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Showing 1 to 12 of 12 data sets matching coefficient
Data Set/Description Owner Last edited Size Views
anscombe.txt
Anscombe's 4 data sets for regression. They are very different, yet have the same correlation and regression coefficients.
butlerMay 31, 2011360B773
10 highest peaks in Rockies and Appalachians
Is there more variation in the heights of the highest peaks in the Rockies (newer) or in the Appalachians (older)? Use coefficient of variation to find out!
anderson_instructorAug 18, 2018174B153
Validity Continuous Data---Class Example
Validity data for class example. Compute correlation, coefficient of determination, standard error of prediction, regression equation, predicted value for X = 70, and 95% confidence interval for X = 70
mldMay 21, 2014120B217
Economic Freedom Index (2018)
This set contains data from: -The Heritage Foundation's "Economic Freedom Index", measuring total economic freedom, property rights, government integrity, tax burden, business freedom, and investment freedom. -The World Bank Group's GDP per capita, Net National Income per capita, and GDP percentage growth -The UN's Human Development Index, total and inequality adjusted -The Gini coefficient measures for several countries from 2013 to 2017 -Reporters Without Borders' "Press Freedom Index"
williamharrisonSep 10, 201817KB135
Inspecting Anscombe's quartet_Scaringelli_A
1) X1 and y1 correlation coefficient: 0.81642052 X2 and y2 correlation coefficient: 0.81623651 X3 and y3 correlation coefficient: 0.81628674 X4 and y4 correlation coefficient: 0.81652144 This means that the strength of each relation is about the same for all four, although x4 & y4 have the highest correlation coefficient and x2 & y2 have the lowest correlation coefficient. 2) The four calculations lead you to conclude that the strength of the linear association between the two quantitative variables of each x and y. With all of the four correlations around 0.816 we can conclude that there is a moderately strong linear association between each x and y variable and this holds throughout all four sets of variables. 3) See posted scatterplots 4) X1 y1 The direction of this association is positive. The form is linear but the strength of the scatter is weak. X2 y2 The direction of this association starts out positive and then switches to negative around an x value of 11. This is because the form of the association is curved; however the strength of the association is strong. X3 y3 The direction of this association is positive. The form is linear and the strength of the association is strong except for one outlier that stands away from the overall pattern of the scatterplot at x=13 and y=12.74. X4 y4 The direction of this association is positive. The form is linear and the strength of the association is a very strong straight vertical line at x=8 except for one outlier that stands away from the overall pattern of the scatterplot at x=19 and y=12.5.
ajs5633Jul 8, 2014391B58
McGuire and Sosa
Correlation of Coefficient between the home runs of Sosa and McGuire.
emprogboyDec 9, 20134KB31
Anscombe's Data
Anscombe's data that illustrates how the correlation coefficient can be similar for very different non-linear associations.
sirelfmanFeb 29, 2012348B26
Turbine CoefficientsstatmanoneNov 13, 2008154B209
Investigating the Coefficient of DeterminationcassitySep 25, 2018279B74
Critical Values for Correlation CoefficientchenleOct 9, 2015307B58
Problem 4.50
Prediciton Coefficients
ds-14483%scAug 11, 2008187B30
Investigating the Coefficient of Determination activitycthomas8247Feb 12, 2018282B11


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