# STATISTICS FOR LIFE SCIENCES

STA1LS

2018

Credit points: 15

## Subject outline

This subject provides an introduction to applied statistics, and strengthens basic numeracy skills. It introduces students to the basic applied statistical methods used in the biological sciences, medical sciences, agricultural sciences, nutrition, and health sciences. The three main areas of study are descriptive statistics, probability, and statistical inference and the use of a statistical computing package is an integral part of this subject. The strengths and limitations of statistical models to enable informed thinking about sustainability are explored. This subject is apossible pre-requisite for the second-year subjects in statistics.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorDavid Farchione

Subject year levelYear Level 1 - UG

Exchange StudentsYes

## Subject particulars

### Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjects STA1SS; STA1PSY; STA1IDA; STA1STM; STA1CTS; ECO1ISB

Equivalent subjectsN/A

Special conditionsN/A

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroductory statistics a problem solving approach, 2nd ed.RecommendedKokoska, S. (2015).FREEMAN
ReadingsManual for SPSS and R with Examples from the Life SciencesPrescribedFarchione, D. (2018)La Trobe

## Graduate capabilities & intended learning outcomes

01. Convert data into information by using appropriate numerical and graphical summaries.

Activities:
Lectures: In the lectures we introduce the basic statistical tools used in statistics and apply these tools to practical examples in the Life Sciences discipline. Practice Classes: Students work through practical examples by applying the concepts and techniques learnt in the lectures. Computer Labs: Students use the SPSS statistical computer package to work through practical examples.
Literacies and Communication Skills (Quantitative Literacy)

02. Calculate probabilities and other quantities from discrete and continuous probability distributions and by applying the basic rules of probability.

Activities:
Lectures: In the lectures we introduce the basic statistical tools used in statistics and apply these tools to practical examples in the Life Sciences discipline. Practice Classes: Students work through practical examples by applying the concepts and techniques learnt in the lectures. Computer Labs: Students use the SPSS statistical computer package to work through practical examples.
Literacies and Communication Skills (Quantitative Literacy)

03. Identify and apply appropriate statistical inference methods for decision making.

Activities:
Lectures: In the lectures we introduce the basic statistical tools used in statistics and apply these tools to practical examples in the Life Sciences discipline. Practice Classes: Students work through practical examples by applying the concepts and techniques learnt in the lectures. Computer Labs: Students use the SPSS statistical computer package to work through practical examples.
Literacies and Communication Skills (Quantitative Literacy)

04. Compute, display and interpret numerical and graphical summaries, probabilities and various statistical inference procedures using the SPSS statistical software package.

Activities:
Lectures: In the lectures we introduce the basic statistical tools used in statistics and apply these tools to practical examples in the Life Sciences discipline. Computer Labs: Students use the SPSS statistical computer package to work through practical examples.
Literacies and Communication Skills (Quantitative Literacy)

05. Apply basic numeracy skills for calculating various quantities in statistics.

Activities:
Lectures: In the lectures we introduce the basic statistical tools used in statistics and apply these tools to practical examples in the Life Sciences discipline. Practice Classes: Students work through practical examples by applying the concepts and techniques learnt in the lectures.
Literacies and Communication Skills (Quantitative Literacy)

## Subject options

Select to view your study options…

Start date between: and    Key dates

## Melbourne, 2018, Semester 2, Day

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorDavid Farchione

### Class requirements

Computer Laboratory Week: 31 - 43
One 1.0 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Lecture Week: 31 - 43
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Practical Week: 31 - 43
One 1.0 hours practical per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

### Assessments

One 3-hour examination60 01, 02, 03, 04, 05
Five assignments (equivalent to 1200 words total)30 01, 02, 03, 04
Ten online quizzes (equivalent to 300 words total)10 05

## Singapore, 2018, Term L3, Day

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorYeliz Boglev

### Class requirements

Lecture Week: 19 - 23
One 36.0 hours lecture per study period on weekdays during the day from week 19 to week 23 and delivered via face-to-face.
"36-hours of Blended Lectures or Interactive Online Activities per teaching period delivered via face-to-face or online."

Practical Week: 19 - 23
One 2.0 hours practical per study period on weekdays during the day from week 19 to week 23 and delivered via face-to-face.
"20-hours of Computer-Based Practical Class per teaching period delivered face-to-face or as directed online learning."

### Assessments

One 3-hour examination60 01, 02, 03, 04, 05
Five assignments (equivalent to 1200 words total)30 01, 02, 03, 04
Ten online quizzes (equivalent to 300 words total)10 05

## Singapore, 2018, Term L3, Night

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorYeliz Boglev

### Class requirements

Lecture Week: 19 - 23
One 36.0 hours lecture per study period on weekdays at night from week 19 to week 23 and delivered via face-to-face.
"36-hours of Blended Lectures or Interactive Online Activities per teaching period delivered via face-to-face or online."

Practical Week: 19 - 23
One 2.0 hours practical per study period on weekdays at night from week 19 to week 23 and delivered via face-to-face.
"20-hours of Computer-Based Practical Class per teaching period delivered face-to-face or as directed online learning."