Statistics for the Sciences

University of Newcastle

Course Description

  • Course Name

    Statistics for the Sciences

  • Host University

    University of Newcastle

  • Location

    Newcastle, Australia

  • Area of Study

    Statistics

  • Language Level

    Taught In English

  • Prerequisites

    If you have successfully completed STAT1060 you cannot enrol in STAT1070 with the exception of students who are active in 12337 OR 12338 OR 12339 OR 40002 OR 40010 OR 40011.

  • 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

  • Host University Units

    10
  • Recommended U.S. Semester Credits
    3 - 4
  • Recommended U.S. Quarter Units
    4 - 6
  • Overview

    Description
    How do we use data to make informed scientific decisions? This course introduces students to statistical thinking, data collection, data presentation and statistical analysis. Examples from a range of science related disciplines are used to illustrate the key concepts.

    Although the emphasis is on applied data analysis rather than statistical theory, the course also provides an appropriate introduction for those students who intend to study statistics at a higher level.

     

    Learning outcomes
    On successful completion of the course students will be able to:

    1. Appreciate the role of statistics in developing scientific knowledge

    2. Relate probability and sampling concepts to statistical analysis of data

    3. Apply basic principles of experimental design when collecting data

    4. Analyse data using common statistical software and interpret results to solve science related problems.

    Content

    • Introduction & overview of statistics in the sciences
    • Understanding variation and describing univariate data
    • Understanding bivariate relationships
    • Collecting data - surveys and experiments
    • Probability concepts
    • Statistical inference hypothesis tests and confidence intervals
    • Simple bivariate statistical models including regression and ANOVA

     

    Contact hours
    Computer Lab
    Face to Face On Campus 1 hour(s) per Week for Full Term

    Lecture
    Face to Face On Campus 2 hour(s) per Week for Full Term

    Tutorial
    Face to Face On Campus 1 hour(s) per Week for Full Term

Course Disclaimer

Courses and course hours of instruction are subject to change.

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.