Syllabus for Analysis of Variance and Regression

Welcome to STA70007 Analysis of Variance and Regression Duration One teaching period Contact hours Recommended 12 hours of study per week Pre-requisites STA70006 Foundations of Statistics Credit points 12.5 Aim This unit introduces students to fundamental statistical areas of research design and linear models. It examines how multiple regression and Analysis of Variance (ANOVA) can be used to analyse experimental and observational research using a variety of research designs. Unit learning outcomes (ULOs) Students who successfully complete this Unit should be able to: 1. apply and analyse data using multiple regression models 2. interpret the results of multiple regression 3. identify and explain the concepts of mediation and interaction (moderation) within multiple regression models 4. apply and analyse data using analysis of variance (ANOVA) models 5. explain the concept of interaction with ANOVA models and identify research problems where they can be usefully applied 6. interpret results of ANOVA models 7. interpret statistical analyses of data pertaining to theoretical and applied problems in psychology and health and write concise reports on the results of analyses in discipline- relevant style. Graduate attributes Schedule (
This unit may contribute to the development of the following Swinburne Graduate Attributes: Analysis skills Problem-solving skills Communication skills Ability to tackle unfamiliar problems Ability to work independently Set text Throughout this unit, the eText we use each week is: Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences (10th ed.). Cengage Learning. The electronic version of this text has been made available at no expense to you. You can use the links provided each week to access MindTap, which contains the interactive chapter readings and extra learning resources. SPSS To complete this unit, you will need to purchase the SPSS statistical analysis program. This will be required to complete activities and assignments from Week 1 of the teaching period. The current version is SPSS Statistics Base Grad Pack Version 28 ( . If you are currently running an older version of SPSS, this will still be compatible with all unit learning materials, however when it comes time to renew your SPSS licence, please upgrade to the latest version. Visit SPSS Grad Packs for Students by IBM ( and select ' SPSS Grad Pack v26 or v28?' at the bottom of the page to view version differences. Hearne Software ( is our preferred supplier, and offers Swinburne Online students a 10% discount. Use code SWIN10 at checkout. For further information on purchasing and installation see Textbooks, software and library ( in the Student Hub. Unit improvements Swinburne Online strives to continuously improve our units in order to provide a high-quality student experience. Please provide feedback through the Student Feedback Survey and our online teaching staff to help us make improvements to this unit. Active learning
You will be engaged in an active learning environment, undertaking regular online discussions, guided through the learning process by expert teaching staff who provide regular feedback. On average you will need to dedicate 10 hours each week for your learning that includes readings, discussion with peers, and assignments. Assignment Assignment task Word count/time limit (+/- 10%) Individual/team task Related unit learning outcomes (ULOs) Weighting Due date Assignment 1: Online tests N/A Individual 1, 2, 3, 4, 5, 6 20% All online tests are due 5pm A Period). See Schedule (https://swinburneonline.instru for recommended completion Assignment 2: Workbook assignment part 1 N/A Individual 1, 2, 3, 4, 5, 6, 7 20% 5pm AEST Friday 22 April 202 Assignment 3: Workbook assignment part 2 N/A Individual 1, 2, 3, 4, 5, 6, 7 20% 5pm AEST Monday 23 May 20 Assignment 4: Exam N/A Individual 2, 3, 4, 6, 7 40% Exam Period (13-24 June 202 Minimum requirements to pass this unit of study In order to achieve a pass in this unit of study, you must achieve an overall 50% minimum pass mark. Referencing Referencing conventions required for this unit are: APA.
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