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Correlation between Minutes and Miles is: 0.76206292(<0.0001)
Correlation Coefficient for Paired Data (Minutes, Miles)  dolorestodaro  Jun 22, 2019  67B  1  Correlation between Hours prior to working and Hours when not working is: 0.27958475
Correlation for paired data (N1, N2)  annakmcg  Jun 20, 2019  88B  3  Correlation between Record_ID# and SALARY is: 0.26865602
Correlation  kalinross  Jun 18, 2019  59B  4 
Bar Plot With Data C2 vs C1 Correlation  annakmcg  Jun 16, 2019  174B  7  Correlation between N1Minutes and N2Miles is: 0.76206292
Correlation  haley.stone  Jun 16, 2019  61B  6  Correlation between Minutes and Miles is: 0.76206292
Correlation
Minutes and miles correlation  martinezandy680  Jun 15, 2019  55B  7  Correlation between Hrs. per wk and Age is: 0.11290067
Correlation of hours/age of game playing  isangsmith17  Jun 15, 2019  57B  7  Correlation between Age and Hours is: 0.04125568
Correlation  cgruver1  Jun 15, 2019  52B  7  Correlation between MMXT and MMNT is: 0.98766525
Correlation  b8641e39e592404593080da6c1758f4657963_d2l_snhumlp  Jun 13, 2019  51B  9 
Dependent Variable: Hrs slept when not working Independent Variable: Hrs slept prior to working Hrs slept when not working = 8.9338536  0.18656284 Hrs slept prior to working Sample size: 86 R (correlation coefficient) = 0.27958475 Rsq = 0.078167631 Estimate of error standard deviation: 1.2912444
Parameter  Estimate  Std. Err.  Alternative  DF  TStat  Pvalue 

Intercept  8.9338536  0.40508613  ≠ 0  84  22.054208  <0.0001  Slope  0.18656284  0.069903352  ≠ 0  84  2.6688683  0.0091 
Source  DF  SS  MS  Fstat  Pvalue 

Model  1  11.876026  11.876026  7.122858  0.0091  Error  84  140.05421  1.667312    Total  85  151.93023    
Simple Linear Regression
Correlation coefficient  kelliern  Jun 13, 2019  3KB  11  Correlation between Cost and Miles is: 0.98775613
Correlation Q20  rachelelam  Jun 12, 2019  52B  9  Correlation between Hrs slept prior to working and Hrs slept when not working is: 0.27958475
Correlation  kelliern  Jun 12, 2019  96B  7  Correlation between NonHispanic and Hispanic is: 0.63236252(<0.0001)
Correlation 2016 Number of deaths and morality totals in the US  monaco1996  May 27, 2019  75B  14 
Correlation Between NSO and GPA  brodriguez705  May 26, 2019  3KB  11 
Angela Whitiker Lab 6 (Scatter Plot)
A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data.
Plot Interpretation
A close glimpse of the scatter plot data and the regression line reveals a positive upward trend of the Height measurement as a function of the Head circumference relationship.
Analysis of the correlation coefficient ( r ) value derived from the Height vs. Head Circumference data validates the above observation. With a value ( r = 0.8881 ), a compelling case can be made that for the data under consideration a positive linear relationship exists between the two variables (Height vs. Head Circumference). Although the relationship is not altogether perfect ( for r = 1.000 ), nevertheless there exits a strong relationship.
Regression Line Model
Y = 0.184 X + 12.494
For the linear equation above representing the regression line where X (the independent variable) represents Height and Y (the dependent variable) represents Head Circumference. The slope value (m = 0.184) represents a positive upward relationship between the two variables where for each increase of one inch in Height, correspondingly there is going to be an increase of 0.184 inch in Head circumference. The value of 12.494 represents the value that would have existed given a height of 0 inch, but in this case or situation this value has no practical significance. In other words no normal child would exist with a body height of 0 inch.
As had been already discussed previously for the Height vs. Head Circumference scenario, both the r value which indicate a positively correlation coefficient between the two variables and a visual inspection of the scatter plot validating that relationship, one could firmly conclude that the mean value of the Head Circumference could not be the same as the predicted value of the Head Circumference.
Coefficient of Determination
For the Height vs. Head Circumference scenario, the coefficient of determination was calculated and evaluated to be about 0.7887 ( or 78.87% ) suggesting the percentage of the total variation in the Head Circumference that is explained by the variation in the Height variable in the regression model. Alternatively, a large Coefficient of Determination value implies that the explained variation is a large portion of the total variation.
Proportion of Variability
R2 = r2 = (0.8881) 2 = 0.7887 ( or 78.87% )
Again for the present scenario, the Proportion of Variability value in the Head Circumference explained by the relation between Height and Head Circumference is 78.87%.
 awhitiker  May 10, 2019  174B  36 

