f. The value of linear correlation coefficient r is i. 0.948 ii. 0.974 iii. -0.948 iv. -0.974 g.Based on your answer in part (f), the linear dependance between weekly exercise time and change in cholesterol level is strong negative i. True ii. False H. When weekly exercise time is 460 minutes (approximately 66 minutes per day), the 95 percent confidence interval for change in cholesterol level is (197.3, 225.4). This means that: i. If one person exercises 460 minutes per week, his/hers change in cholesterol level will be between 197.3 and 225.4 with 95 percent certainty. ii. If several people exercise 460 minutes per week, their average change in choles- terol level will be between 197.3 and 225.4 with 95 percent certainty. I. When weekly exercise time is 460 minutes (approximately 66 minutes per day), the 95 percent prediction interval for change in cholesterol level is (155.1, 267.6). This means that i. If one person exercises 460 minutes per week, his/hers change in cholesterol level will be between 155.1 and 267.6 with 95 percent certainty. ii. If several people exercise 460 minutes per week, their average change in choles- terol level will be between 155.1 and 267.6 with 95 percent certainty.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
This is a continuation of the last question I posted:
f. The value of linear
i. 0.948
ii. 0.974
iii. -0.948
iv. -0.974
g.Based on your answer in part (f), the linear dependance between weekly exercise time and change in cholesterol level is strong negative
i. True
ii. False
H. When weekly exercise time is 460 minutes (approximately 66 minutes per day), the 95 percent confidence interval for change in cholesterol level is (197.3, 225.4). This means that:
i. If one person exercises 460 minutes per week, his/hers change in cholesterol level will be between 197.3 and 225.4 with 95 percent certainty.
ii. If several people exercise 460 minutes per week, their average change in choles- terol level will be between 197.3 and 225.4 with 95 percent certainty.
I. When weekly exercise time is 460 minutes (approximately 66 minutes per day), the 95 percent prediction interval for change in cholesterol level is (155.1, 267.6). This means that
i. If one person exercises 460 minutes per week, his/hers change in cholesterol level will be between 155.1 and 267.6 with 95 percent certainty.
ii. If several people exercise 460 minutes per week, their average change in choles- terol level will be between 155.1 and 267.6 with 95 percent certainty.
![300
200
100
-100
-200
100
200
300
400
500
Exercise Time
Figure 1: Scatter plot and fitted regression line
Coefficients"
Standardized
Coefficients
Unstandardized Coefficients
Model
B
Std. Error
Beta
Sig.
1
(Constant)
-239.772
10.130
-23.669
.000
ExerciseTime
.981
.033
.974
29.609
.000
a. Dependent Variable: Change
Figure 2: Estimated regression coefficients
Change](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3e2ac6b5-7297-4261-a489-d10ebbcc6556%2Ff5aefd28-6abc-4566-8706-c75aae50a943%2Fak0tp5hc_processed.png&w=3840&q=75)
![ANOVAª
Sum of
Squares
Model
df
Mean Square
F
Sig.
Regression 642950.535
1 642950.535 876.722
.000b
1
Residual
35201.145
48
733.357
Total
678151.680
49
a. Dependent Variable: Change
b. Predictors: (Constant), ExerciseTime
Figure 3: Analysis of Variance](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3e2ac6b5-7297-4261-a489-d10ebbcc6556%2Ff5aefd28-6abc-4566-8706-c75aae50a943%2Fh8e8zmd_processed.png&w=3840&q=75)
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