STA201 Scientific Statistics (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)
On Campus
Orange Campus
Online
Wagga Wagga Campus
Session 2 (60)
On Campus
Wagga Wagga Campus
Online
Wagga Wagga Campus

Continuing students should consult the SAL for current offering details: STA201. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System

HD/FL

Duration

One session

School

School of Computing and Mathematics

Enrolment Restrictions

Available to undergraduate students only.

Students who have completed STA109, STA117, QBM217 or  STA401 may not enrol in STA201. This is because a significant component of material covered in STA201 is similar to material they have already studied in these other subjects.

Subject Relationships

QBM117 Contains some overlap in content. However, it excludes some topics and software needed for higher level STA subjects.
STA401 Subject contains significant overlap of content

Incompatible Subjects

QBM217, STA109, STA117, STA401

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to explain standard uses of Statistics in the media and in scientific papers, and determine whether the statistical methodology and conclusions drawn are appropriate;
  • be able to use a statistical package to: summarise data graphically and numerically and analyse data appropriately, and interpret the output;
  • be able to calculate and interpret probabilities, and use standard discrete and continuous probability distributions;
  • be able to explain the concepts of statistical inference, 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 apply basic principles of experimental design, such as determination of appropriate sample sizes, randomisation and blocking.

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.
  • 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 and Normal 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.
  • 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.
  • 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.
  • Chi-square tests: Testing whether data fit a specified distribution, checking distributions graphically, and testing for independence of two categorical variables.
  • Simple 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.
  • Experimental design: Difference between experimental and observational data, randomisation, causality, replication, blocking, sample size.

The information contained in the CSU Handbook was accurate at the date of publication: January 2020. The University reserves the right to vary the information at any time without notice.

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