11 oct 2018

Relation between categorical variables

A '''chi-squared test''', also written as \(\chi^2\) test, is any statistical hypothesis test wherein the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Without other qualification, 'chi-squared test' often is used as short for Pearson's chi-squared test.

Chi-squared tests are often constructed from a Lack-of-fit sum of squares#Sums of squares|sum of squared errors, or through the Variance Distribution of the sample variance|sample variance. Test statistics that follow a chi-squared distribution arise from an assumption of independent normally distributed data, which is valid in many cases due to the central limit theorem. A chi-squared test can be used to attempt rejection of the null hypothesis that the data are independent.

Source: wikipedia

\[\chi^2 = \sum \frac{(\text{observed}_{ij} - \text{model}_{ij})^2}{\text{model}_{ij}}\]

\[\text{observed}_{ij} = \begin{pmatrix} o_{11} & o_{12} & \cdots & o_{1j} \\ o_{21} & o_{22} & \cdots & o_{2j} \\ \vdots & \vdots & \ddots & \vdots \\ o_{i1} & o_{i2} & \cdots & o_{ij} \end{pmatrix}\] |
\[\text{model}_{ij} = \begin{pmatrix} m_{11} & m_{12} & \cdots & m_{1j} \\ m_{21} & m_{22} & \cdots & m_{2j} \\ \vdots & \vdots & \ddots & \vdots \\ m_{i1} & m_{i2} & \cdots & m_{ij} \end{pmatrix}\] |

The \(\chi^2\) distribution describes the test statistic under the assumption of \(H_0\), given the degrees of freedom.

\(df = (r - 1) (c - 1)\) where \(r\) is the number of rows and \(c\) the amount of columns.

chi = seq(0,8,.01) df = c(1,2,3,6,8,10) col = rainbow(n = length(df)) plot( chi, dchisq(chi, df[1]), lwd = 2, col = col[1], type="l", main = "Chi squared distributions", ylab = "Density", ylim = c(0,1), xlab = "Chi squared") lines(chi, dchisq(chi, df[2]), lwd = 2, col = col[2], type="l") lines(chi, dchisq(chi, df[3]), lwd = 2, col = col[3], type="l") lines(chi, dchisq(chi, df[4]), lwd = 2, col = col[4], type="l") lines(chi, dchisq(chi, df[5]), lwd = 2, col = col[5], type="l") lines(chi, dchisq(chi, df[6]), lwd = 2, col = col[6], type="l") legend("topright", legend = paste("k =",df), col = col, lty = 1, bty = "n")