+33	2	20.8	84	125	70.2	46	3	3.7842	66	70
+68	1	32.8	105.67	205	116.4	40	5.13	5.4931	117	220
+49	2	31.9	94	234	155.8	34	7	5.3982	122	268
+48	1	23.9	109	232	105.2	37	6	6.107	96	152
+55	2	24.5	84	179	105.8	66	3	3.5835	87	47
+43	1	22.1	66	134	77.2	45	3	4.0775	80	74
+60	2	33	97	217	125.6	45	5	5.4467	112	295
+31	2	19	93	137	73	47	3	4.4427	78	101
+53	2	27.3	82	119	55	39	3	4.8283	93	151
+67	1	22.8	87	166	98.6	52	3	4.3438	92	127
+61	2	28.2	106	204	132	52	4	4.6052	96	237
+62	1	28.9	87.33	206	127.2	33	6.24	5.4337	99	225
+60	1	25.6	87	207	125.8	69	3	4.1109	84	81
+42	1	24.9	91	204	141.8	38	5	4.7958	89	151
+38	2	26.8	105	181	119.2	37	5	4.8203	91	107
+62	1	22.4	79	222	147.4	59	4	4.3567	76	64
+61	2	26.9	111	236	172.4	39	6	4.8122	89	138
+61	2	23.1	113	186	114.4	47	4	4.8122	105	185
+53	1	28.6	88	171	98.8	41	4	5.0499	99	265
+28	2	24.7	97	175	99.6	32	5	5.3799	87	101
+26	2	30.3	89	218	152.2	31	7	5.1591	82	137
+30	1	21.3	87	134	63	63	2	3.6889	66	143
+50	1	26.1	109	243	160.6	62	4	4.625	89	141
+48	1	20.2	95	187	117.4	53	4	4.4188	85	79
+51	1	25.2	103	176	112.2	37	5	4.8978	90	292
+47	2	22.5	82	131	66.8	41	3	4.7536	89	178
+64	2	23.5	97	203	129	59	3	4.3175	77	91
+51	2	25.9	76	240	169	39	6	5.0752	96	116
+30	1	20.9	104	152	83.8	47	3	4.6634	97	86
+56	2	28.7	99	208	146.4	39	5	4.7274	97	122
+42	1	22.1	85	213	138.6	60	4	4.2767	94	72
+62	2	26.7	115	183	124	35	5	4.7875	100	129
+34	1	31.4	87	149	93.8	46	3	3.8286	77	142
+60	1	22.2	104.67	221	105.4	60	3.68	5.6276	93	90
+64	1	21	92.33	227	146.8	65	3.49	4.3307	102	158
+39	2	21.2	90	182	110.4	60	3	4.0604	98	39
+71	2	26.5	105	281	173.6	55	5	5.5683	84	196
+48	2	29.2	110	218	151.6	39	6	4.92	98	222
+79	2	27	103	169	110.8	37	5	4.6634	110	277
+40	1	30.7	99	177	85.4	50	4	5.3375	85	99
+49	2	28.8	92	207	140	44	5	4.7449	92	196
+51	1	30.6	103	198	106.6	57	3	5.1475	100	202
+57	1	30.1	117	202	139.6	42	5	4.625	120	155
+59	2	24.7	114	152	104.8	29	5	4.5109	88	77
+51	1	27.7	99	229	145.6	69	3	4.2767	77	191
+74	1	29.8	101	171	104.8	50	3	4.3944	86	70
+67	1	26.7	105	225	135.4	69	3	4.6347	96	73
+49	1	19.8	88	188	114.8	57	3	4.3944	93	49
+57	1	23.3	88	155	63.6	78	2	4.2047	78	65
+56	2	35.1	123	164	95	38	4	5.0434	117	263
+52	2	29.7	109	228	162.8	31	8	5.1417	103	248
+69	1	29.3	124	223	139	54	4	5.0106	102	296
+37	1	20.3	83	185	124.6	38	5	4.7185	88	214
+24	1	22.5	89	141	68	52	3	4.654	84	185
+55	2	22.7	93	154	94.2	53	3	3.5264	75	78
+36	1	22.8	87	178	116	41	4	4.654	82	93
+42	2	24	107	150	85	44	3	4.654	96	252
+21	1	24.2	76	147	77	53	3	4.4427	79	150
+41	1	20.2	62	153	89	50	3	4.2485	89	77
+57	2	29.4	109	160	87.6	31	5	5.3327	92	208
+20	2	22.1	87	171	99.6	58	3	4.2047	78	77
+67	2	23.6	111.33	189	105.4	70	2.7	4.2195	93	108
+34	1	25.2	77	189	120.6	53	4	4.3438	79	160
+41	2	24.9	86	192	115	61	3	4.382	94	53
+38	2	33	78	301	215	50	6.02	5.193	108	220
+51	1	23.5	101	195	121	51	4	4.7449	94	154
+52	2	26.4	91.33	218	152	39	5.59	4.9053	99	259
+67	1	29.8	80	172	93.4	63	3	4.3567	82	90
+61	1	30	108	194	100	52	3.73	5.3471	105	246
+67	2	25	111.67	146	93.4	33	4.42	4.585	103	124
+56	1	27	105	247	160.6	54	5	5.0876	94	67
+64	1	20	74.67	189	114.8	62	3.05	4.1109	91	72
+58	2	25.5	112	163	110.6	29	6	4.7622	86	257
+55	1	28.2	91	250	140.2	67	4	5.366	103	262
+62	2	33.3	114	182	114	38	5	5.0106	96	275
+57	2	25.6	96	200	133	52	3.85	4.3175	105	177
+20	2	24.2	88	126	72.2	45	3	3.7842	74	71
+53	2	22.1	98	165	105.2	47	4	4.1589	81	47
+32	2	31.4	89	153	84.2	56	3	4.1589	90	187
+41	1	23.1	86	148	78	58	3	4.0943	60	125
+60	1	23.4	76.67	247	148	65	3.8	5.1358	77	78
+26	1	18.8	83	191	103.6	69	3	4.5218	69	51
+37	1	30.8	112	282	197.2	43	7	5.3423	101	258
+45	1	32	110	224	134.2	45	5	5.4116	93	215
+67	1	31.6	116	179	90.4	41	4	5.4723	100	303
+34	2	35.5	120	233	146.6	34	7	5.5683	101	243
+50	1	31.9	78.33	207	149.2	38	5.45	4.5951	84	91
+71	1	29.5	97	227	151.6	45	5	5.0239	108	150
+57	2	31.6	117	225	107.6	40	6	5.9584	113	310
+49	1	20.3	93	184	103	61	3	4.6052	93	153
+35	1	41.3	81	168	102.8	37	5	4.9488	94	346
+41	2	21.2	102	184	100.4	64	3	4.585	79	63
+70	2	24.1	82.33	194	149.2	31	6.26	4.2341	105	89
+52	1	23	107	179	123.7	42.5	4.21	4.1589	93	50
+60	1	25.6	78	195	95.4	91	2	3.7612	87	39
+62	1	22.5	125	215	99	98	2	4.4998	95	103
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+54	2	36.1	115	163	98.4	43	4	4.6821	101	321
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+36	2	39.1	90	219	135.8	38	6	5.4205	103	233
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+44	2	26.6	99	205	109	43	5	5.5797	111	123
+46	1	29.9	83	171	113	38	4.5	4.585	98	167
+54	1	21	78	188	107.4	70	3	3.9703	73	63
+63	2	25.5	109	226	103.2	46	5	5.9506	87	197
+41	2	24.2	90	199	123.6	57	4	4.5218	86	71
+28	1	25.4	93	141	79	49	3	4.1744	91	168
+19	1	23.2	75	143	70.4	52	3	4.6347	72	140
+61	2	26.1	126	215	129.8	57	4	4.9488	96	217
+48	1	32.7	93	276	198.6	43	6.42	5.1475	91	121
+54	2	27.3	100	200	144	33	6	4.7449	76	235
+53	2	26.6	93	185	122.4	36	5	4.8903	82	245
+48	1	22.8	101	110	41.6	56	2	4.1271	97	40
+53	1	28.8	111.67	145	87.2	46	3.15	4.0775	85	52
+29	2	18.1	73	158	99	41	4	4.4998	78	104
+62	1	32	88	172	69	38	4	5.7838	100	132
+50	2	23.7	92	166	97	52	3	4.4427	93	88
+58	2	23.6	96	257	171	59	4	4.9053	82	69
+55	2	24.6	109	143	76.4	51	3	4.3567	88	219
+54	1	22.6	90	183	104.2	64	3	4.3041	92	72
+36	1	27.8	73	153	104.4	42	4	3.4965	73	201
+63	2	24.1	111	184	112.2	44	4	4.9345	82	110
+47	2	26.5	70	181	104.8	63	3	4.1897	70	51
+51	2	32.8	112	202	100.6	37	5	5.7746	109	277
+42	1	19.9	76	146	83.2	55	3	3.6636	79	63
+37	2	23.6	94	205	138.8	53	4	4.1897	107	118
+28	1	22.1	82	168	100.6	54	3	4.2047	86	69
+58	1	28.1	111	198	80.6	31	6	6.0684	93	273
+32	1	26.5	86	184	101.6	53	4	4.9904	78	258
+25	2	23.5	88	143	80.8	55	3	3.5835	83	43
+63	1	26	85.67	155	78.2	46	3.37	5.037	97	198
+52	1	27.8	85	219	136	49	4	5.1358	75	242
+65	2	28.5	109	201	123	46	4	5.0752	96	232
+42	1	30.6	121	176	92.8	69	3	4.2627	89	175
+53	1	22.2	78	164	81	70	2	4.1744	101	93
+79	2	23.3	88	186	128.4	33	6	4.8122	102	168
+43	1	35.4	93	185	100.2	44	4	5.3181	101	275
+44	1	31.4	115	165	97.6	52	3	4.3438	89	293
+62	2	37.8	119	113	51	31	4	5.0434	84	281
+33	1	18.9	70	162	91.8	59	3	4.0254	58	72
+56	1	35	79.33	195	140.8	42	4.64	4.1109	96	140
+66	1	21.7	126	212	127.8	45	4.71	5.2781	101	189
+34	2	25.3	111	230	162	39	6	4.9767	90	181
+46	2	23.8	97	224	139.2	42	5	5.366	81	209
+50	1	31.8	82	136	69.2	55	2	4.0775	85	136
+69	1	34.3	113	200	123.8	54	4	4.7095	112	261
+34	1	26.3	87	197	120	63	3	4.2485	96	113
+71	2	27	93.33	269	190.2	41	6.56	5.2417	93	131
+47	1	27.2	80	208	145.6	38	6	4.804	92	174
+41	1	33.8	123.33	187	127	45	4.16	4.3175	100	257
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+55	1	23	94.67	190	137.6	38	5	4.2767	106	146
+59	2	27.9	101	218	144.2	38	6	5.1874	95	212
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+49	2	27.4	89	177	113	37	5	4.9053	97	111
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+57	2	23.2	107.33	231	159.4	41	5.63	5.0304	112	120
+39	2	26.9	93	136	75.4	48	3	4.1431	99	67
+62	2	34.6	120	215	129.2	43	5	5.366	123	310
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+46	1	21.1	80	205	144.4	42	5	4.5326	87	183
+68	2	23.5	101	162	85.4	59	3	4.4773	91	66
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+41	1	20.8	86	223	128.2	83	3	4.0775	89	72
+53	1	26.5	97	193	122.4	58	3	4.1431	99	49
+45	1	24.2	83	177	118.4	45	4	4.2195	82	64
+33	1	19.5	80	171	85.4	75	2	3.9703	80	48
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+47	2	24.9	75	225	166	42	5	4.4427	102	104
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+36	1	19.6	71	250	133.2	97	3	4.5951	92	57
diff --git a/1-Introduction/04-stats-and-probability/assignment.ipynb b/1-Introduction/04-stats-and-probability/assignment.ipynb
index a6f81476a..a35abee12 100644
--- a/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ b/1-Introduction/04-stats-and-probability/assignment.ipynb
@@ -2,36 +2,45 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Introduction to Probability and Statistics\r\n",
     "## Assignment\r\n",
     "\r\n",
     "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
     "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
-    "df.head()"
-   ],
+    "In this dataset, columns as the following:\r\n",
+    "* Age and sex are self-explanatory\r\n",
+    "* BMI is body mass index\r\n",
+    "* BP is average blood pressure\r\n",
+    "* S1 through S6 are different blood measurements\r\n",
+    "* Y is the qualitative measure of disease progression over one year\r\n",
+    "\r\n",
+    "Let's study this dataset using methods of probability and statistics.\r\n",
+    "\r\n",
+    "### Task 1: Compute mean values and variance for all values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
-       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
-       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
-       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
-       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
-       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -51,202 +60,398 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>Variance</th>\n",
+       "      <th>Std Dev</th>\n",
+       "      <th>RSD (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
        "      <th>AGE</th>\n",
+       "      <td>48.518100</td>\n",
+       "      <td>171.846610</td>\n",
+       "      <td>13.109028</td>\n",
+       "      <td>27.018840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>SEX</th>\n",
+       "      <td>1.468326</td>\n",
+       "      <td>0.249561</td>\n",
+       "      <td>0.499561</td>\n",
+       "      <td>34.022502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>BMI</th>\n",
+       "      <td>26.375792</td>\n",
+       "      <td>19.519798</td>\n",
+       "      <td>4.418122</td>\n",
+       "      <td>16.750669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>BP</th>\n",
+       "      <td>94.647014</td>\n",
+       "      <td>191.304401</td>\n",
+       "      <td>13.831283</td>\n",
+       "      <td>14.613544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>S1</th>\n",
+       "      <td>189.140271</td>\n",
+       "      <td>1197.717241</td>\n",
+       "      <td>34.608052</td>\n",
+       "      <td>18.297558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
+       "      <td>115.439140</td>\n",
+       "      <td>924.955494</td>\n",
+       "      <td>30.413081</td>\n",
+       "      <td>26.345554</td>\n",
        "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>59</td>\n",
-       "      <td>2</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>93.2</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.8598</td>\n",
-       "      <td>87</td>\n",
-       "      <td>151</td>\n",
+       "      <th>S3</th>\n",
+       "      <td>49.788462</td>\n",
+       "      <td>167.293585</td>\n",
+       "      <td>12.934202</td>\n",
+       "      <td>25.978313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>183</td>\n",
-       "      <td>103.2</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.8918</td>\n",
-       "      <td>69</td>\n",
-       "      <td>75</td>\n",
+       "      <th>S4</th>\n",
+       "      <td>4.070249</td>\n",
+       "      <td>1.665261</td>\n",
+       "      <td>1.290450</td>\n",
+       "      <td>31.704447</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>72</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>156</td>\n",
-       "      <td>93.6</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.6728</td>\n",
-       "      <td>85</td>\n",
-       "      <td>141</td>\n",
+       "      <th>S5</th>\n",
+       "      <td>4.641411</td>\n",
+       "      <td>0.272892</td>\n",
+       "      <td>0.522391</td>\n",
+       "      <td>11.254995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25.3</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>198</td>\n",
-       "      <td>131.4</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.8903</td>\n",
-       "      <td>89</td>\n",
-       "      <td>206</td>\n",
+       "      <th>S6</th>\n",
+       "      <td>91.260181</td>\n",
+       "      <td>132.165712</td>\n",
+       "      <td>11.496335</td>\n",
+       "      <td>12.597317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>192</td>\n",
-       "      <td>125.4</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.2905</td>\n",
-       "      <td>80</td>\n",
-       "      <td>135</td>\n",
+       "      <th>Y</th>\n",
+       "      <td>152.133484</td>\n",
+       "      <td>5943.331348</td>\n",
+       "      <td>77.093005</td>\n",
+       "      <td>50.674580</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "           Mean     Variance    Std Dev    RSD (%)\n",
+       "AGE   48.518100   171.846610  13.109028  27.018840\n",
+       "SEX    1.468326     0.249561   0.499561  34.022502\n",
+       "BMI   26.375792    19.519798   4.418122  16.750669\n",
+       "BP    94.647014   191.304401  13.831283  14.613544\n",
+       "S1   189.140271  1197.717241  34.608052  18.297558\n",
+       "S2   115.439140   924.955494  30.413081  26.345554\n",
+       "S3    49.788462   167.293585  12.934202  25.978313\n",
+       "S4     4.070249     1.665261   1.290450  31.704447\n",
+       "S5     4.641411     0.272892   0.522391  11.254995\n",
+       "S6    91.260181   132.165712  11.496335  12.597317\n",
+       "Y    152.133484  5943.331348  77.093005  50.674580"
       ]
      },
+     "execution_count": 19,
      "metadata": {},
-     "execution_count": 13
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
    "source": [
-    "\r\n",
-    "In this dataset, columns as the following:\r\n",
-    "* Age and sex are self-explanatory\r\n",
-    "* BMI is body mass index\r\n",
-    "* BP is average blood pressure\r\n",
-    "* S1 through S6 are different blood measurements\r\n",
-    "* Y is the qualitative measure of disease progression over one year\r\n",
-    "\r\n",
-    "Let's study this dataset using methods of probability and statistics.\r\n",
-    "\r\n",
-    "### Task 1: Compute mean values and variance for all values"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"S:\\\\ProgrammierenResourcen\\\\Data-Science-For-Beginners-main\\\\1-Introduction\\\\04-stats-and-probability\\\\DiabetesData\\\\diabetes.tab.txt\",sep='\\t')\n",
+    "\n",
+    "# Calculate mean and standard deviation\n",
+    "mean_values = df.mean(numeric_only=True)\n",
+    "std_values = df.std(numeric_only=True)\n",
+    "variance_values = df.var(numeric_only=True)\n",
+    "\n",
+    "# Calculate RSD (Coefficient of Variation)\n",
+    "rsd_values = (std_values / mean_values) * 100\n",
+    "\n",
+    "# Combine into a tidy DataFrame\n",
+    "rsd_df = pd.DataFrame({\n",
+    "    'Mean': mean_values,\n",
+    "    'Variance': variance_values,\n",
+    "    'Std Dev': std_values,\n",
+    "    'RSD (%)': rsd_values\n",
+    "})\n",
+    "\n",
+    "rsd_df\n",
+    "\n"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"S:\\\\ProgrammierenResourcen\\\\Data-Science-For-Beginners-main\\\\1-Introduction\\\\04-stats-and-probability\\\\DiabetesData\\\\diabetes.tab.txt\",sep='\\t')\n",
+    "\n",
+    "# Set the visual style\n",
+    "sns.set(style=\"whitegrid\")\n",
+    "\n",
+    "# Create a figure with subplots\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
+    "\n",
+    "# Boxplot for BMI vs SEX\n",
+    "sns.boxplot(data=df, x='SEX', y='BMI', ax=axes[0])\n",
+    "axes[0].set_title('BMI by SEX')\n",
+    "\n",
+    "# Boxplot for BP vs SEX\n",
+    "sns.boxplot(data=df, x='SEX', y='BP', ax=axes[1])\n",
+    "axes[1].set_title('Blood Pressure (BP) by SEX')\n",
+    "\n",
+    "# Boxplot for Y vs SEX\n",
+    "sns.boxplot(data=df, x='SEX', y='Y', ax=axes[2])\n",
+    "axes[2].set_title('Disease Progression (Y) by SEX')\n",
+    "\n",
+    "# Improve layout\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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AABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAGa1vcA5WbKlCmprq5Os2bN6nsUAAAA6tmyZctSKpWyyy67fOKxXsH+kOrq6lRXV9f3GB+ruro6lZWVZT8njYc1STmxHik31iTlxHqk3DSENbk6jegV7A9Z8cr1TjvtVM+TfLRFixZl6tSp2X777dOqVav6HgesScqK9Ui5sSYpJ9Yj5aYhrMlnn322zsd6BRsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAK7gWrWrFlKpVJ9jwEAAMD/17S+B2D1lUqldOv2hTRtukF9j/KJqqqq06SJJwIAAIBPP4HdQDVtukFG3jw5r815t75H+UjbbNEmpw7ctb7HAAAAWC8EdgP22px389Lr79T3GAAAAMRnsAEAAKAQAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACoF6VSKc2aNavvMQojsAEAAD6lqqqq63uEj1VRUZFu3b6QUqlU36MUoml9DwAAAMC60aRJKSNvnpzX5rxb36Os0jZbtMmpA3fNsmX1PUkxBDYAAMCn2Gtz3s1Lr79T32M0Ct4iDgAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABaj3wH777bdz9tlnp3///unZs2cOOeSQTJo0qWb/k08+mW9+85vp3r179t9///zP//xPrfOXLl2ac889N3369Mkuu+ySU045Jf/617/W98MAAACgkav3wD755JMzZcqUXHLJJbnjjjvy+c9/PkcddVRefvnlvPTSSxk8eHD69euXcePG5dvf/naGDh2aJ598sub8c845J48//nguv/zyjB49Oi+//HKGDBlSj48IAACAxqhefw72rFmz8sQTT2Ts2LHZddddkyRnnXVW/vznP+eee+7JP//5z3Tp0iUnnXRSkqRz58557rnn8utf/zp9+vTJnDlzcuedd+bqq6/ObrvtliS55JJLsv/++2fKlCnZZZdd6u2xAQAA0LjU6yvYbdu2zahRo7LTTjvVbCuVSimVSlmwYEEmTZqUPn361Dqnd+/emTx5cqqrqzN58uSabSt06tQpW2yxRZ566qn18yAAAAAg9RzYG220Ufbaa680b968Ztv999+fWbNmpV+/fpk9e3Y6dOhQ65z27dtn8eLFmT9/fubMmZO2bdumRYsWKx0ze/bs9fIYAAAAIKnnt4h/2NNPP53TTz89++67b/bee+8sWbKkVnwnqfl9ZWVlFi9evNL+JGnRokWWLl26xnNUV1dn0aJFa3z+ulZZWZmKior6HqPOFi9enOrq6voeg3Vo8eLFtf4J9cl6pNxYk5QT67FxKZVKDaYbli5dWrbNUF1dnVKpVKdjyyawH3rooZx66qnp2bNnRo4cmeSDUK6srKx13IrfV1RUpGXLlivtTz74y1mbhbRs2bJMnTp1jc9f1yoqKrLJJpvU9xh1NmPGDN/EG4mZM2fW9whQw3qk3FiTlBPrsXGoqKhIt27d6nuMOnnjjTfKuhlW9cLuqpRFYI8ZMyYjRozI/vvvn5/97Gc1w2+55ZaZO3durWPnzp2bVq1apU2bNunQoUPefvvtVFZW1nrAc+fOzRZbbLHG8zRr1izbb7/9Gp+/rq3qSYVy1qlTp7J9NopiLF68ODNnzkzHjh0bzLOkfHpZj5Qba5JyYj02LnV91bUcbLXVVnWO2PXtxRdfrPOx9R7YY8eOzbBhwzJo0KD89Kc/rbUIdtttt0ycOLHW8ePHj0/Pnj3TpEmT7LrrrqmqqsrkyZNrLoY2Y8aMzJkzJ7169VrjmUqlUlq1arXG569rDekLJYlv3o1IRUVFWX/t0LhYj5Qba5JyYj1Sblq0aFG23bA6/VWvFzmbMWNGzj///Hz5y1/O4MGDM2/evLz11lt566238u6772bQoEF55plnMnLkyLz00ku5/vrrc9999+Xoo49OkmyxxRb5yle+kjPPPDMTJkzIM888k5NPPjm77757evToUZ8PDQAAgEamXl/Bvv/++7Ns2bI8+OCDefDBB2vtO+igg3LhhRfmyiuvzMUXX5zRo0dnm222ycUXX1zrR3cNGzYs559/fk444YQkSf/+/XPmmWeu18cBAAAA9RrYxx13XI477riPPaZ///7p37//R+5v1apVhg8fnuHDhxc9HgAAANRZvb5FHAAAAD4tBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEAByiqwr7nmmgwaNKjWtjPPPDNdunSp9WvAgAE1+6uqqvLLX/4y/fr1S48ePXLMMcfk1VdfXd+jAwAA0MiVTWDffPPNueyyy1ba/vzzz+e4447L448/XvPr9ttvr9l/5ZVXZuzYsRk2bFh++9vfpqqqKkcffXQqKyvX4/QAAAA0dvUe2HPmzMlxxx2XkSNHpmPHjrX2VVdX58UXX8yOO+6YzTffvOZXu3btkiSVlZW5/vrrM2TIkOy9997p2rVrLr300syePTsPPPBAPTwaAAAAGqt6D+x//OMfadasWe6+++5079691r5XXnklixYtynbbbbfKc6dNm5b33nsvffr0qdm20UYbpVu3bnnqqafW6dwAAADw75rW9wADBgyo9Znqf/fCCy8kSW666aY89thjadKkSfr375+TTjopbdq0yezZs5MkW265Za3z2rdvX7MPAAAA1od6D+yP88ILL6RJkyZp3759rr766rzyyiu56KKLMn369IwePTqLFy9OkjRv3rzWeS1atMg777yzxvdbXV2dRYsWrdXs61JlZWUqKirqe4w6W7x4caqrq+t7DNahFV+LK/4J9cl6pNxYk5QT67FxKZVKDaYbli5dWrbNUF1dnVKpVKdjyzqwjz/++Bx66KFp27ZtkmSHHXbI5ptvnu985zt59tln07JlyyQfBOeKf08++MtZm4W0bNmyTJ06de2GX4cqKiqyySab1PcYdTZjxgzfxBuJmTNn1vcIUMN6pNxYk5QT67FxqKioSLdu3ep7jDp54403yroZPvyi7kcp68Bu0qRJTVyv8LnPfS5JMnv27Jq3hs+dOzef/exna46ZO3duunTpssb326xZs2y//fZrfP661tCukN6pU6eyfTaKYixevDgzZ85Mx44dG8yzpHx6WY+UG2uScmI9Ni51fdW1HGy11VZ1jtj17cUXX6zzsWUd2EOHDs3cuXPzm9/8pmbbs88+myTZfvvt85nPfCYbbrhhJkyYUBPYCxYsyHPPPZfDDjtsje+3VCqlVatWazX7utSQvlCS+ObdiFRUVJT11w6Ni/VIubEmKSfWI+WmRYsWZdsNq9Nf9X4V8Y+z33775cknn8wVV1yRV155JY8++mjOOOOMfPWrX03nzp3TvHnzHHbYYRk5cmQefvjhTJs2LSeddFI6dOiQfffdt77HBwAAoBEp61ewv/SlL+Wyyy7LqFGjcu2116ZNmzb52te+lhNPPLHmmCFDhmT58uU588wzs2TJkvTq1SvXXXddmjVrVn+DAwAA0OiUVWBfeOGFK2074IADcsABB3zkORtssEFOO+20nHbaaetyNAAAAPhYZf0WcQAAAGgoBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEAB1klgz549e13cLAAAAJStNQrsz3/+83nmmWdWuW/SpEk54IAD1mooAAAAaGia1vXA66+/PosWLUqSVFdX57bbbstjjz220nFTpkxJ8+bNi5sQAAAAGoA6B/bSpUtzxRVXJElKpVJuu+22lY5p0qRJ2rRpk+OPP764CQEAAKABqHNgH3/88TXh3LVr19x6663Zeeed19lgAAAA0JDUObD/3bRp04qeAwAAABq0NQrsJHniiSfyxz/+MYsXL05VVVWtfaVSKeeff/5aDwcAAAANxRoF9vXXX5+LLrooLVq0SLt27VIqlWrt//DvAQAA4NNujQJ7zJgx+drXvpYRI0a4YjgAAABkDX8O9rx58/Ktb31LXAMAAMD/t0aB3a1bt0yfPr3oWQAAAKDBWqO3iJ9xxhk58cQT06pVq3Tv3j0VFRUrHbPVVlut9XAAAADQUKxRYB9yyCGpqqrKGWec8ZEXNJs6depaDQYAAAANyRoF9rBhw1wpHAAAAP7NGgX2N7/5zaLnAAAAgAZtjQL7qaee+sRjevXqtSY3DQAAAA3SGgX2oEGDUiqVUl1dXbPtw28Z9xlsAAAAGpM1Cuwbb7xxpW2LFi3KpEmTctddd+Xyyy9f68EAAACgIVmjwN59991XuX3vvfdOq1atctVVV+Waa65Zq8EAAACgIWlS9A3utttumThxYtE3CwAAAGWt8MB+5JFH0rp166JvFgAAAMraGr1F/PDDD19pW1VVVWbPnp3XX389xxxzzFoPBgAAAA3JGgX2v189fIUmTZpkhx12yODBg3PwwQev9WAAAADQkKxRYN90001FzwEAAAAN2hoF9gqPPfZYJk6cmAULFqRdu3bZdddd069fv6JmAwAAgAZjjQK7srIyP/jBD/L4449ngw02SNu2bTN//vxcc8016d27d6655po0b9686FkBAACgbK3RVcQvv/zyTJ48ORdddFGeeeaZPP744/nb3/6WCy64IH/9619z1VVXFT0nAAAAlLU1Cux77703J5xwQr7+9a9ngw02SJI0bdo0Bx54YE444YTcc889hQ4JAAAA5W6NAvtf//pXunXrtsp93bp1y5w5c9ZqKAAAAGho1iiwP/vZz2by5Mmr3PfUU09lyy23XKuhAAAAoKFZo4ucffe7382FF16Yli1b5itf+Uo222yzzJs3L/fee2+uvfbanHDCCUXPCQAAAGVtjQL7kEMOyXPPPZeRI0fm5z//ec326urqHHTQQTn22GMLGxAAAAAagjX+MV0jRozIkUcemYkTJ+add95JqVTKPvvsk86dOxc9IwAAAJS91foM9vPPP5+DDz44N9xwQ5Kkc+fOOeSQQ3LooYfmF7/4RU4++eTMmDFjnQwKAAAA5azOgf3aa6/l8MMPz7x589KpU6da+5o1a5ahQ4fm7bffzqGHHuoq4gAAADQ6dQ7sUaNGZZNNNsnvf//77L///rX2VVRU5Pvf/35uv/32tGjRItdcc03hgwIAAEA5q3NgP/nkkzn66KPTrl27jzxm8803z5FHHpknnniikOEAAACgoahzYM+dOzcdO3b8xON22GGHzJ49e21mAgAAgAanzoHdrl27zJ079xOPmz9/fjbeeOO1GgoAAAAamjoHdq9evTJu3LhPPO7OO+9Mt27d1mooAAAAaGjqHNiDBg3KhAkTcuGFF2bp0qUr7a+srMxFF12Uxx57LAMHDix0SAAAACh3Tet64E477ZTTTz89559/fu6666706dMn22yzTd5///288cYbmTBhQubPn58f/ehH6dev37qcGQAAAMpOnQM7SQYOHJiuXbvmuuuuy8MPP1zzSnbr1q3Tt2/fHHnkkenevfs6GRQAAADK2WoFdpLsuuuu2XXXXZMk//rXv9K0adNstNFGhQ8GAAAADclqB/a/+7ifiQ0AAACNSZ0vcgYAAAB8NIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUoq8C+5pprMmjQoFrbpk6dmsMOOyw9evTIgAEDcuONN9baX1VVlV/+8pfp169fevTokWOOOSavvvrq+hwbAAAAyiewb7755lx22WW1ts2fPz9HHHFEPvvZz+aOO+7ID3/4w4wcOTJ33HFHzTFXXnllxo4dm2HDhuW3v/1tqqqqcvTRR6eysnI9PwIAAAAas6b1PcCcOXPy3//935kwYUI6duxYa9+tt96aZs2a5bzzzkvTpk3TuXPnzJo1K6NGjcrBBx+cysrKXH/99Tn11FOz9957J0kuvfTS9OvXLw888EC++tWvrv8HBAAAQKNU769g/+Mf/0izZs1y9913p3v37rX2TZo0KbvvvnuaNv2/5wF69+6dmTNnZt68eZk2bVree++99OnTp2b/RhttlG7duuWpp55ab48BAAAA6v0V7AEDBmTAgAGr3Dd79uzssMMOtba1b98+SfLmm29m9uzZSZItt9xypWNW7FsT1dXVWbRo0Rqfv65VVlamoqKivseos8WLF6e6urq+x2AdWrx4ca1/Qn2yHik31iTlxHpsXEqlUoPphqVLl5ZtM1RXV6dUKtXp2HoP7I+zZMmSNG/evNa2Fi1aJPngL2DFN4ZVHfPOO++s8f0uW7YsU6dOXePz17WKiopssskm9T1Gnc2YMcM38UZi5syZ9T0C1LAeKTfWJOXEemwcKioq0q1bt/oeo07eeOONsm6GDzfnRynrwG7ZsuVKFytbunRpkqRVq1Zp2bJlkg9e0V3x7yuOWZtnapo1a5btt99+jc9f1xraBdw6depUts9GUYzFixdn5syZ6dixY4N5lpRPL+uRcmNNUk6sx8alrq+6loOtttqqzhG7vr344ot1PrasA7tDhw6ZO3durW0rfr/FFltk+fLlNds++9nP1jqmS5cua3y/pVIprVq1WuPz17WG9IWSxDfvRqSioqKsv3ZoXKxHyo01STmxHik3LVq0KNtuWJ3+qveLnH2cXr16ZfLkyXn//fdrto0fPz6dOnXKpptumq5du2bDDTfMhAkTavYvWLAgzz33XHr16lUfIwMAANBIlXVgH3zwwVm4cGF++tOf5sUXX8y4cePym9/8JoMHD07ywfvgDzvssIwcOTIPP/xwpk2blpNOOikdOnTIvvvuW8/TAwAA0JiU9VvEN9100/z617/OiBEjctBBB2XzzTfP0KFDc9BBB9UcM2TIkCxfvjxnnnlmlixZkl69euW6665Ls2bN6nFyAAAAGpuyCuwLL7xwpW0777xzfve7333kORtssEFOO+20nHbaaetyNAAAAPhYZf0WcQAAAGgoBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABGkRgz5kzJ126dFnp17hx45IkU6dOzWGHHZYePXpkwIABufHGG+t5YgAAABqbpvU9QF1MmzYtLVq0yEMPPZRSqVSzvU2bNpk/f36OOOKIDBgwIOeee27++te/5txzz03r1q1z8MEH1+PUAAAANCYNIrBfeOGFdOzYMe3bt19p3+jRo9OsWbOcd955adq0aTp37pxZs2Zl1KhRAhsAAID1pkG8Rfz5559P586dV7lv0qRJ2X333dO06f89V9C7d+/MnDkz8+bNW18jAgAA0Mg1mFew27Ztm4EDB2bGjBnZdtttc/zxx6d///6ZPXt2dthhh1rHr3il+80338xmm2222vdXXV2dRYsWFTL7ulBZWZmKior6HqPOFi9enOrq6voeg3Vo8eLFtf4J9cl6pNxYk5QT67FxKZVKDaYbli5dWrbNUF1dXeujyh+n7AN7+fLlefnll7P99tvnJz/5STbccMP8z//8T4499tjccMMNWbJkSZo3b17rnBYtWiT54C9pTSxbtixTp05d69nXlYqKimyyySb1PUadzZgxwzfxRmLmzJn1PQLUsB4pN9Yk5cR6bBwqKirSrVu3+h6jTt54442yboYPN+dHKfvAbtq0aSZMmJANNtggLVu2TJLsuOOOmT59eq677rq0bNkylZWVtc5ZEdatWrVao/ts1qxZtt9++7UbfB368OMtd506dSrbZ6MoxuLFizNz5sx07NixwTxLyqeX9Ui5sSYpJ9Zj41LXV13LwVZbbVXniF3fXnzxxTofW/aBnSStW7deadvnPve5PP744+nQoUPmzp1ba9+K32+xxRZrdH+lUmmN43x9aEhfKEl8825EKioqyvprh8bFeqTcWJOUE+uRctOiRYuy7YbV6a+yv8jZ9OnT07Nnz0yYMKHW9r///e/Zfvvt06tXr0yePDnvv/9+zb7x48enU6dO2XTTTdf3uAAAADRSZR/YnTt3znbbbZfzzjsvkyZNyksvvZQLLrggf/3rX3P88cfn4IMPzsKFC/PTn/40L774YsaNG5ff/OY3GTx4cH2PDgAAQCNS9m8Rb9KkSa6++ur8/Oc/z4knnpgFCxakW7duueGGG2quHv7rX/86I0aMyEEHHZTNN988Q4cOzUEHHVTPkwMAANCYlH1gJ8lmm22WCy644CP377zzzvnd7363HicCAACA2sr+LeIAAADQEAhsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAnwqAruqqiq//OUv069fv/To0SPHHHNMXn311foeCwAAgEbkUxHYV155ZcaOHZthw4blt7/9baqqqnL00UensrKyvkcDAACgkWjwgV1ZWZnrr78+Q4YMyd57752uXbvm0ksvzezZs/PAAw/U93gAAAA0Eg0+sKdNm5b33nsvffr0qdm20UYbpVu3bnnqqafqcTIAAAAak6b1PcDamj17dpJkyy23rLW9ffv2NftWx7Jly1JdXZ1nnnmmkPnWherq6jRp0iTf6tMmy99vXd/jfKSmGzTJs88+m+rq6voehXWsuro6pVIp06dPT6lUqu9xaOSsR8qNNUk5sR4bn1KpVNbdsKIZqqqqynZNLlu2rM6zNfjAXrx4cZKkefPmtba3aNEi77zzzmrf3oo/uHL9y03+b7aNN2z+CUeWh3L+s6QYpVIpTZo0+DfE8ClhPVJurEnKifXYODWEbijndVkqlRpPYLds2TLJB5/FXvHvSbJ06dJUVFSs9u3tsssuhc0GAABA41G+TxPU0Yq3hs+dO7fW9rlz52aLLbaoj5EAAABohBp8YHft2jUbbrhhJkyYULNtwYIFee6559KrV696nAwAAIDGpMG/Rbx58+Y57LDDMnLkyLRr1y5bb711Lr744nTo0CH77rtvfY8HAABAI9HgAztJhgwZkuXLl+fMM8/MkiVL0qtXr1x33XVp1qxZfY8GAABAI1Gq9jOUAAAAYK01+M9gAwAAQDkQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBXeauueaaDBo06GOPmT9/fk455ZT06tUru+++e84999wsXrx4PU1IY1OXNTl9+vQce+yx2WOPPdKnT58MGTIkb7zxxnqakMakLuvx3919993p0qVLXnvttXU4FY1ZXdbksmXL8vOf/zz9+vVLjx49cthhh2Xq1KnraUIak7qsx3/+85855ZRT0rt37+yxxx456aSTMmfOnPU0IY3B22+/nbPPPjv9+/dPz549c8ghh2TSpEkfefxrr72WwYMHp2fPnunbt28uu+yyvP/+++tx4rUjsMvYzTffnMsuu+wTjxsyZEhmzZqV3/zmN/nFL36RRx99NOecc846n4/Gpy5rcv78+TniiCPSsmXL3HTTTbn22mvzr3/9K0cffXSWLl26fgalUajr98gVXn/99Zx33nnrbiAavbquyXPOOSfjxo3L+eefnzvuuCPt2rXLMccck3fffXfdD0mjUdf1eOKJJ+aNN97IDTfckBtuuCFvvPFGfvjDH677AWk0Tj755EyZMiWXXHJJ7rjjjnz+85/PUUcdlZdffnmlY5ctW5ajjjoqSfLb3/4255xzTm655Zb86le/Wt9jrzGBXYbmzJmT4447LiNHjkzHjh0/9tgpU6Zk4sSJ+dnPfpYvfOEL6dOnT84777zcddddnn2kMKuzJh966KEsWrQoF110UXbYYYfsuOOOufjii/PSSy/l6aefXj8D86m2Outxhaqqqpx22mn5whe+sG6Ho1FanTX56quv5o477siIESPSr1+/dO7cOcOHD0/z5s3z97//ff0MzKfa6qzHBQsWZOLEiTnmmGPy+c9/Pt26dcuxxx6bZ599Nm+//fZ6mZdPt1mzZuWJJ57IOeeck9122y2dOnXKWWedlfbt2+eee+5Z6fj7778/b7zxRs3/R+6zzz45+eSTM3r06FRWVtbDI1h9ArsM/eMf/0izZs1y9913p3v37h977KRJk7L55punc+fONdt23333lEqlTJ48eV2PSiOxOmuyT58+ufLKK9OyZcuabU2afPCtZsGCBet0ThqH1VmPK1x99dVZtmxZBg8evI6nozFanTX5xBNPpE2bNunfv3/Nto022iiPPPJI+vTps65HpRFYnfXYsmXLtG7dOnfeeWcWLlyYhQsX5q677kqnTp2y0UYbraeJ+TRr27ZtRo0alZ122qlmW6lUSqlUWuX/F06aNClf+MIXsvHGG9ds6927dxYuXNhgPkrTtL4HYGUDBgzIgAED6nTsnDlzsuWWW9ba1rx582yyySZ5880318V4NEKrsya32WabbLPNNrW2jRo1Ki1btkyvXr3WxXg0MquzHpPkmWeeyfXXX5/bb7/dO3tYJ1ZnTc6YMSOf+cxn8sADD2TUqFGZM2dOunXrlp/85Ce1niyHNbU667F58+a58MILc/bZZ2e33XZLqVRK+/btM2bMmJonx2FtbLTRRtlrr71qbbv//vsza9asnHHGGSsdP3v27HTo0KHWtvbt2ydJ3nzzzTo/sV6ffOU0cIsXL07z5s1X2t6iRQufd6Us3HTTTRkzZkxOPfXUtGvXrr7HoZFZtGhRTj311Jx66ql1fjs5rEsLFy7MrFmzcuWVV+bkk0/OVVddlaZNm+bQQw/NP//5z/oej0amuro6U6dOzS677JKbb745o0ePzlZbbZUf/OAHWbhwYX2Px6fQ008/ndNPPz377rtv9t5775X2L1myZKW2adGiRZI0mLYR2A1cy5YtV/l5hKVLl6ZVq1b1MBF8oLq6OpdddlmGDx+e448/frWu9AxFGT58eDp16pTvfve79T0KJEmaNm2ahQsX5tJLL03fvn2z884759JLL02S/P73v6/n6Whs/vCHP2TMmDG5+OKLs+uuu2b33XfP1Vdfnddffz233357fY/Hp8xDDz2UI488Mj169MjIkSNXecyq2mZFWDeUtvEW8QauQ4cOeeihh2ptq6yszNtvv13zdgpY35YtW5bTTz899957b04//fR8//vfr++RaKTuuOOONG/ePLvsskuS1PyYj69+9as57rjjctxxx9XneDRCHTp0SNOmTWu9Hbxly5b5zGc+48fHsd5NmjQpnTp1yoYbblizbeONN06nTp0ya9asepyMT5sxY8ZkxIgR2X///fOzn/1sle/ATT74HvnCCy/U2jZ37twkyRZbbLHO5yyCV7AbuF69emX27Nm1vglOnDgxSbLrrrvW11g0ckOHDs19992Xn//85+KaevXAAw/k3nvvzZ133pk777wzw4cPT/LBdQG8qk196NWrV5YvX55nn322ZtuSJUvy6quvZtttt63HyWiMOnTokFmzZtV66+2iRYvy2muv+VgNhRk7dmyGDRuWgQMH5pJLLvnIuE4++B753HPP1fqIwvjx49O6det07dp1fYy71gR2A/P+++/nrbfeypIlS5Ik3bt3T8+ePXPSSSflmWeeyfjx43P22WfnwAMPbDDP8tCwfXhNjhs3Lv/7v/+bk046Kbvvvnveeuutml8rjoF15cPrcdttt631a8X3xa222iqbbLJJPU5KY/HhNbnbbrvli1/8Yn784x9n0qRJefHFFzN06NBssMEG+cY3vlHP0/Jp9+H1eOCBByb54GdhT5s2LdOmTcvJJ5+cFi1a5Jvf/GY9TsqnxYwZM3L++efny1/+cgYPHpx58+bV/H/hu+++m8rKyrz11ls1bwvfZ599svnmm9esyYceeiiXXHJJjjzyyI8N83IisBuYN998M3379s3//u//JvngMvdXXHFFttlmm3zve9/LiSeemP79++ecc86p30FpND68Ju+9994kyUUXXZS+ffvW+rXiGFhXPrweob6tak1efvnl2X333XPCCSfkW9/6VhYuXJgbb7zRhSBZ5z68Htu3b5+xY8emuro63/ve93LEEUekWbNmGTt2bNq0aVPP0/JpcP/992fZsmV58MEHV/r/whEjRmTKlCnp27dvpkyZkuSDC5r9+te/TlVVVb7zne/k3HPPzaGHHpof/OAH9fxI6q5UXV1dXd9DAAAAQEPnFWwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAGgEXnjhhZx00knZc889s+OOO6Zv37458cQTM23atJpjLr/88nTp0uVjfy1dujRVVVU59NBDs8suu2TWrFkr3dff//737LTTTrn44ovX50MEgHpXqq6urq7vIQCAdWf69On5zne+kx49euQ73/lONt1008yePTtjxozJtGnTcuONN6ZHjx65/PLLc8UVV+R3v/vdR95W9+7dUyqV8tprr+Ub3/hGOnfunLFjx6Zp06ZJkgULFuSggw7KZpttlptvvrlmOwA0Bv6rBwCfcjfccEPatm2ba6+9tlbw7rPPPtl///1z5ZVXZtSoUTXbe/To8Ym3uc022+Sss87Kj3/841x99dU54YQTkiRnnHFGFixYkNGjR4trABod/+UDgE+5efPmpbq6OlVVVbW2t2rVKmeccUYWL168Rrd74IEH5o9//GOuuuqq9O/fP88++2wefPDBXH755dlmm22KGB0AGhSBDQCfcnvvvXceffTRfPe7383BBx+c3r17Z7vttkupVMr++++/0vHLly9f5e00adIkTZrUvnzLeeedlylTpmTo0KGZPXt2Dj300Oy7777r5HEAQLnzGWwAaAR+8Ytf5LrrrsvSpUuTJG3btk3fvn1z+OGHZ+edd06Sms9gf5SBAwfm7LPPXmn7I488kuOPPz6bb755Hn744bRo0WLdPAgAKHNewQaARuBHP/pRvv/97+fPf/5znnzyyUyYMCH33HNP7r333pxxxhk5/PDDa469/fbbV3kbm2666Sq333fffSmVSnnrrbfyxz/+cZWvigNAY+AVbABopJ577rmcdtppeeWVV/LYY49lzJgxueKKK/L888/X+TbuvPPO/PjHP86ZZ56ZW2+9NXPmzMndd9+dDh06rMPJAaA8+TnYAPApNmfOnPTt2ze33XbbSvu6deuWk046KZWVlXn11VdX+7ZnzJiRc889N3vttVcGDRqUiy++OIsWLcqPf/zjeP4egMZIYAPAp9hmm22Wpk2bZuzYsTWfv/53L7/8clq0aJFtt912tW63srIyJ598cioqKnLBBRckSbp27Zof/ehHGT9+fK677rpC5geAhsRnsAHgU2yDDTbIOeeckx/+8Ic5+OCDM3DgwHTu3DmLFy/OE088kZtvvjk/+tGPsvHGG9ec89e//vUjb69Tp07ZeOONc9FFF+W5557LqFGjan02+6ijjsqjjz6ayy67LF/84hfTrVu3dfnwAKCs+Aw2ADQC//jHP3Lddddl8uTJ+de//pXmzZunW7duGTRoUM2P1fqkq4gnya9+9auUSqX84Ac/yGGHHZazzjprpWNee+21fOMb30j79u3z+9//Pi1btlwnjwkAyo3ABgAAgAL4DDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEAB/h+pYu/t4keUcwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"S:\\\\ProgrammierenResourcen\\\\Data-Science-For-Beginners-main\\\\1-Introduction\\\\04-stats-and-probability\\\\DiabetesData\\\\diabetes.tab.txt\",sep='\\t')\n",
+    "\n",
+    "df['AGE'].hist(bins=20, figsize=(10,6))\n",
+    "plt.suptitle('Age Histogram')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "df['SEX'].hist(bins=20, figsize=(10,6))\n",
+    "plt.suptitle('SEX Histogram')\n",
+    "plt.xlabel('SEX')\n",
+    "plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "df['BMI'].hist(bins=20, figsize=(10,6))\n",
+    "plt.suptitle('BMI Histogram')\n",
+    "plt.xlabel('BMI')\n",
+    "plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "df['Y'].hist(bins=20, figsize=(10,6))\n",
+    "plt.suptitle('Y Histogram')\n",
+    "plt.xlabel('Y')\n",
+    "plt.ylabel('Count')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
     "\r\n",
     "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
-   ],
-   "metadata": {}
+   ]
   },
   {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation of each variable with disease progression (Y):\n",
+      "Y      1.000000\n",
+      "BMI    0.586450\n",
+      "S5     0.565883\n",
+      "BP     0.441482\n",
+      "S4     0.430453\n",
+      "S6     0.382483\n",
+      "S1     0.212022\n",
+      "AGE    0.187889\n",
+      "S2     0.174054\n",
+      "SEX    0.043062\n",
+      "S3    -0.394789\n",
+      "Name: Y, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_csv(\"S:\\\\ProgrammierenResourcen\\\\Data-Science-For-Beginners-main\\\\1-Introduction\\\\04-stats-and-probability\\\\DiabetesData\\\\diabetes.tab.txt\",sep='\\t')\n",
+    "\n",
+    "corr_matrix = df.corr(numeric_only=True)\n",
+    "\n",
+    "y_corr = corr_matrix['Y'].sort_values(ascending=False)\n",
+    "print(\"Correlation of each variable with disease progression (Y):\")\n",
+    "print(y_corr)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
-   ],
-   "metadata": {}
+   ]
   },
   {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t-statistic: -0.902\n",
+      "p-value: 0.36744\n",
+      "Fail to reject the null hypothesis: No significant difference in diabetes progression between men and women.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "df = pd.read_csv(\"S:\\\\ProgrammierenResourcen\\\\Data-Science-For-Beginners-main\\\\1-Introduction\\\\04-stats-and-probability\\\\DiabetesData\\\\diabetes.tab.txt\",sep='\\t')\n",
+    "\n",
+    "# Split Y values by sexist parameter\n",
+    "y_male = df[df['SEX'] == 1]['Y']\n",
+    "y_female = df[df['SEX'] == 2]['Y']\n",
+    "\n",
+    "# Perform an independent two-sample t-test\n",
+    "t_stat, p_value = stats.ttest_ind(y_male, y_female, equal_var=False)\n",
+    "\n",
+    "print(f\"t-statistic: {t_stat:.3f}\")\n",
+    "print(f\"p-value: {p_value:.5f}\")\n",
+    "\n",
+    "# Interpret the result\n",
+    "alpha = 0.05\n",
+    "if p_value < alpha:\n",
+    "    print(\"Reject the null hypothesis: Significant difference in diabetes progression between men and women.\")\n",
+    "else:\n",
+    "    print(\"Fail to reject the null hypothesis: No significant difference in diabetes progression between men and women.\")\n"
+   ]
   }
  ],
  "metadata": {
-  "orig_nbformat": 4,
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python",
-   "version": "3.8.8",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.8 64-bit (conda)"
-  },
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
diff --git a/1-Introduction/04-stats-and-probability/notebook.ipynb b/1-Introduction/04-stats-and-probability/notebook.ipynb
index 208eee50c..8f1608a6e 100644
--- a/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ b/1-Introduction/04-stats-and-probability/notebook.ipynb
@@ -1114,7 +1114,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
diff --git a/translations/README.ne.md b/translations/README.ne.md
index 00387e611..094cffbb9 100644
--- a/translations/README.ne.md
+++ b/translations/README.ne.md
@@ -44,7 +44,7 @@ Microsoft मा Azure Cloud अधिवक्ताहरु एक १०-ह
 - असाइनमेन्ट
 - पोस्ट पाठ प्रश्नोत्तरी
 
-> ** क्विजहरु को बारे मा एक नोट **: सबै क्विज  [यो एप मा](https://purple-hill-04aebfb03.1.azurestaticapps.net/) निहित छन्, प्रत्येक तीन प्रश्नहरु को ४० कुल क्विज को लागी। तिनीहरू पाठ भित्र बाट जोडिएका छन् तर प्रश्नोत्तरी अनुप्रयोग स्थानीय स्तर मा चलाउन सकिन्छ; 'क्विज- app' फोल्डर मा निर्देशन पालना गर्नुहोस्। उनीहरु बिस्तारै स्थानीयकृत हुँदैछन्।
+> ** क्विजहरु को बारे मा एक नोट **: सबै क्विज  [यो एप मा](https://red-water-0103e7a0f.azurestaticapps.net/) निहित छन्, प्रत्येक तीन प्रश्नहरु को ४० कुल क्विज को लागी। तिनीहरू पाठ भित्र बाट जोडिएका छन् तर प्रश्नोत्तरी अनुप्रयोग स्थानीय स्तर मा चलाउन सकिन्छ; 'क्विज- app' फोल्डर मा निर्देशन पालना गर्नुहोस्। उनीहरु बिस्तारै स्थानीयकृत हुँदैछन्।
 
 
 ## पाठ