The Correlation Secret by Jason Fielder How to make the most of your forex trading by understanding forex pair correlation. The secret used by many institutional investors to make substan…Full description
Short research paper on orkiszewski's correlationDescripción completa
Short research paper on orkiszewski's correlationFull description
Descripción completa
SIEM
SPE-51396-PA Horizontal Well IPR Calculations
Syntethic Corellation done in PetrelFull description
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Regression and Correlation
RQD & UCS Correlation
SPE-51396-PA Horizontal Well IPR Calculations
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For Calculating Compressibility Factor
Report written at the University of Leeds, 2006
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II. Pearson Correlation - The Pearson correlation allows us to establish the strength of relationships of continuous variables. - One of the easiest ways wa ys to visualize the relationship between two variables is to plot the values of one variable against the values of the other. => Example : fictitious data on short-term memory memor y and language proficiency Here arc the scores for the first five students:
-A scatterplot/ scattergram +A graph in which the the vertical axis (y axis) indicates the dependent variables and the horizontal axis ( x axis) reprents the independent variables. + A point is plotted for each individual individual at the intersection of their scores.
positive correlation
negative correlation
- Sometime, the line does not touch all the points but but that it does reflect the general direction of the relationship
-It is also possible that no relationship will be apparent from the scatterplot
- The actual strength of the correlation is reflected in these scatterplots. The tighter the points cluster around the straight line, the stronger the relationship between the two variables
- Regression line : a technical term t erm for the imaginary straight line around which all the points cluster. The angle of the regression line is called the slope. - The amount of variation of the point from the regression line and the tightness of the clustering of the points to the line determine the magnitude, the strength of the correlation coefficient
1. Computing the Correlation Coefficient -We can equate the values or the two variables by converting them to z scores.
-z score is a way to standardize scores across tests and is computed as:
-The closer each student’s z score is on the two ratings (whether positive or negative), the greater the strength of the relationship between the two tests.
=>This is the value of the correlation coefficient for these data, the value of an absolutely perfect correlation. - The z score formula for the Pearson correlation:
(Notice that this formula uses NN - l in the denominator while the previous example used N. N- 1 is an adjustment for sample data.) r: correlation coefficient, always always be somewhere between -1 and 0 or 0 and and + l. The The closer the r is to ± l, the stronger the relationship between the variables.