Applied Statistics


Course Information

Quantitative Analysis I is a course on the fundamental concepts and methods of statistics, covering topics such as averages, frequency distribution tables, stem & leaf diagrams, data analysis and use of probability tools such as correlation. Many jobs or professions require you to make objective decisions based upon statistical data. To help you make these kinds of decisions, this course shows you how to collect, analyse, and interpret data correctly. The course also shows you how to present data in ways that are clear and accurate.

Quantitative Analysis II is a course on the representation of sample data and methods of summarizing data. It also involves the use of probability tools such as correlation, regression analysis, as well as use of random variables, the Binomial, Poisson, Normal and Continuous Distributions and Hypothesis Tests to interpret data. Many jobs or professions require you to make objective decisions based upon statistical data you have collected and analysed accurately. To help you make these kinds of decisions, this course shows you how to collect, analyze, and interpret data correctly. The course also shows you how to present data in ways that are clear and accurate upon which management of companies and organization can based their decisions.

Quantitative Analysis I

Quantitative Analysis I is a course on the fundamental concepts and methods of statistics, covering topics such as averages, frequency distribution tables, stem & leaf diagrams, data analysis and use of probability tools such as correlation. Many jobs or professions require you to make objective decisions based upon statistical data. To help you make these kinds of decisions, this course shows you how to collect, analyse, and interpret data correctly. The course also shows you how to present data in ways that are clear and accurate.

Collecting and recording data; Averages and spread; Processing data; Representing data; Interpreting data; Scatter graphs and relationships; Probability; Regression.

This course does not involve complex mathematics, however, the basic principles of mathematics or equivalent skills as established by assessment, are recommended as the minimum mathematical knowledge required. Basic computer literacy is recommended.

At the end of this course, students should be able to:

  • Understand and discuss some of the issues and problems associated with collecting and interpreting data from surveys, polls, and other statistical studies.
  • Select and produce appropriate tabular and graphical formats for displaying univariate data sets and know how to summarize information about the centre and spread of a univariate data set.
  • Understand the concepts of probability, random variables and their distributions, in particular the binomial distribution and normal distributions.
  • Understand the concepts of estimation (confidence intervals) and hypothesis testing for population averages and percentages.
  • Select and produce the appropriate tabular and graphical formats for displaying bi-variate data sets and carry out correlation, regression and chi-square analyses.

Assignments and Projects

A number of assignments will be provided for students to complete on their own and return for grading on specified dates. A short research project involving data collection and analysis will also be given to the students to complete. Assignments will predominantly be assessed based on material that has been covered throughout the course.

Assessment

In order to successfully complete this course, it is recommended that students complete all assignments in order to achieve the learning outcomes of the course. Primary communication is by email.

Course Materials: Recommended text books, library materials, online materials, appropriate journal articles.

Course Length: One Semester of 4.0 – 4.5 Months or 12 – 15 weeks

Teaching Method: 60% online course presentation and 40% combination of coursework, assignments and practical exercises.

Total Hours: 3 hours per week

Award: Candidates must achieve the Pass Mark of 60% to be awarded the Certificate in Quantitative Analysis I.

Option #1: Instructor-facilitated Online Study

Course Venue: Flowers Online Learning (FOL) portal

Course Fee: US $150.00

Registration:
Rolling (Open Enrolment)

FOL Access Length: 12 months

Click here to apply


 

Option #2: Instructor-led Blended Learning

Course Venues: In-Class and FOL

Course Fee: US $500

Click here to apply

Quantitative Analysis II

Quantitative Analysis II is a course on the representation of sample data and methods of summarizing data. It also involves the use of probability tools such as correlation, regression analysis, as well as use of random variables, the Binomial, Poisson, Normal and Continuous Distributions and Hypothesis Tests to interpret data. Many jobs or professions require you to make objective decisions based upon statistical data you have collected and analysed accurately. To help you make these kinds of decisions, this course shows you how to collect, analyze, and interpret data correctly. The course also shows you how to present data in ways that are clear and accurate upon which management of companies and organization can based their decisions.

Representation of sample data; Methods for summarising sample data; Methods for Summarising data (dispersion); Probability; Correlation; Regression; Discrete random variable; The normal distribution; The binomial and Poisson distributions; Continuous random variables; Hypothesis tests.

Although this course does not involve complex mathematics, the basic Principles of Mathematics or equivalent skills as established by assessment, are recommended as the minimum mathematical knowledge required. Students might have also taken the course in Quantitative Analysis I courses before attempting this course. Basic computer literacy is recommended.

At the end of this course, students should be able to:

  • Understand and discuss some of the issues and problems associated with collecting and interpreting data from surveys, polls, and other statistical studies.
  • Select and produce appropriate tabular and graphical formats for displaying univariate data sets and know how to summarize information about the centre and spread of a univariate data set.
  • Understand the concepts of probability, random variables and their distributions, in particular the binomial distribution and normal distributions.
  • Understand the concepts of estimation (confidence intervals) and hypothesis testing for population averages and percentages.
  • Select and produce the appropriate tabular and graphical formats for displaying bi-variate data sets and carry out correlation, regression and chi-square analyses.

Assignments and Projects

A number of assignments will be provided for students to complete on their own and return for grading on specified dates. A short research project involving data collection and analysis will also be given to the students to complete. Assignments will predominantly be assessed based on material that has been covered throughout the course.

Assessment

In order to successfully complete this course, it is recommended that students complete all assignments in order to achieve the learning outcomes of the course. Primary communication is by email.

Course Materials: Recommended text books, library materials, online materials, appropriate journal articles.

Course Length: One Semester of 4.0 – 4.5 Months or 12 – 15 weeks

Teaching Method: 60% online course presentation and 40% combination of coursework, assignments and practical exercises.

Total Hours: 3 hours per week

Award: Candidates must achieve the Pass Mark of 60% to be awarded the Certificate in Quantitative Analysis II.

Option #1: Instructor-facilitated Online Study

Course Venue: Flowers Online Learning (FOL) portal

Course Fee: US $150.00

Registration:
Rolling (Open Enrolment)

FOL Access Length: 12 months

Click here to apply


 

Option #2: Instructor-led Blended Learning

Course Venues: In-Class and FOL

Course Fee: US $500

Click here to apply