Year No. Offer Mode Description Cred. Pts 97 64001 S3 X DATA ANALYSIS 1.00
Practitioners in many disciplines are often required to deal with observations of variable phenomena and imprecise or approximate measurements. Statistics provides tools which help to identify the underlying nature of such phenomena, to evaluate the precision of the measurements, to discover the strength of the relationships between variables and to make predictions about the likelihood of particular events occurring in the future. This unit provides the statistical concepts, methods and skills necessary for students in business, engineering and the physical and social sciences to analyse and interpret data. Because these concepts are interdisciplinary in nature, students will encounter problems from many sources including their own area of interest. The statistical skills developed in this unit will form the basis for more advanced statistical methods and concepts in specialist fields.
Students will be introduced to the concepts involved in descriptive and inferential statistics. Topics include the role of statistics in a scientific investigation, methods of condensing, displaying, describing and presenting data, elementary descriptive statistics, elementary probability, the binomial, Poisson and normal distributions, single-sample inference, comparison of frequencies, regression and correlation, and inference for two or more samples.
On completion of this unit students should be able to:
Description Weighting(%)
- Examining Distributions. 16.00 Displaying distributions with graphs - categorical and quantitative variables, histograms, relative frequencies, stemplots, bar charts, shape, skewness, outliers. Describing distributions with numbers - mean, median, quartiles, boxplots, interquartile range, standard deviation, variance, degrees of freedom. The normal distribution - density curves, 68-95-99.7 rule, standardised observations, standard normal, using normal tables, assessing normality.
- Examining Relationships. 14.00 Scatterplots - interpretation, association, linearity, subplots, outliers. Correlation - interpretation. Least squares regression - interpretation
- Producing Data. 8.00 Designing samples - simple random samples, stratified sampling, multistage sampling, surveys, problems and cautions. Populations, inference, probability. Designing experiments - comparative experiments, completely randomised experiments, main principles of design, statistical significance, cautions.
- Sampling Distributions and Probability. 16.00 Sampling distributions - sampling variability, parameters and statistics, simulation, bias, precision. Probability, randomness, basic facts, equally likely outcomes, random variables, discrete distributions, mean and standard deviation, law of large numbers, continuous distributions, normal distributions. Sample proportions - sampling distribution, normal approximation. The binomial distribution - sample counts, binomial probabilities, mean and standard deviation. Sample means - sampling distribution, central limit theorem, law of large numbers.
- Introduction to Inference. 12.00 Estimation - point estimates, statistical confidence, confidence intervals, margin of error, C.I. for a population mean, sample size, cautions. Hypothesis testing - null and alternative hypotheses, reasoning, procedure, one and two-sided alternatives, p-values and statistical significance, tests for a population mean, tests with fixed significance level, tests from confidence intervals. Using significance tests - choosing a significance level, statistical and practical significance, cautions, multiple analyses. Inference as decision - type I and II errors, power, interpretation.
- Inference for means 8.00 - the t distribution, tests and C.I.'s, matched pairs procedure, assumptions, robustness. Comparing two means - comparative studies, sampling distribution, unpooled t procedure, assumptions, robustness.
- Inference for proportions 8.00 - assumptions, the z procedure, sample size, comparing two proportions, sampling distribution, tests and C.I.
- Inference for Two-way Tables. 8.00 Multiple comparison problem, two-way tables, expected counts, the chi-square test and distribution, test of equality of proportions, test of independence, robustness, comparison with z-test, follow-up analysis.
- Inference for Regression. 10.00 Introduction - the regression model. Inference about the model - C.I. for the slope, testing for a linear relationship, inference for prediction. Checking assumptions.
1996, Pace2000, Addison-Wesley.
Notz, W. & Busam, R. 1995, Study Guide for Moore's The Basic Practice
of Statistics, Freeman. (external students only).
ACTIVITY HOURS Private Study 142 Examinations 3 Assessments 20
No *F/S Marks Due Description Wtg(%) LBL 1 S 100.00 12/12/97 ASSIGNMENT 10.00 Y 2 S 100.00 02/01/98 ASSIGNMENT 10.00 Y 3 S 100.00 23/01/98 ASSIGNMENT 10.00 Y 4 S 30.00 END SEM 3 HOUR EXAM (RESTRICTED) PART A 30.00 N 5 S 40.00 END S3 PART B OF ABOVE EXAM 40.00 N
1 To obtain a pass in the unit, students must perform
satisfactorily in all aspects of assessment.
2 The due date for assessments is the date by which a student must
despatch an assignment to the USQ. The onus is on the student to
provide proof of the despatch date, if requested by the Examiner.
3 Students MUST retain a copy of all assignments which must be
produced if and when required by the Examiner.
4 Extensions for assignment submission may be granted in
extenuating circumstances. The decision to grant or refuse an
extension is made by the Examiner. Students should be aware that
an application for an extension does not guarantee that an
extension will be granted.
5 Students apply for extension by either applying at the time of
submitting an assignment or applying in writing prior to
submitting an assignment. All relevant documentation should
accompany the application.
6 If assignments are submitted after the due date and no extension
is granted, then a penalty up to a maximum of 20% of the
assignment mark for each working day late may apply.
7 No further assignments will be accepted for assessment purposes
after assignments or model solutions have been released, except
in extenuating circumstances.