At this point the teacher states the need for an objective statistical test to determine whether a 3:1 ratio actually exists and introduces the

chi-square test.

Chi-square tests and MANOVA were used to analyze the data.

The familiar formula for the

chi-square test statistic is written in terms of the observed (O) and expected (E) numbers in the cells of the 2 by J table.

The results of the

chi-square test of the question items has shown in Table 3 and separately for teachers and professionals.

The

chi-square test also establishes this observation.

After performing

Chi-square tests and binary logistic regression, the factors with independent effect on morbidity and mortality were listed.

Chi-square test was used for qualitative variable (frequency of post-operative pain, gender) and independent sample T-test for quantitative variable (age).

Table 3:

Chi-square test for attitude of students to tradition based on Their family income.

For statistical analysis, frequency and percentage (%) distributions were used and the

Chi-square test was conducted for variables.

The authors cover descriptive statistics (the histogram, average and standard deviation, normal approximation for data, measurement error and plotting points and lines), correlation and regression (including the RMS error for regression and the regression line), probability (chance, the binomial formula), chance variability (the law of averages, expected value and standard error, and normal approximation for probability histograms), sampling (errors, accuracy of percentages and case studies), chance models, including those used in genetics and tests of significance, including the

chi-square test.

Bivariate analyses were performed in order to acquire correlations using the

Chi-square test when both variables being compared were nominal.

Cross tabulation and results of the

chi-square test for qualities respected, admired and emulated by each gender/socioeconomic status group are displayed in Table 4.