Course Statistics

Descriptive Statistics, including: data description (data tables and graphs), central tendency (average, mode, median, deciles, quartiles and percentiles), dispersion (standard deviation, variance). Statistical computer program (SPSS). Estimation of population parameters (mean, standard deviation/variance, proportion). Principles of hypothesis testing (one tail and two tails). Parametric statistics: (1) similarity test of the average of one sample and two samples (t-test and z test), (2) test of similarity of the average of k samples (1-way ANOVA, 2-way factorial ANOVA, and post hoc test) , (3) correlation analysis (moment and partial products), (4) regression analysis. Test analysis requirements (normality of distribution, homogeneity/homoscedasticity of variance, linearity of homoscedasticity/heteroscedasticity relationship, independence of independent variables (multicollinearity), and auto correlation). Non-parametric statistics, includes comparative hypothesis testing: (1) one sample, (2) two independent samples, (3) two correlated samples, (4) many (k) samples, (5) associative hypothesis testing of nominal and ordinal data.

Program Objectives (PO)