1. Introduction to statistics, what are statistics, Types of Statistics Some Basic Concepts, population, sample, types of variables, Levels of Measurement.

  2. Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation(Boxplot, Histogram, Bar charts, Error bars, Line charts, and scatterplot)

  3. Describing Data: Numerical Measures, Measures of Central Tendency (Mean, median and mode), Measures of Dispersion (Variance, Standard deviation, and standard error), Histogram, Normal curve Dot plot, Stem and leaf, Box plot, Kurtosis, and skewness.
  4. Some basics of Probability Concepts (introduction, definition of Probability. Discrete, Continuous (Normal distribution), Binomial, Hypergeometric and Poisson Probability Distributions.
  5. Sampling method and central method theorem estimation of sample size, Point Estimation and confidence intervals.
  6. Hypothesis Testing One sample test (one-tailed, two-tailed, Understanding the P-value, type one error and type two error), Two samples test (Pairs and group)
  7. Design and Analysis of Questionnaires, Likert scale, how can deal with missing value.
  8. Experimental Designs for social, marketing, agriculture, science and Clinical Trials and Analysis of Variance, One-Way ANOVA—Fixed-effects and the Random-effects Model, Two-WayANOVA and interaction and The Factorial Experiment design, Latin Square Design. Test of Assumption of data, Normality test, and Homogeneity test.
  9. Transformation of data and Multiple Comparisons (Post hoc tests) Fisher’s Least Significant Difference (LSD), Tukey’s Honestly Significant Difference (HSD), Dunnett’s,  Scheffé’s, Duncan, S.N.K, Sidak, Bonferroni and A Nonparametric Multiple-Comparison Procedure.
  10. Nonparametric Methods ( Introduction, Measurement Scales, Chi-Square of goodness-of-fit, The Sign test, The Wilcoxon Signed-Rank test, The Mann–Whitney Test, The Kolmogorov–Smirnov, The Kruskal–Wallis One-Way Analysis of Variance by Ranks, The Friedman Two-Way Analysis , The Spearman Rank Correlation Coefficient, the kappa statistic, McNemar's test, Odds Ratio, Risk Ratio, Incidence and prevalence, and Epidemic curve.
  11. Simple Correlation and Simple Regression for parametric and non-parametric data The Correlation Model, The Correlation Coefficient, Multiple Correlation, and Correlation Matrix.
  12. Multiple Regression analysis, Estimating Multiple Regression Coefficients, Forecasting Using Multiple Regression. Test of linearity, multicollinearity (also collinearity) and homoscedasticity in multiple regression.

How to read and understand statistical concepts in a scientific paper?