Business & Finance Week 6 data analytical assignment

Please see assignment instructions for this assignment you will need to use a free software program named JASP to complete this assignment. 

When saving PDF doc please save as Question 2 or Question 4 only 

1056

170

039955

159

168

170

165

168

168

4

6

168

170

1

165

6

170

176

3.35

176

170

172

175
3.01

172

1243031

173

171

173

170

163

05

173

169

170

171

4.41

174

176

176

171

176

176
3.25
172

173
3.5
176

172

171

173

176

171
3.12
182

3.91

170

176

182

174
3.27
178
3.6
176

169

172

176

175

170

177

180
4.21

176
3.99

171

183
3.66
177
3.23
169

167

171

170
3.23
174
3.91
170
4.21
170

173
3.56
173

180

177

168

179
4
163

179

168

170

171

171
4.1
172

171

LDL_Chol	Hours_TV
						168	2.5950	4
													170	2.797427517
3.39
164	3. Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	159
3.092402595
3.7
		165	3.249408427
156	2.888637523
						172	2.897454286
2.684321261
2.766328258
2.649433141
2.811045366
166		3.6
	167		4.1
		163		3.35
161	2.647565999
3.79
2.795904577
										171	3.04567												176
	3.66
162	2.626149483
		175		3.01
			169		3.5
	4.41
3.03
		179
							173	3.090394765
	3.99
2.715664635
2.697116546
	3.25
3.83
			177	4.06
	3.27
3.54
3.84
3.012		182
4.04
2.686413374
3.43
3.71
			174
	178	3.26
3.61
4.11
3.250547806
3.53
4.55
3.55
		3.23
	3.12
4.27
3.31
3.098778487
		3.91
		180
	183	4.33
2.987071673
3.10493154
	3.56
2.866647124
3.73
2.577060443
3.222755354
3.13
3.68
		4.21
2.57324386
3.82
4.14
2.798365361
2.64554954
3.156208077
4.02
3.65
3.303111622
3.48
4.43
2.363621942
3.17
3.52
3.413805439
3.411823675

max

.

7

0

.7

2

.

4

3

7.

2

Male

.93

4

Female

.3

5

3.02

Male

.

6
29

2

Female

7
24

5

Male

8

.94

Female

.

9

Male

10

5

Female

11
30

Male

.22

12

.

Female

6

25

Female

14
36

.97

Female

15
23

.8

Male

.94

16
29

Female

17
37

Male

18
30

Male

36

Male

36

147
Male

26

.52

147
Female

22
31

Female

23
30

.5

1

Male
62.5

24
24

Female

25
26

.43

Male

26
23
68.52

Female

27
45

147
Male

28
38

Female

.87

29
30

Male

30
21

139
Male

31
39

.14

Female

26

111
Male

33
28

Male

34

145
Male

35
40

Male

36
25

Female

37
21

111
Female
38.7

38
22

147
Male

39
33

156
Male

40
28

133
Male

41
21

Male

42
37

Male

43
21

Male

44
22

Male

45
27

Male

24

Male

47
31

Male

48
28

134
Male

49
30

134
Female

50
31

129
Male

51
33

172
Female

52
23

127
Male

53
29

133
Female

54
25

Female

55
32
64

Female
35

56
29

Female

21

122
Male

58
34
50

Female
42

59
25

Male

60
31

Male

61
64

Female

62
26

Male

63
45

145
Female

64
21

131
Male

65
28

Male

66
45

Male

22

Female

68
20
55

Female
60

69
22

117
Female

70
33

119
Female

71
38

Male

72
30

141
Male

73
52

Male
32

74
22

Male

75
23
63
149
Female
50

caseno	age	weight	heart_rate	gender	VO	2
1	2	7		70	4	1	5																																												Male			55	9
	6	3				50		34		14																															Female			35
				36		8	65		16			42
					26	89.8	1					29						28
				24		10	1	43			40	56
77.			37		15					33
82.	48		17	43.48
		27		75	1		60								30			38
					25	97.	11	148	40.17
						22	78.42		12	36.01
88.02	155		44
				45		74	47	1					23		38.7
13	75.98					147	33.09
	58		1		39	44.81
		111			145						31
79.81	128	34.48
56.	18	1		63	47.23
86.13		156	45.06
19	87.3		127	55.12
	20	88.		52	45.58
						21			68	37.52
76.7		129	36.27
		62	140
60.9		1	72	37.09
	53	158	44.27
		133	40.34
72.37	55.19
82.16	144		49
90.92	177	38.06
95.	51	48.13
	73	108	42.42
		32	62.	59	48.23
91.38	151	42.96
	41	94.29	42.53
58.07	1	61	40.9
90.25	132	27.35
78.45
74.78	62.13
85.13	45.	69
101.25	40.73
58.94	1	54	41.82
101.06		131	35.6
97.75	136	42.2
72.5	123	45.3
88.45	175	40.02
46	72.29	187	47.17
94.59			134	44.43
115.42	38.12
51.96	50.05
89.35	48.			64
62.01	36.49
68.03	61.	71
79.39	33.84
81.91	138	51.2
178
59.78		122	47.71
57	71.03	52.3
183
80.98		119	55.	66
112.59	116	37.34
73.38		117	40.58
62.85		149	60.55
68.29	37.93
94.68	44.94
103.23	186	35.01
81.01	169	45.57
67	69.02	150	36.63
	141
73.44	40.52
60.31	47.92
85.94	153	45.84
64.65	60.92
88.83	109
82.41	146	49.4

Jeffreys’s Amazing Software Program ($0.00)

·
JASP v19.03 available for free download

o

Download JASP – JASP – Free and User-Friendly Statistical SoftwareLinks to an external site.

You have two simple problems. For Question 1, you will load a dataset into JASP and perform a correlation analysis and a simple regression to determine the relationship between time spent watching television and cholesterol numbers. Once you answer the questions about the analyses, you will export your results to pdf (Save each PDF as Question 2, Question 4 ). In Question 3, you will load a dataset into JASP and perform a multiple regression analysis to estimate a regression equation to predict aerobic performance based on several predictors. Once you perform the analyses requested and answer the questions, you will export your results to pdf save each PDF as Question 2, Question 4

In this dataset, the 100 respondents were included in a study which recorded the number of hours spent watching television along with the LDL cholesterol value in an attempt to determine if television leads to a sedentary lifestyle which, in turn, increases the LDL cholesterol concentration.

Load the dataset into JASP then follow the directions below.

Step 1 – Correlation.

Open a new correlation (classical) module in JASP and name it Q1 Correlation Your Name. Enter both variables and perform Pearson’s r. Include the significance, flag significant results. Also include the scatterplot with densities and check statistics. Assess multivariate normality using the Shapiro Wilks test.

Use the results from the Q1 Correlation module to answer the following question:

Q1-1. What is the correlation value?

Step 2 – Simple Regression

Open a new analytical module under regression and name it Q1 Regression Your Name. Perform a linear regression analysis to determine if time watching television significantly increases LDL cholesterol concentrations. Load LDL_Chol as the dependent variable and Hours_TV as the predictor and include the intercept. Include the model fit, descriptives, coefficient estimates, Durbin-Watson results and casewise diagnostics to standardized residuals greater than 3. Use default values for specification. Plot the residuals vs predicted, the residuals histogram with standardized residuals, and the Q-Q plot of the residuals to establish normality.

Use the results from Q1 Regression to answer the following questions (round all answers to 3 decimal places):

Q1-2. What is the coefficient of determination for the model?

Q1-3. What would the LDL be for someone who never watches television?

Q1-4. What is F-value for the model?

Q1-5. Every hour per day spent watching television increases LDL cholesterol by how much?

Export the results of Question 1 to pdf, attach them and save them in Question 2.

A health researcher wants to be able to predict maximal aerobic capacity (VO2max), an indicator of fitness and health. Normally, to perform this procedure requires expensive laboratory equipment and necessitates that an individual exercise to their maximum (i.e., until they can longer continue exercising due to physical exhaustion). This can put off those individuals that are not very active/fit and those individuals that might be at higher risk of ill health (e.g., older unfit subjects). For these reasons, it has been desirable to find a way of predicting an individual’s VO2max based on more easily and cheaply measured attributes. To this end, the researcher recruits 75 participants to perform a maximum VO2max test, but also records their age, weight, and heart rate. Heart rate is the average of the last 5 mins of a 20 mins much easier, lower workload cycling test. The researcher’s goal is to be able to predict VO2max based on age, weight, and heart rate.

Step 1 – Pearson’s Correlation.

Open a new (classical) correlation analysis in JASP and name the module VO2 Correlation Your Name. For this one, just report Pearson’s r, significance, and include the heatmap.

Step 2 – Multiple Regression.

Open a new linear regression analysis and name it VO2 OLS Your Name. Create a linear regression equation which estimates the impact of weight and heart rate on VO2 Max while controlling for age and gender. Enter VO2 Max as the dependent, enter all scale variables as covariates and enter gender as a factor. Under model, enter age and gender under M0 to control for those effects. Under model summary, select R2 change, F2 change, and Durbin Watson. Under coefficients, select estimates and Tolerance/VIF. Set casewise diagnostics to standardized residual greater than 2. Finally, to affirm normality, plot the residuals vs predicted, residual histogram with standardized values, and the Q-Q plot of the residuals.

Use the results from VO2 OLS to answer the following questions:

Q3-1. What is the F-Statistic for the control model (M0)? (round to 3 decimal place)

Q3-2. What is the total amount of variance explained by the final model? % (round to 1 decimal place)

Q3-3. On average, how much higher is VO2 Max for Males than Females once all factors are considered? (round to 3 decimal place)

Q3-4. Every 1beat per minute increase in heart rate reduces the VO2 max by how much? (round to 3 decimal place)

Q3-5. Every 1 kg increase in weight decreases VO2 Max by how much? (round to 3 decimal place)

Export your results for Question 3 to pdf – (save as question 4)