Testing Significance of Linear Relationship A test of significance for a linear relationship between the variables $x$ and $y$ can be performed using the sample correlation coefficient $r_{xy}$. Testing Correlation Coefficient rho = 0 Use Correlation Coefficient calculator for testing significance of correlation coefficient. T test Calculator for testing correlation coefficient Data 1 : …
Calculator for Testing Correlation Coefficient $\rho = \rho_0$ Use this calculator for testing whether the correlation coefficient equals the specified value, i.e., $H_0:\rho=\rho_0$. T test Calculator for testing correlation coefficient $\rho=\rho_0$ Data 1 : X Data 2 : Y Enter Data (Separated by comma ,) Value of $\rho_0$) Level of Significance ($\alpha$) Tail Left …
Testing homogeneity of two correlation coefficient calculator Use this calculator to test the homogeneity of two correlation coefficients using $Z$-transformation. Homogeneity of two corr coeff Sample 1 Sample 2 Number of observations Correlation Coefficient Level of Significance ($\alpha$) Tail Left tailed Right tailed Two tailed Calculate Results Z1: Z2: Z-statistic: Z-Critical value(s): p-value: How …
Spearman’s Rank correlation Coefficient Let $(x_1, y_1), (x_2, y_2), \cdots , (x_n, y_n)$ be the ranks of $n$ individuals in two characteristics $A$ and $B$ respectively. Spearman’s Rank Correlation Formula Then the Spearman’s rank correlation coefficient is denoted by $\varrho$ and is given by $\varrho = 1- \dfrac{6 \sum_{i=1}^{n}d_i^2}{n(n^2-1)}$ where, $d_i = x_i – y_i$ …
Karl Pearson’s Correlation Coefficient Let $(x_i, y_i), i=1,2, \cdots , n$ be $n$ pairs of observations then the Karl Pearson’s coefficient of correlation between two variables $X$ and $Y$ is denoted by $r_{xy}$ or $r$ and is given by $r = \dfrac{Cov(X,Y)}{\sqrt{Var(X) Var(Y)}}$ where the sample covariance between $x$ and $y$ is $$ \begin{aligned} Cov(x,y) …
Covariance between X and Y Covariance measures the simultaneous variability between the two variables. It indicates how the two variables are related. A positive value of covariance indicate that the two variables moves in the same direction, whereas a negative value of covariance indicate that the two variables moves on opposite direction. Let $(x_i, y_i), …
