STATS 101G : Introduction to Statistics

Science

2020 Summer School (1200) (15 POINTS)

Course Prescription

Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.

Course Overview

The purpose of this course is to provide students with an introduction to statistical investigation and analysis. Topics Include: Statistics and the process of investigation; types of investigations; data collection; tools for exploring and summarising data; proportions; tools for extrapolating from data (includes confidence intervals to convey uncertainty, statistical significance, t-tests, and P-Values); analysing relationships (includes comparing groups and one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.) Applying statistical techniques, interpreting statistical results and communicating statistical findings.

Course Requirements

Restriction: STATS 102, 107, 108, 191

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Identify the main components of a statistical investigation. (Capability 1 and 3)
  2. Identify the characteristics of well-designed studies, critique strengths and weaknesses of study designs and data collections. (Capability 1, 2, 3, 4, 5 and 6)
  3. Use appropriate tools for exploratory data analysis. (Capability 1 and 3)
  4. Form and communicate conclusions from basic exploratory analysis. (Capability 1, 2, 4 and 5)
  5. Apply basic concepts of proportions. (Capability 1 and 3)
  6. Apply the basic concepts of statistical inference and choose appropriate inferential tools. (Capability 1 and 3)
  7. Form and communicate the results of statistical analysis output. (Capability 1, 2, 4 and 5)

Assessments

Assessment Type Percentage Classification
Assignments and Online Quizzes 30% Individual Coursework
Test 20% Individual Test
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Assignments and Online Quizzes
Test
Final Exam
Test will count 10% and Exam will count 60% if this is more favourable.
A minimum of 45% is required in the Exam component of the paper to be eligible to pass.

Tuākana

Statistics has a Tuākana Programme where there is a work space and a social space shared with Science Tuakana students. Tutorials and one-to-one assistance are available.
Contacts are Susan Wingfield (s.wingfield@auckland.ac.nz) and
Heti Afimeimounga (h.afimeimounga@auckland.ac.nz).

Key Topics

1  –  Exploring Data    
Exploratory data analysis: sources of data, types of data, data organisation, types of variables, types of plots, types of numerical summaries, feature spotting, describing and comparing variables, graphical techniques with software. Proportional reasoning: estimates, likely outcomes, conditional situations, independence, relative risk, two-way tables of counts, notation, plots.
2  –  Observational Studies and Experiments    
Observational studies, experimentation, experimental design.
3  –  Randomisation Tests with Experiments    
Randomisation tests for medians, means, proportions, differences between medians, differences between means, differences between proportions.
4  –  Polls and Surveys    
Polls and surveys, random sampling.
5  –  Bootstrap Confidence Intervals    
Bootstrap confidence intervals for medians, means, proportions, differences between medians, differences between means, differences between proportions.
6  –  Confidence Intervals (Normality-based)    
Confidence intervals for population means and proportions and differences between means and proportions.
7  –  Hypothesis Testing    
Tests for population means and proportions. Tests for the difference between two means.  Large sample comparisons of two proportions.   
8  –  Data on Numeric Variables    
Integrated treatment of problems involving batches of data. How do tools for exploring data, confidence intervals, and hypothesis tests work together? One-way analysis of variance.   Paired comparisons.  
9  –  Data on Categorical Variables    
Chi-square test and graphical methods for two-way tables of counts.
10 – Relationships between Numeric Variables: Regression and Correlation    
Fitting straight lines by least-squares. Confidence intervals and tests for slope. Prediction intervals.  Residual plots.

Learning Resources

All learning resources are available on Canvas.
A Course book containing printed versions of the main lecture notes can be purchased from the Faculty of Science Student Resource Centre.

Special Requirements

Not applicable.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 20 hours per week involved in each 15 point course that they are enrolled in. (For a Summer School Course.)
For this course, you can expect a total of 36 hours of lectures, 36 hours of reading and thinking about the content and 48 hours of work on assignments and/or test preparation (including up to 12 hours of optional tutorials).

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

In response to student feedback, we updated the online course book  notes to better differentiate the white notes (interactive lecture notes and examples) from blue notes (detailed summary notes for each chapter). In this way they now are the same as the printed version.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

Published on 04/10/2019 08:42 p.m.