Descriptive statistics: central tendency, arithmetic mean, geometric mean, median, percentiles, range, sum of squares, variance, standard deviation, standard error, coefficient of variability.
Probability distributions: Z, Student’s t, 2 and Fisher’s F.
Sampling from a normal distribution: distribution of sample means and variances. Linear model. Confidence interval for a sample mean.
Hypothesis test: concept, procedures. Type I and II error, test protection and power. One and two tailed tests.
Comparisons between proportions: 2 test.
Comparison of two sample means:: Student’s t test to compare a sample means with a benchmark, between two sample means independent or paired t test.
Comparison between more than two sample means: F test and one way analysis of variance (ANOVA I). Assumptions. Data transformation. Least significant differences.
Linear correlation and regression: regression equation, sources of variation in the linear regression, hypothesis test, linear correlation coefficient (Pearson).