# Applied Statistics

AUT University

## Course Description

• ### Course Name

Applied Statistics

• ### Host University

AUT University

• ### Location

Auckland, New Zealand

Statistics

• ### Language Level

Taught In English

• ### Course Level Recommendations

Lower

ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

### Hours & Credits

• Credit Points

15
• Recommended U.S. Semester Credits
3 - 4
• Recommended U.S. Quarter Units
4 - 6
• ### Overview

An introduction to applied statistics. Topics include: summary statistics, plotting data, the Normal distribution, confidence intervals, hypothesis tests, basic statistical computing, simulation, regression, correlation, analysis of variance, and the detection of outliers.

STUDENT LEARNING HOURS:
The learning hours are a guide to the total time needed for a student to complete the paper:
Lectures 24
Labs/Forum 24
Self directed 102
Total learning hours 150

PRESCRIPTOR:
An introduction to applied statistics. Topics include: summary statistics, plotting data, the Normal
distribution, confidence intervals, hypothesis tests, basic statistical computing, simulation,
regression, correlation, analysis of variance, and the detection of outliers.
LEARNING OUTCOMES:
On successful completion of this paper students will be able to:
1. Produce and interpret appropriate graphs
2. Calculate and interpret summary statistics
3. Create a contingency table and test for dependence of variables
4. Compute probabilities from the Binomial and Normal distributions
5. Simulate data from the Binomial and Normal distributions
6. Calculate and interpret confidence intervals
7. Test and interpret a statistical hypothesis
8. Detect outliers
9. Fit regression models to multivariate data
10. Interpret the results of an ANOVA
11. Use statistical software to summarise and analyse data
CONTENT
? Basic statistical concepts
? Samples
? Measures of central tendency and variance
? Confidence intervals
? Binomial and Normal distributions
? Simulation of random variables
? Outliers Hypothesis testing
? t-tests for the mean, proportions and two samples
? Contingency tables
? Correlation
? Regression
? Analysis of variance
715184_2015_desc.doc
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LEARNING & TEACHING STRATEGIES
Students will be expected to read the relevant sections of the text before the first class for each
unit. The lecturer will provide activities designed to clarify and reinforce the ideas introduced in the
text. These will include group discussions, practical problems (usually using R) and written
exercises.
ASSESSMENT PLAN
Assessment Event Weighting Learning Outcomes
COURSEWORK 0.5
Assignments (0.3) 1-11
Mid Semester test (0.2) 1,2,4,5, 6, 8, 11
EXAMINATION 0.5 1-11
A+ A A- Pass with Distinction
B+ B B- Pass with Merit
C+ C C- Pass
D Fail
Overall requirement/s to pass the paper: To pass the paper, the student needs to gain:
? A minimum mark of 35% in examination, AND
Prescribed Text Recommended reading will be supplied
Software R available free of charge from CRAN

### Course Disclaimer

Courses and course hours of instruction are subject to change.

Eligibility for courses may be subject to a placement exam and/or pre-requisites.

Some courses may require additional fees.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Availability of courses is based on enrollment numbers. All students should seek pre-approval for alternate courses in the event of last minute class cancellations

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.