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Results shared by StatCrunch members
Showing 1 to 15 of 1464 results matching Correlation
| Name/Notes |
Owner |
Created |
Size |
Views |
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 N1-Minutes and N2-Miles 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 | b8641e39-e592-4045-9308-0da6c1758f46-57963_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 R-sq = 0.078167631 Estimate of error standard deviation: 1.2912444
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Parameter | Estimate | Std. Err. | Alternative | DF | T-Stat | P-value |
|---|
| Intercept | 8.9338536 | 0.40508613 | ≠ 0 | 84 | 22.054208 | <0.0001 | | Slope | -0.18656284 | 0.069903352 | ≠ 0 | 84 | -2.6688683 | 0.0091 |
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Source | DF | SS | MS | F-stat | P-value |
|---|
| 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 Non-Hispanic 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 |
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