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
The answer depends on the what the leading coefficient is of!
Depends on the equation.
The term coefficient refers to a number that is next to a variable. For example in the term 4x2, 4 is a coefficient, and 2 is an exponent; x is a variable.
It is the number that multiples the variable as for example 2x means 2 times x and here 2 is the coefficient of x.
Corr = cov(X,Y)/[stdev(X)*stdev(Y)] Negative correlation would be when X decreases as Y increases or vice versa.
Stop taking PLATO
Right.. Clearly u are supposed to be in a lesson so why are u asking me ? Not the Teacher ? -.-
1r
Let me assume that you are familiar with the (Pearson) correlation coefficient. If you estimate how one variable might be a linear function of another (using least-squares) then the measure of how strong the association is is known as that with which you are correlation coefficient. If you generalise by estimating what linear function one variable is of two or more other variables then the measure of how strong the relationship is is the multiple correlation.For mathematical reasons which may or may not interest you, and which I won't go into here, if we now go backwards we find that the multiple correlation for the situation where one variable is regressed against one other variable is just the square of the Pearson correlation coefficient.As you probably know, the Pearson ranges from -1 to +1. Because the multiple correlation is the squared value it ranges only from 0 to 1 and can indicate only degree of association, not the sense of direction.
The answer depends on the what the leading coefficient is of!
How to find the coefficient of uniformity for a particular sample give an example
Generally speaking it is the coefficient that produces a ratio between variables of 1:1. If the variables are of a dependent/independent framework, I find that Chronbach's or Pearson's produces the most accurate (desirable) results. Hope this helps for answering a very good question for what appears to be n enthusiastic novice investigator.
It is easy to find the correlation. First you see how far apart the dots are. if they are going UP like this / <---- it means its a positive correlation. if its like this \ <---- its a negative correlation. if its everywhere its a neutral (although they almost never do them in tests). To find out the strength is your opinion. If alot are grouped together almost making a line its a Strong correlation. Then you decide if its a Strong or Weak correlation depending on how close together the dots are. So put them together in a 1 mark question like::::it is a Strong Positive Correlation
i need to find out
Depends on the equation.
It is important to determine what the correlation is so that you can control it. If you can find out how two factors are related you can manipulate the situation.
The term coefficient refers to a number that is next to a variable. For example in the term 4x2, 4 is a coefficient, and 2 is an exponent; x is a variable.