Question

Female Health Data			Looking at the correlation matrix identify one good and one bad regression pair and conduct detailed regression analysis with all explanations similar to example from Ch 18 repeated below
			Data>Data Analysis ToolBox>Correlation					Data>Data Analysis ToolBox> Regression
	AGE	HT	WT	WAIST	PULSE	SYS	DIAS	CHOL	BMI	LEG	ELBOW	WRIST	ARM
	17	64.3	114.8	67.2	76	104	61	264	19.6	41.6	6	4.6	23.6
	32	66.4	149.3	82.5	72	99	64	181	23.8	42.8	6.7	5.5	26.3
	25	62.3	107.8	66.7	88	102	65	267	19.6	39	5.7	4.6	26.3
	55	62.3	160.1	93	60	114	76	384	29.1	40.2	6.2	5	32.6
	27	59.6	127.1	82.6	72	94	58	98	25.2	36.2	5.5	4.8	29.2
	29	63.6	123.1	75.4	68	101	66	62	21.4	43.2	6	4.9	26.4
	25	59.8	111.7	73.6	80	108	61	126	22	38.7	5.7	5.1	27.9
	12	63.3	156.3	81.4	64	104	41	89	27.5	41	6.8	5.5	33
	41	67.9	218.8	99.4	68	123	72	531	33.5	43.8	7.8	5.8	38.6
	32	61.4	110.2	67.7	68	93	61	130	20.6	37.3	6.3	5	26.5
	31	66.7	188.3	100.7	80	89	56	175	29.9	42.3	6.6	5.2	34.4
	19	64.8	105.4	72.9	76	112	62	44	17.7	39.1	5.7	4.8	23.7
	19	63.1	136.1	85	68	107	48	8	24	40.3	6.6	5.1	28.4
	23	66.7	182.4	85.7	72	116	62	112	28.9	48.6	7.2	5.6	34
	40	66.8	238.4	126	96	181	102	462	37.7	33.2	7	5.4	35.2
	23	64.7	108.8	74.5	72	98	61	62	18.3	43.4	6.2	5.2	24.7
	27	65.1	119	74.5	68	100	53	98	19.8	41.5	6.3	5.3	27
	45	61.9	161.9	94	72	127	74	447	29.8	40	6.8	5	35
	41	64.3	174.1	92.8	64	107	67	125	29.7	38.2	6.8	4.7	33.1
	56	63.4	181.2	105.5	80	116	71	318	31.7	38.2	6.9	5.4	39.6
	22	60.7	124.3	75.5	64	97	64	325	23.8	38.2	5.9	5	27
	57	63.4	255.9	126.5	80	155	85	600	44.9	41	8	5.6	43.8
	24	62.6	106.7	70	76	106	59	237	19.2	38.1	6.1	5	23.6
	37	60.6	149.9	98	76	110	70	173	28.7	38	7	5.1	34.3
	59	63.5	163.1	104.7	76	105	69	309	28.5	36	6.7	5.1	34.4
	40	58.6	94.3	67.8	80	118	82	94	19.3	32.1	5.4	4.2	23.3
	45	60.2	159.7	99.3	104	133	83	280	31	31.1	6.4	5.2	35.6
	52	67.6	162.8	91.1	88	113	75	254	25.1	39.4	7.1	5.3	31.8
	31	63.4	130	74.5	60	113	66	123	22.8	40.2	5.9	5.1	27
	32	64.1	179.9	95.5	76	107	67	596	30.9	39.2	6.2	5	32.8
	23	62.7	147.8	79.5	72	95	59	301	26.5	39	6.3	4.9	31
	23	61.3	112.9	69.1	72	108	72	223	21.2	36.6	5.9	4.7	27
	47	58.2	195.6	105.5	88	114	79	293	40.6	27	7.5	5.5	41.2
	36	63.2	124.2	78.8	80	104	73	146	21.9	38.5	5.6	4.7	25.5
	34	60.5	135	85.7	60	125	73	149	26	39.9	6.4	5.2	30.9
	37	65	141.4	92.8	72	124	85	149	23.5	37.5	6.1	4.8	27.9
	18	61.8	123.9	72.7	88	92	46	920	22.8	39.7	5.8	5	26.5
	29	68	135.5	75.9	88	119	81	271	20.7	39	6.3	4.9	27.8
	48	67	130.4	68.6	124	93	64	207	20.5	41.6	6	5.3	23
	16	57	100.7	68.7	64	106	64	2	21.9	33.8	5.6	4.6	26.4

Answer 1

Using Excel

data -> data analysis -> correlation

we see the correlation between weight and BMI is 0.9392 , which is good regression

y = BMI and x =Weight

Using Excel

data -> data analysis -> regression

BMI^ = 2.8209 + 0.1562 WT

for bad regression

correlation between Height and BMI = -0.0137 , which is very low

y = BMI and x= Height(HT)

BMI^ = 27.9288 -0.0331 HT