Design of experiments: experiments, observational studies, sample surveys; measurements & variables; replication & pseudo-replication.
Descriptive statistics: graphical & numerical summaries; the shape of a distribution; data screening & outliers.
Exploring relationships: predictor-response data; the least-squares line; residuals & transformations; prediction; the sample correlation coefficient; time series.
Probability: basic concepts; conditional probability & independence; random variables, the binomial distribution, the normal distribution.
Statistical inference: samples & populations; estimation, confidence intervals, hypothesis testing; inference for normal samples (one-sample, independent samples, paired samples); inference for proportions.
The theoretical concepts will be presented and illustrated in a series of lectures. The practical implementation of the concepts will be developed in a series of practicals in the computer laboratory. Microsoft Excel will be the vehicle of instruction. Examples in the lectures and practicals will be drawn from across the spectrum of statistical applications, to illustrate the utility of statistics to a vast range of scientific disciplines.