# STA401 Scientific Statistics (PG) (8)

Statistics is the science and art of making decisions in the presence of variability.  This subject provides a foundation in the basic practice of statistics. The subject's orientation is towards the sciences and covers both experimental and observational data. The emphasis is on understanding statistical concepts and applying acquired skills to data interpretation by the use of a modern software package.

## Availability

Session 1 (30)
Online
Wagga Wagga Campus
Session 2 (60)
Online
Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: STA401. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

## Subject Information

HD/FL

One session

##### School

School of Computing and Mathematics

##### Enrolment Restrictions

Students who have completed STA201 may not enrol in STA401. This is because a significant component of material covered in STA401 is similar to material they have already studied in this subject.

##### Subject Relationships

STA201 Covers similar content

STA201

## Learning Outcomes

##### Upon successful completion of this subject, students should:
• be able to judge and justify whether the statistical methodology and conclusions drawn in the media and scientific papers are appropriate;
• be able to use a statistical package to: summarise data graphically and numerically, analyse data appropriately, and interpret and present the output in a clear logical manner;
• be able to calculate and interpret probabilities, use standard discrete and continuous probability distributions, and assess the suitability of these distributions;
• be able to explain the concepts of statistical inference, regression and correlation, and apply these to confidence intervals and tests of hypotheses;
• be able to evaluate if the assumptions underlying statistical techniques are valid in a given scenario;
• be able to compare standard experimental designs, determine appropriate sample sizes and justify randomisation and blocking;
• be able to appraise a scientific problem and develop a statistical solution to that problem.

## Syllabus

##### This subject will cover the following topics:
• Descriptive statistics: Graphs and statistics as summaries, frequency tables, concept of population and sample, random variation, measurement levels (categorical, ordinal, interval, ratio), measures of location and dispersion, mean and median, standard deviation, range and coefficient of variation.
• Basic probability: Probability estimate from relative frequency and percentiles, conditional probability, probabilities when not all outcomes are equally likely.
• Random variables: Representing the outcome of experiments in terms of random variables, distinguishing between discrete and continuous random variables, calculating probabilities using the Binomial, Poisson, Exponential and Normal distributions. Describing and verifying assumptions of Binomial and Poisson distributions.
• Sampling distributions: The application of various sampling strategies and the distribution of sample means and proportions.
• Inference based on a single sample: Using a sample to gain information about a population, distribution of sample means and proportions, confidence intervals, hypothesis tests.Understanding power and what affects it. Introduction to non-parametric tests.
• Inference based on two independent samples: Using samples to gain information about populations, controlled experimentation, distribution of differences in sample means or proportions, confidence intervals, hypothesis tests. Comparison of paired-sample tests with tests for two independent samples.
• Analysis of variance: One-way Analysis of Variance for testing differences in more than two means, graphical displays, checks on assumptions and post hoc tests. Introduction to two-way Analysis of Variance.
• Chi-square tests: Testing whether data fit a specified distribution, checking distributions graphically, and testing for independence of two categorical variables.
• Linear regression and correlation: Estimating the intercept and slope of the Least Squares line, testing the slope of the regression line, interpreting coefficient of determination (R squared) and correlation. Introduction to multiple linear regression.
• Experimental design: Difference between experimental and observational data, randomisation, causality, replication, blocking and sample size.