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In a correlation coefficient the difference between r and r squared?
Wiki User
∙ 11y ago
r is correlation and can be positive or negative. If you want an analogy, consider it like the slope of a line. If the slope is negative, the line slopes downward and therelationship between the two variables (x & y) are inverse. That is, as x increases, y will decrease. If r is positive, then the line slopes upward and as x increases so does y. Now if x equals or is close to zero, there is no significant relationship between the two variables ... as x increases y does not change or fluctuates between positive and negative changes.
The closer r is to +1 or -1, the stronger the relationship between x and y.
Wiki User
∙ 11y ago
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Can A regression equation have a negative coefficient of correlation and a negative coefficient of determination?
It's not quite possible for the coefficient of determination to be negative at all, because of its definition as r2 (coefficient of correlation squared). The coefficient of determination is useful since tells us how accurate the regression line's predictions will be but it cannot tell us which direction the line is going since it will always be a positive quantity even if the correlation is negative. On the other hand, r (the coefficient of correlation) gives the strength and direction of the correlation but says nothing about the regression line equation. Both r and r2 are found similarly but they are typically used to tell us different things.
What is an example of two variables that would be positively correlated and two variables that could be negatively correlated?
False. Correlation coefficient as denoted by r, ranges from -1 to 1. Coefficient of determination, or r squared ranges from 0 to 1. I note that x,y data points that have a high negative correlation would plot with a negative trend or a negatively sloped line if a best fit regression line is determined. I note also that x,y data points with a high positive correlation would plot with a positive trend or positively sloped line if a best fit regression line is determined. The coefficient of determination for r = 0.9 and r= -0.9 would be 0.81.
If class attendance explained 16 percent of the variation in grade index among students what is the numerical value of the correlation between percentage of classes attended and grade index?
correlation equals 16% thus r squared (r^2) = 0.16 thus r = 0.4 or 40%
What should be the value of R squared for a good correlation?
1 is the best, 0 is the worst. So the closer you are to 1, the better. Beyond that, I can't tell you a specific cutoff. It depends on what you're trying to prove. Sometimes, you won't settle for anything less than 0.99. Other times, you'll be tickled pink to get a 0.3. But the whole point of an R-squared is to give a numerical representation of how close the correlation is without resorting to vague terms like "good correlation". Publish the value of R-squared and let the readers make their own decisions about whether it's "good" or "bad".
What is the chi square test used for?
The chi-squared test is used to compare the observed results with the expected results. If expected and observed values are equal then chi-squared will be equal to zero. If chi-squared is equal to zero or very small, then the expected and observed values are close. Calculating the chi-squared value allows one to determine if there is a statistical significance between the observed and expected values. The formula for chi-squared is: X^2 = sum((observed - expected)^2 / expected) Using the degrees of freedom, use a table to determine the critical value. If X^2 > critical value, then there is a statistically significant difference between the observed and expected values. If X^2 < critical value, there there is no statistically significant difference between the observed and expected values.
If the correlation coefficient r is equal to 0.35 find the coefficient of nondetermination?
The coefficient of nondetermination is found by 1.00-r squared so 1.00-0.35X0.35 1.00-0.1225 0.8772 round it to 0.88
Can A regression equation have a negative coefficient of correlation and a negative coefficient of determination?
It's not quite possible for the coefficient of determination to be negative at all, because of its definition as r2 (coefficient of correlation squared). The coefficient of determination is useful since tells us how accurate the regression line's predictions will be but it cannot tell us which direction the line is going since it will always be a positive quantity even if the correlation is negative. On the other hand, r (the coefficient of correlation) gives the strength and direction of the correlation but says nothing about the regression line equation. Both r and r2 are found similarly but they are typically used to tell us different things.
What are sophisticated mathematical processes that could be used in Mathematical Studies Project work according to the IBO?
chi squared test, pearsons correlation coefficient etc
What the difference between 10 squared and 9 squared?
19
What is the difference between 5 squared and 7 squared?
7 squared is 49 and 5 squared is 25. So the difference is 49-25=24
What is the meaning of correlation coefficient?
The correlation coefficient takes on values ranging between +1 and -1. The following points are the accepted guidelines for interpreting the correlation coefficient:0 indicates no linear relationship.+1 indicates a perfect positive linear relationship: as one variable increases in its values, the other variable also increases in its values via an exact linear rule.-1 indicates a perfect negative linear relationship: as one variable increases in its values, the other variable decreases in its values via an exact linear rule.Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear relationship via a shaky linear rule.Values between 0.3 and 0.7 (0.3 and -0.7) indicate a moderate positive (negative) linear relationship via a fuzzy-firm linear rule.Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative) linear relationship via a firm linear rule.The value of r squared is typically taken as "the percent of variation in one variable explained by the other variable," or "the percent of variation shared between the two variables."Linearity Assumption. The correlation coefficient requires that the underlying relationship between the two variables under consideration is linear. If the relationship is known to be linear, or the observed pattern between the two variables appears to be linear, then the correlation coefficient provides a reliable measure of the strength of the linear relationship. If the relationship is known to be nonlinear, or the observed pattern appears to be nonlinear, then the correlation coefficient is not useful, or at least questionable.
What is the coefficient of p in -8 p squared?
The coefficient of the given term is -8
How do we know if a correlation is significant or not?
There are several statistical measures of correlation: some require only a nominal scale, that is, data classified according to two criteria; others require an ordinal scale, which is the ability to determine whether one measurement is bigger or smaller than another; others require an interval scale, which allows you to determine the difference in values but not the ratio between them. [A good example of the latter is temperature measured in any scale other than Kelvin: the difference between 10 degrees C and 15 degrees C is 5 C degrees, but 15 C is not 1.5 times as warm as 10 C.]The contingency coefficient, which is suitable for nominal data, has a chi-squared distribution.The Spearman rank correlation, requiring ordinal data, has its own distribution for small data sets but as the number of units increases to n, the distribution approaches Student's t-distribution with n-2 degrees of freedom.The Kendall rank correlation coefficient can be used in identical situations and gives the same measure of significance. However, the Kendall coefficient can also be used to test partial correlation - whether the correlation between two variables is "genuine" or whether it arises because both variables are actually correlated to a third variable.The Pearson's product moment correlation coefficient (PMCC) is the most powerful but requires measurement on an interval scale as well as an underlying bivariate Normal distribution.The significance levels of these correlation measures are tabulated for testing.A simple "rule of thumb" for testing the significance of PMCC is that values below -0.7 or above 0.7 are highly significant. Values in the ranges (-0.7, -0.3) and (0.3, 0.7) are moderate, and values between -0.3 and +0.3 are not significant.
What is a numerial coefficient if the only number is a letter squared?
1 is the numerical coefficient if no other numeral is shown.
What is the difference between negative 4 squared and negative four squared in parenthesis?
Because in parenthesis you have to multiply it by something.
The difference between a number squared and the number?
When a number is squared, it has been multiplied by itself. For instance: 4 squared means 4x4, which is equal to 16.
What is an example of two variables that would be positively correlated and two variables that could be negatively correlated?
False. Correlation coefficient as denoted by r, ranges from -1 to 1. Coefficient of determination, or r squared ranges from 0 to 1. I note that x,y data points that have a high negative correlation would plot with a negative trend or a negatively sloped line if a best fit regression line is determined. I note also that x,y data points with a high positive correlation would plot with a positive trend or positively sloped line if a best fit regression line is determined. The coefficient of determination for r = 0.9 and r= -0.9 would be 0.81.
