To do this assignment, you must first create your own dataset, drawn from the file Fall 2020...

Question

To do this assignment, you must first create your own dataset, drawn from the file Fall 2020 Assignment 2.xls (available on Canvas at the end of the "Module 2"). The observations that will make up your dataset depend on the last digit of your student ID (mine ends in 7), as shown in this table:

Student ID's Last Digit Patients
1 1-200
2 201-400
3 401-600
4 601-800
5 801-1000
6 1001-1200
7 1201-1400
8 1401-1600
9 1601-1800
0 1801-2000
You will use the first 160 of your observations for Parts 1, 2 and 3 of this assignment. You will use the last 40 for Part 4.

These data result from a study of patient satisfaction at a network of dermatology practices. The variables are: “Patient” (once you've created your dataset you can ignore this one); “Sex”; “Satisfaction” (the patient's self-reported overall satisfaction (1 to 100); “Effectiveness” (the patient's self-reported view of the effectiveness of the procedure (1 to 80); and “Pain” (the patient's self-reported post-procedure pain (1 to 80).

Part 1 (25 points). What is a 90% confidence interval for the correlation between Satisfaction and Effectiveness?

Part 2 (25 points). Conduct a simple regression analysis with Satisfaction as your dependent variable and Pain as your independent variable. Discuss what you see/observe.

Part 3 (25 points). Conduct a multiple regression analysis with both Effectiveness and Pain as your predictors. Discuss what you see/observe. Having done this, evaluate (and discuss) whether the incorporation of Sex into your model would be useful.

Part 4 (25 points). Using your “hold-out” sample, cross-validate the model you fit in Part 3. Discuss what you see/observe.

fall-2020-assignment-2-yr4mgdd3.xlsx

Pooja · Accepted Answer

males
	Patient	Sex	Satisfaction	Effectiveness	Pain	female		1)
	1	Female	68	44	36	1		Step 1:
	2	Male	47	53	27	0		r=	0.3414
	3	Female	50	49	42	1
	4	Male	55	51	35	0		z=	 =0.5*LN((1+0.3414)/(1-0.3414))
	5	Female	53	40	36	1		z=	0.3557
	6	Male	50	41	37	0
	7	Female	34	45	42	1		Step 2:
	8	Male	51	51	32	0		z(a/2) = z(0.10/2) =	1.645
	9	Female	57	64	53	1		C_L =	 =0.3557-1.645*SQRT(1/(160-3)) =	0.2244
	10	Male	49	45	33	0		C_U =	 =0.3557+1.645*SQRT(1/(160-3)) =	0.4870
	11	Female	61	48	42	1
	12	Male	44	57	35	0		Step 3:
	13	Female	54	55	37	1		corr_L =	 =(EXP(2*0.2244)-1)/(EXP(2*0.2244)+1) =	0.2207
	14	Male	48	53	32	0		corr_U =	 =(EXP(2*0.487)-1)/(EXP(2*0.487)+1) =	0.4518
	15	Female	19	32	33	1
	16	Male	34	51	39	0
	17	Female	42	41	31	1		2)
	18	Male	56	55	37	0
	19	Female	36	50	34	1		SUMMARY OUTPUT
	20	Male	23	49	41	0
	21	Female	68	64	50	1		Regression Statistics
	22	Male	57	53	26	0		Multiple R	0.042031594
	23	Female	56	32	33	1		R Square	0.0017666549
	24	Male	59	62	42	0		Adjusted R Square	-0.0045512777
	25	Female	50	49	34	1		Standard Error	12.1149433307
	26	Male	46	35	35	0		Observations	160
	27	Female	35	61	49	1
	28	Male	43	45	34	0		ANOVA
	29	Female	69	35	30	1			df	SS	MS	F	Significance F
	30	Male	36	42	27	0		Regression	1	41.0411487226	41.0411487226	0.2796254744	0.5976888526
	31	Female	55	60	34	1		Residual	158	23189.9526012774	146.7718519068
	32	Male	28	31	28	0		Total	159	23230.99375
	33	Female	36	41	30	1
	34	Male	39	33	34	0			Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
	35	Female	47	43	46	1		Intercept	45.7854093926	4.2845865106	10.6860742055	2.1164058316492E-20	37.3229565406	54.2478622446	37.3229565406	54.2478622446
	36	Male	45	41	31	0		Pain	0.0629381007	0.1190214579	0.5287962504	0.5976888526	-0.1721402374	0.2980164387	-0.1721402374	0.2980164387
	37	Female	52	45	30	1
	38	Male	29	35	39	0
	39	Female	49	42	30	1
	40	Male	45	53	30	0		3)
	41	Female	59	57	24	1		SUMMARY OUTPUT
	42	Male	35	25	27	0
	43	Female	55	63	39	1		Regression Statistics
	44	Male	50	47	25	0		Multiple R	0.3476107897
	45	Female	42	52	31	1		R Square	0.1208332611
	46	Male	38	47	38	0		Adjusted R Square	0.1096336848
	47	Female	70	54	40	1		Standard Error	11.4056408596
	48	Male	47	36	32	0		Observations	160
	49	Female	58	65	46	1
	50	Male	51	42	24	0		ANOVA
	51	Female	62	61	54	1			df	SS	MS	F	Significance F
	52	Male	43	57	16	0		Regression	2	2807.0767332535	1403.5383666267	10.7890921893	0.0000406999
	53	Female	53	56	29	1		Residual	157	20423.9170167465	130.0886434188
	54	Male	51	43	41	0		Total	159	23230.99375
	55	Female	38	53	39	1
	56	Male	33	32	17	0			Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
	57	Female	84	43	34	1		Intercept	27.8747878886	5.599822225	4.9777987173	0.0000016757	16.8140797147	38.9354960625	16.8140797147	38.9354960625
	58	Male	50	56	41	0		Effectiveness	0.5017062435	0.1088028613	4.611149353	0.0000082535	0.2868000109	0.7166124761	0.2868000109	0.7166124761
	59	Female	62	59	33	1		Pain	-0.1024884898	0.1176559401	-0.8710864046	0.3850370069	-0.3348812276	0.129904248	-0.3348812276	0.129904248
	60	Male	49	53	28	0
	61	Female	63	48	31	1
	62	Male	41	45	43	0
	63	Female	34	33	52	1		4)
	64	Male	63	54	33	0
	65	Female	54	45	35	1		SUMMARY OUTPUT
	66	Male	38	39	27	0
	67	Female	45	49	49	1		Regression Statistics
	68	Male	47	41	35	0		Multiple R	0.5453146585
	69	Female	54	44	33	1		R Square	0.2973680768
	70	Male	45	43	33	0		Adjusted R Square	0.2388154165
	71	Female	46	48	40	1		Standard Error	12.4200234895
	72	Male	41	45	37	0		Observations	40
	73	Female	39	46	52	1
	74	Male	48	48	28	0		ANOVA
	75	Female	47	55	37	1			df	SS	MS	F	Significance F
	76	Male	34	58	30	0		Regression	3	2350.2485947108	783.4161982369	5.0786433169	0.0049161025
	77	Female	57	47	39	1		Residual	36	5553.2514052892	154.2569834803
	78	Male	47	28	40	0		Total	39	7903.5
	79	Female	15	48	36	1
	80	Male	42	36	31	0			Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
	81	Female	45	36	40	1		Intercept	46.1637468376	12.0049295471	3.8453992301	0.0004721624	21.8166212408	70.5108724343	21.8166212408	70.5108724343
	82	Male	43	60	34	0		X Variable 1	0.6211937295	0.1983948765	3.1310976401	0.0034488723	0.2188302707	1.0235571883	0.2188302707	1.0235571883
	83	Female	53	48	43	1		X Variable 2	-0.8637956325	0.2933990494	-2.9440982657	0.005642148	-1.4588364846	-0.2687547805	-1.4588364846	-0.2687547805
	84	Male	55	53	44	0		X Variable 3	3.9846875418	4.1011561713	0.9716010255	0.3377323202	-4.3328426863	12.3022177699	-4.3328426863	12.

To do this assignment, you must first create your own dataset, drawn from the file Fall 2020 Assignment 2.xls (available on Canvas at the end of the "Module 2"). The observations that will make up...

Answer To: To do this assignment, you must first create your own dataset, drawn from the file Fall 2020...

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment