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Showing 1 to 15 of 1464 results matching Correlation
Name/Notes Owner Created Size Views
Correlation between Minutes and Miles is:
Correlation Coefficient for Paired Data (Minutes, Miles)
dolorestodaroJun 22, 201967B1
Correlation between Hours prior to working and Hours when not working is:
Correlation for paired data (N1, N2)
annakmcgJun 20, 201988B3
Correlation between Record_ID# and SALARY is:
kalinrossJun 18, 201959B4
Bar Plot With Data C2 vs C1 CorrelationannakmcgJun 16, 2019174B7
Correlation between N1-Minutes and N2-Miles is:
haley.stoneJun 16, 201961B6
Correlation between Minutes and Miles is:
Minutes and miles correlation
martinezandy680Jun 15, 201955B7
Correlation between Hrs. per wk and Age is:
Correlation of hours/age of game playing
isangsmith17Jun 15, 201957B7
Correlation between Age and Hours is:
cgruver1Jun 15, 201952B7
Correlation between MMXT and MMNT is:
b8641e39-e592-4045-9308-0da6c1758f46-57963_d2l_snhumlpJun 13, 201951B9

Simple linear regression results:

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

Parameter estimates:

ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept8.93385360.40508613 ≠ 08422.054208<0.0001
Slope-0.186562840.069903352 ≠ 084-2.66886830.0091

Analysis of variance table for regression model:

Simple Linear Regression
Correlation coefficient
kelliernJun 13, 20193KB11
Correlation between Cost and Miles is:
Correlation Q20
rachelelamJun 12, 201952B9
Correlation between Hrs slept prior to working and Hrs slept when not working is:
kelliernJun 12, 201996B7
Correlation between Non-Hispanic and Hispanic is:
Correlation 2016 Number of deaths and morality totals in the US
monaco1996May 27, 201975B14

Summary statistics:

ColumnnMeanVarianceStd. dev.Std. err.MedianRangeMinMaxQ1Q3IQRMode
Student5025.5212.514.577382.061552825.549150133825No mode
Correlation Between NSO and GPA
brodriguez705May 26, 20193KB11
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%.
awhitikerMay 10, 2019174B36

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