The limits of a multiple correlation coefficient R1.23 are:
0 ≤ R1.23 ≤ 1
The multiple correlation coefficient, R1.23, lies within the range 0 ≤ R1.23 ≤ 1. It measures the strength of the linear relationship between one variable and a set of other variables, and it cannot exceed 1 or be less than 0.
Similar Questions
If X 1, X 2, … X nis a simple random sample without replacement of size n from a finite population of N units with mean μ and variance σ 2, the covariance of (X i, Xj ) will be:
If x = X- \(\bar{X}\) and y = Y - \(\bar{Y}\) and the number of pairs (X, Y) is n, then the Karl Pearson's coefficient of correlation is:
The given table shows the ranking of ten students in two subjects mathematics and statistics.
Mathematics
Statistics
3
6
5
4
8
9
4
8
7
1
10
2
2
3
1
10
6
5
9
7
The coefficient of rank correlation is:
The Karl Pearson’s correlation coefficient (r) between two random variables, X and Y, is computed by:
If the correlation between X and Y is 0.3, then correlation coefficient between 2X and 3Y is:
For three random variables X1 , X2 and X3, the pairwise correlation coefficients are r12 = r13 = r23 = r. The multiple correlation coefficient \(\rm R_{1.2.3}^2\) is
If the correlation coefficient between X and Y is 0.7, then the correlation coefficient between U and V, where U = X - 5 and V = Y + 2, is:
For Spearman's rank correlation, if the correlation coefficient is 0.7 and \(\rm \Sigma_{i=1}^n d_i^2=49.5\) then the value of sample size 'n' is:
If the regression coefficient bxy > 1 then byx is :
The two regression lines y = c + mx and x = a + dy always pass through:
Important Questions from Correlation and Regression
If the regression coefficient of x on y and y on x are \(- \frac{1}{2}\) and \(- \frac{1}{8}\) respectively, then what is the correlation coefficient between x and y?
For 10 observations on price (x) and supply (y), the following data was obtained:
∑x = 130, ∑y = 220, ∑ x 2= 2288, ∑y 2= 5506 and ∑ xy = 3467.
What is the line of regression of y on x?If X 1, X 2, … X nis a simple random sample without replacement of size n from a finite population of N units with mean μ and variance σ 2, the covariance of (X i, Xj ) will be:
If x = X- \(\bar{X}\) and y = Y - \(\bar{Y}\) and the number of pairs (X, Y) is n, then the Karl Pearson's coefficient of correlation is:
The given table shows the ranking of ten students in two subjects mathematics and statistics.
Mathematics
Statistics
3
6
5
4
8
9
4
8
7
1
10
2
2
3
1
10
6
5
9
7
The coefficient of rank correlation is:
