Pearson’s r in BlueSky Statistics Correlation Analysis Example’
This will cover Week 10 (Chapter 11: Correlation) Application Lesson videos. Students will have one opportunity and 24 hours to complete the 10 questions. The must be completed in one sitting.
Students must use Excel and BlueSky Statistics to complete this multiple-choice assessment.
Download this pre-populated Excel workbook file to complete your calculations.
You are interested in the relationship between obesity and renal disease so you design a project with 20 participants to assess the impact of weight loss on key renal parameters, particularly proteinuria. Specifically, you seek to find out if significant reductions in body mass index (BMI) through weight loss interventions could lead to improved renal outcomes, providing valuable insights into potential non-pharmacological strategies for managing renal disease and potentially preventing its progression to end-stage renal disease. Use the data attached to calculate the correlation between BMI and proteinuria using Pearson’s r in BlueSky Statistics for these 20 participants. Alpha is set at .05.
(NOTE: this data is contrived and randomly generated. It may or may not be realistic.)
BMI and proteinuria for 20 participants is provided in the spreadsheet.
In the correlation between BMI and Proteinuria (Prot) the Pearson’s r is:
Group of answer choices
0.71
0.7478
0.70
0.6969
What is the direction of the association?
Group of answer choices
Negative
No way to tell from the data
Positive
Neutral
PLACE YOUR ORDER HERE
What is the strength of the association?
Group of answer choices
Strong
No way to tell from the data
Moderate
Weak
When BMI increases…
Group of answer choices
Proteinuria increases
No way to tell from the data
Proteinuria decreases
Proteinuria remains neutral
What is the p value?
Group of answer choices
There is no p value in correlation statistics
0.0002
0.0006
0.05
The result is statistically significant. (True or False)
Group of answer choices
True
False
The results show a:
Group of answer choices
Small effect
No way to tell the effect from the data
Medium effect
Large effect
The effect size influences the sample size needed to achieve statistical significance. (True or False)
Group of answer choices
True
False
What is the percent of variance?
Group of answer choices
0.5592
0.5
No way to tell from the data
0.4856
Based on the percent of variance, we know that:
Group of answer choices
55.92% of the percentage of change in proteinuria is accounted for by the BMI
48.56% of the percentage of change in proteinuria is accounted for by the BMI
50% of the percentage of change in proteinuria is accounted for by the BMI
Cannot calculate percent of variance from the data
BMI
Prot
age
fasting glucose
random glucose
35
152
57
134
209
26
140
49
98
169
33
142
62
150
184
28
140
53
117
176
33
152
46
126
193
30
150
59
112
201
36
160
68
105
195
35
162
50
122
173
28
160
56
129
182
35
170
45
116
164
38
177
52
123
185
37
180
71
137
177
33
182
38
108
212
38
200
60
127
168
41
212
43
119
174
32
180
75
143
198
39
192
35
97
179
35
200
54
125
204
34
151
56
133
208
36
153
58
135
210
Pearson’s r in BlueSky Statistics Correlation Analysis Example
Exploring the Relationship Between BMI and Proteinuria: A Correlation Analysis
You are interested in the relationship between obesity and renal disease so you design a project with 20 participants to assess the impact of weight loss on key renal parameters, particularly proteinuria. Specifically, you seek to find out if significant reductions in body mass index (BMI) through weight loss interventions could lead to improved renal outcomes, providing valuable insights into potential non-pharmacological strategies for managing renal disease and potentially preventing its progression to end-stage renal disease. Use the data attached to calculate the correlation between BMI and proteinuria using Pearson’s r in BlueSky Statistics for these 20 participants. Alpha is set at .05.
(NOTE: this data is contrived and randomly generated. It may or may not be realistic.)
BMI and proteinuria for 20 participants is provided in the spreadsheet.
In the correlation between BMI and Proteinuria (Prot) the Pearson’s r is:
Group of answer choices
0.71
0.7478
0.70
0.6969
What is the direction of the association?
Group of answer choices
Negative
No way to tell from the data
Positive
Neutral
What is the strength of the association?
Group of answer choices
Strong
(since 0.6969 is close to 0.7)
No way to tell from the data
Moderate
Weak
When BMI increases…
Group of answer choices
Proteinuria increases
No way to tell from the data
Proteinuria decreases
Proteinuria remains neutral
What is the p value?
Group of answer choices
There is no p value in correlation statistics
0.0002
0.0006
0.05
The result is statistically significant. (True or False)
Group of answer choices
True
(p-value < 0.05)
False
The results show a:
Group of answer choices
Small effect
No way to tell the effect from the data
Medium effect
Large effect
The effect size influences the sample size needed to achieve statistical significance. (True or False)
Group of answer choices
True
False
What is the percent of variance?
Group of answer choices
0.5592
0.5
No way to tell from the data
0.4856
Based on the percent of variance, we know that:
Group of answer choices
55.92% of the percentage of change in proteinuria is accounted for by the BMI
48.56% of the percentage of change in proteinuria is accounted for by the BMI
50% of the percentage of change in proteinuria is accounted for by the BMI
Cannot calculate percent of variance from the data
BMI
Prot
age
fasting glucose
random glucose
35
152
57
134
209
26
140
49
98
169
33
142
62
150
184
28
140
53
117
176
33
152
46
126
193
30
150
59
112
201
36
160
68
105
195
35
162
50
122
173
28
160
56
129
182
35
170
45
116
164
38
177
52
123
185
37
180
71
137
177
33
182
38
108
212
38
200
60
127
168
41
212
43
119
174
32
180
75
143
198
39
192
35
97
179
35
200
54
125
204
34
151
56
133
208
36
153
58
135
210
Pearson’s r value.
0.69685
Pearson’s r in BlueSky Statistics Correlation Analysis Example
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