For Question 1, you will load a dataset into JASP and perform a correlation analysis and a simple regression to determine the relationship betwee

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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 

LDL_Chol Hours_TV
168 2.595041056
170 2.797427517
170 3.39
164 3.159039955
159 3.092402595
168 3.7
165 3.249408427
156 2.888637523
172 2.897454286
170 2.684321261
165 2.766328258
168 2.649433141
168 2.811045366
166 3.64
167 4.16
163 3.35
161 2.647565999
168 3.79
170 2.795904577
171 3.045671761
165 3.66
162 2.626149483
175 3.01
169 3.56
170 4.41
176 3.03
179 3.35
173 3.090394765
176 3.99
170 2.715664635
172 2.697116546
175 3.01
172 3.251243031
173 3.83
177 4.06
171 3.27
173 3.54
170 3.84
163 3.01218205
173 4.04
169 2.686413374
170 3.43
171 3.71
174 4.41
178 3.26
174 3.61
176 4.11
176 3.250547806
171 3.53
176 4.55
176 3.25
172 3.55
173 3.5
176 3.23
172 3.12
171 4.27
173 3.31
176 3.098778487
171 3.12
182 3.91
180 3.91
183 4.33
170 2.987071673
176 3.10493154
182 3.56
174 3.27
178 3.6
176 2.866647124
169 3.73
172 2.577060443
176 3.222755354
175 3.13
170 3.68
177 4.21
180 4.21
176 3.99
171 2.57324386
183 3.66
177 3.23
169 3.82
167 4.14
171 2.798365361
170 3.23
174 3.91
170 4.21
170 2.64554954
173 3.56
173 3.156208077
180 4.02
177 3.65
168 3.303111622
179 4
163 3.48
179 4.43
168 2.363621942
170 3.17
171 3.52
171 4.1
172 3.413805439
171 3.411823675

,

caseno age weight heart_rate gender VO2max
1 27 70.47 150 Male 55.79
2 63 50.34 144 Female 35
3 36 87.65 162 Male 42.93
4 26 89.8 129 Female 28.3
5 24 103.02 143 Male 40.56
6 29 77.37 152 Female 33
7 24 82.48 175 Male 43.48
8 27 75.94 160 Female 30.38
9 25 97.11 148 Male 40.17
10 22 78.42 125 Female 36.01
11 30 88.02 155 Male 44.22
12 45 74.47 123 Female 38.76
13 25 75.98 147 Female 33.09
14 36 58.97 139 Female 44.81
15 23 111.8 145 Male 31.94
16 29 79.81 128 Female 34.48
17 37 56.18 163 Male 47.23
18 30 86.13 156 Male 45.06
19 36 87.3 127 Male 55.12
20 36 88.52 147 Male 45.58
21 26 68.52 147 Female 37.52
22 31 76.7 129 Female 36.27
23 30 62.51 140 Male 62.5
24 24 60.9 172 Female 37.09
25 26 53.43 158 Male 44.27
26 23 68.52 133 Female 40.34
27 45 72.37 147 Male 55.19
28 38 82.16 144 Female 49.87
29 30 90.92 177 Male 38.06
30 21 95.51 139 Male 48.13
31 39 73.14 108 Female 42.42
32 26 62.59 111 Male 48.23
33 28 91.38 151 Male 42.96
34 41 94.29 145 Male 42.53
35 40 58.07 161 Male 40.9
36 25 90.25 132 Female 27.35
37 21 78.45 111 Female 38.7
38 22 74.78 147 Male 62.13
39 33 85.13 156 Male 45.69
40 28 101.25 133 Male 40.73
41 21 58.94 154 Male 41.82
42 37 101.06 131 Male 35.6
43 21 97.75 136 Male 42.2
44 22 72.5 123 Male 45.3
45 27 88.45 175 Male 40.02
46 24 72.29 187 Male 47.17
47 31 94.59 134 Male 44.43
48 28 115.42 134 Male 38.12
49 30 51.96 134 Female 50.05
50 31 89.35 129 Male 48.64
51 33 62.01 172 Female 36.49
52 23 68.03 127 Male 61.71
53 29 79.39 133 Female 33.84
54 25 81.91 138 Female 51.2
55 32 64 178 Female 35
56 29 59.78 122 Female 47.71
57 21 71.03 122 Male 52.3
58 34 50 183 Female 42
59 25 80.98 119 Male 55.66
60 31 112.59 116 Male 37.34
61 64 73.38 117 Female 40.58
62 26 62.85 149 Male 60.55
63 45 68.29 145 Female 37.93
64 21 94.68 131 Male 44.94
65 28 103.23 186 Male 35.01
66 45 81.01 169 Male 45.57
67 22 69.02 150 Female 36.63
68 20 55 141 Female 60
69 22 73.44 117 Female 40.52
70 33 60.31 119 Female 47.92
71 38 85.94 153 Male 45.84
72 30 64.65 141 Male 60.92
73 52 88.83 109 Male 32
74 22 82.41 146 Male 49.4
75 23 63 149 Female 50

,

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 re

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