Lectures and practical applications of statistical data analyses.
Simple Statistical Distribution. Data tables. Numerical and categorical data. Frequency distributions. Frequency density. Statistical ratios and index numbers. Arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variation. Variance, standard deviation, Relative variation: variation coefficient. Concentration. Box-plot. Asymmetry.
Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Means and variance of marginal and conditional distributions. Association between statistical variables. Covariance and correlation.
Probability. Events. Probability. Rules for probability. Conditional events. Conditional probability. Independent events. Random variables. Association between random variables. Probability models for count data: uniform, binomial, Poisson. Gaussian probability model. Skewness and Kurtosis.
Statistical inference. Sample distributions: Student-t, chi-square. Point estimation. Properties of estimators. Methods of estimation: method of least squares, maximum likelihood estimation.
Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions.
Hypothesis testing. Null hypotheses and alternative hypotheses. Types of errors in testing hypothesis. Test rules. Significance level. Power of a test. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions. Test for independence and homogeneity.
Statistical models. The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model.
Newbold, P., Carlson, W.L. and Thorne, B. (2013), Statistics, Pearson-Prentice Hall.