how to calculate the relationship between two variables?
97336
['Find the mean of all the x-values.', 'Find the standard deviation of all the x-values (call it sx) and the standard deviation of all the y-values (call it sy). ... ', 'For each of the n pairs (x, y) in the data set, take.', 'Add up the n results from Step 3.', 'Divide the sum by sx ∗ sy.']
1435641
['make sure both equations are in "y=" form.', 'choose some x-values that will hopefully be near where the two equations cross over.', 'calculate y-values for those x-values.', 'plot the points and see!']
1432463
The relationship between variables can be linear, non-linear, proportional or non-proportional. A proportional relationship is a special kind of linear relationship, but while all proportional relationships are linear relationships, not all linear relationships are proportional.
1106670
['Start withy − y1 = m(x − x1)', '(x1, y1) is actually (0, b):y − b = m(x − 0)', 'Which is:y − b = mx.', 'Put b on other side:y = mx + b.']
332846
The direction of the relationship between two variables is identified by the sign of the correlation coefficient for the variables. Postive relationships have a "plus" sign, whereas negative relationships have a "minus" sign. The Degree (Strength) of a Relationship.
67620
A relationship is a proportional relationship if its graph is a straight line. Example 1 : The equation y = 5x represents the relationship between the number of gallons of water used (y) and the number of minutes (x) for most shower heads manufactured before 1994.
1147665
['If b = 0 in a linear equation (so y = mx), then the equation is a proportional linear relationship between y and x.', 'If b ≠ 0, then y = mx + b is a non-proportional linear relationship between y and x.']
237975
mx and my are the means of x and y variables. the p-value (significance level) of the correlation can be determined : by using the correlation coefficient table for the degrees of freedom : df=n−2. or by calculating the t value : t=r√1−r2√n−2.
537093
A linear relationship (or linear association) is a statistical term used to describe a straight-line relationship between two variables. Linear relationships can be expressed either in a graphical format or as a mathematical equation of the form y = mx + b.
253922
['Find the mean of the set of data.', 'Subtract each number from the mean.', 'Square the result.', 'Add the results together.']
643560
["Step 1: Solve one of the equations for one of the variables. Let's solve the first equation for y: ... ", 'Step 2: Substitute that equation into the other equation, and solve for x. ... ', 'Step 3: Substitute x = 4 x = 4 x=4 into one of the original equations, and solve for y.']
893523
['Step 1: Multiply each equation by a suitable number so that the two equations have the same leading coefficient. ... ', 'Step 2: Subtract the second equation from the first.', 'Step 3: Solve this new equation for y.', 'Step 4: Substitute y = 2 into either Equation 1 or Equation 2 above and solve for x.']
211383
To measure the relationship between numeric variable and categorical variable with > 2 levels you should use eta correlation (square root of the R2 of the multifactorial regression). If the categorical variable has 2 levels, point-biserial correlation is used (equivalent to the Pearson correlation).
356993
The relationship between x and y is called a linear relationship because the points so plotted all lie on a single straight line. The number 95 in the equation y=95x+32 is the slope of the line, and measures its steepness.
882853
Another way to calculate the correlation coefficient (r) is to multiply the slope of the regression line by the standard deviation of X and then divide by the standard deviation of Y. Covariance: a measure of how much two variables change with respect to one another.
1124005
['subtract the Y values,', 'subtract the X values.', 'then divide.']
378743
The relationship between 2 numbers is defined by the transformation process that transforms the input number (x) into the output number (y). This relationship can be represented in multiple formats.