What is this course about?
Professor David J. Hand of the Imperial College (London) wrote that statistical ideas and methods underline just about every aspect of modern life and whilst some are obvious, many statistical tools and concepts are very much hidden in the background. From urban city planning to healthcare, from public citizenry safety to education, we see how decisions were made, policies crafted and legislations passed with the statistical elements in them.
In this course, we endeavor to equip participants with a sound knowledge and strong grounding in intermediate and advanced statistical tools and methods. Conducted in an interactive and easy-to-understand approach, this course covers topics ranging from 3-Way Chi-Sq, to advanced Analysis of Variance (ANOVA) models and Logistic Regression models involving categorical targets.
At the end of this course, Participants will be able to:
1. Differentiate amongst the various statistical modeling tools,
2. Evaluate and assess the strengths and weaknesses of various statistical modeling techniques,
3. Describe and validate the assumptions of each statistical tool and model,
4. Design and apply the use of relevant analytical models and statistical solutions in addressing research problems.
1. Categorical Data Analysis: 3-Way Chi-Sq Test
(a) 3-Way Cross-tabulation Tables
(b) Control variables and its interpretations
• Segmentation Modeling
• Association Rule Mining
2. Means Testing: Analysis of Variance (ANOVA) Models
(a) 1-Way ANOVA and Post-Hoc Tests
(b) 2-Way ANOVA
(c) Analysis of Covariance (ANCOVA)
(d) Multiple Analysis of Variance (MANOVA)
(e) Repeated measures ANOVA)
3. Predictive Models: Regression models involving categorical targets
(a) Multiple Linear Regression
(b) Logistic Regression
*Software will be provided during training and participants will be taught how to use them during the course
This course is specially designed for data analysts, researchers and executives who are interested to learn intermediate to advanced statistical modeling techniques and its applications. Aimed at equipping participates with statistical modeling techniques ranging from ANOVA models to logistic regression modeling, this 2-day workshop offers participants the plethora of important statistical tools and models to analyze their data.
Participants should ideally possessed a tertiary qualification (Diploma and above) and be highly comfortable with quantitative discussions, applications and working with models and algorithms.
Assumed Skills and Knowledge
Participants should have attended the course “Introduction to Statistics and Quantitative Data Analysis”, or possessed knowledge of the content to the above course before signing up for this course.
Participants should generally be comfortable with quantitative discussions, applications and working with models, testing of assumptions and algorithms.
1. Skillsfuture (SF) Series
Singaporeans and Singapore PRs are eligible for 70% course fee funding for registration with effect from 28 October 2017.
Singaporeans aged 40 and above will enjoy 90% subsidy of course fee.
This funding is applicable to both individual and company-sponsored participants. Participants are required to achieve at least 75% attendance and/or sit and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.
2. SkillsFuture Credit (SFC)
With effect from January 2016, Singaporeans aged 25 years and above who received their SkillsFuture Credit account activation letter will be eligible for an initial credit of $500 which can be used to pay for course fees for a range of eligible skills-related courses. The credits can be used on top of existing course fee subsidies/funding.
This is only applicable for self-sponsored applicants. Application via SkillsFuture Portal can only be made starting from 60 days before the course commencement date.
Participants are required to sit for an open-book quiz where participants will be graded in accordance to the course objectives to ensure they are met in a practical way.
Certificate of Performance will be awarded if participant satisfies the course criteria.
1. All applications must be made via Online Registration at www.pace.sp.edu.sg
Course fees can be paid by the following payment modes:
Credit Cards, Internet Banking, NETS (Not Applicable for company sponsored)
a) For e-payment using Visa/Master cards and Internet Banking, please click on the ‘Make e-Payment’ button on the acknowledgement page to proceed.
b) For NETS payment, you can pay at:
Blk T1A, Level 1
Mon-Fri: 8:30am to 7:30pm
c) Please make cheques payable to “Singapore Polytechnic”. Please cross the cheque and write the Registration Reference ID, Applicant Name and NRIC/FIN number on the back of the cheque. Mail the cheque to:
500 Dover Road
Blk T1A, Level 1
Please note that an administrative charge of $15 will be imposed for any returned cheques from the bank or financial institution.
2. All successful applicants will be notified with a letter of confirmation via email.
3. Withdrawal and Deferment
Withdrawal and deferment notice must be made in writing to the Professional & Adult Continuing Education (PACE) Academy, Singapore Polytechnic via email accordingly:
Online Courses - firstname.lastname@example.org
All other Courses – email@example.com
For withdrawal cases, the portion of course fee to be refunded is based on the date of notice as follows:
- 2 weeks before the commencement of the course - Full refund
- Less than 2 weeks before commencement of the course - 70% refund
- On or after date of commencement - No refund
- There will be no refund or transfer of course once the account has been created.
The Singapore Polytechnic reserves the right to cancel or postpone any of the courses. Applicants will be duly notified and where applicable, the full fees will be refunded.
The Singapore Polytechnic also reserves the right to amend the fees charged or the period and duration of the courses.
The data provided to Singapore Polytechnic will be kept strictly confidential and will be used for the purpose of course administration. The data may be passed on to the relevant organisations that require the information related to the course.