Course Content: Quantitative Methods

Course SYLLABUS Outline

Quantitative Methods

COURSE: IS 211, BSIS
INSTRUCTOR: ED NEIL O. MARATAS, BS-STAT, MA-MATH
JRMSU-CAMPUS
Research Coordinator, CAS Department
ednielmaratas@gmail.com

Description:

Quantitative Methods applies statistical methods in marketing contexts in order to address business related questions and help make evidence-based decisions. In Quantitative Analysis you will learn to apply commonly used statistical methods in business contexts and how to interpret analyses performed by others.

Pre-requisite: Introduction to Statistics/ Probability and Statistics

Statistics is the science that deals with the (a) collection, (b) description, (c) analysis, (d) interpretation, and (e) presentation of data. Statistics can be used to describe a particular data set (termed descriptive statistics) as well as to draw conclusions about the population from a particular data set (termed inferential statistics).

Objectives:

The overarching objective of Quantitative Methods is for students to describe data and make inferences based on well-reasoned statistical arguments. The specific course objectives are to:

  • describe data with descriptive statistics;
  • perform statistical analyses;
  • interpret the results of statistical analyses; and
  • make inferences about the population from sample data.
  • Participation:

    Your time in class will be more enjoyable and productive if you participate fully in activities, discussions, and ask as well as answer questions.

    Assignments:

    There will be an assignment based on each of the classes, with the assignments are due electronically via email or in google classroom. Handwritten type is required for these assignments. Late assignments will receive a 10% penalty for every 24-hour period in which they are late, starting immediately after assignments are due.

    Quizzes:

    There will be a quiz based on each of the first four classes. The quiz grade that most negatively affects your grade the most will be dropped (optional). The quiz component will count 30% toward the course grade.

    Special Needs:

    If you have any particular needs that should be addressed before the start of the semester, please let me know. We can collaborate to meet any particular requirements you may have.

    Examination:

    There is a major exam that covers all of the course topics in each period. The examination is based on the in-class exercises, assignments, relevant readings, class discussions, and quizzes. The format of the examinations is varied with multiple choice, fill-in, short answer, and calculation-based questions. Students are encouraged to make and use a help sheet that is one standard piece of paper with handwritten notes on each side for the examination. The help sheet may contain notes, equations, definitional terms, worked examples, et cetera, but no material may be printed or attached to the help sheet. Each help sheet will be handed in with the exams. Standard calculators are required. The exam will account for 40% of the final grade.

    Getting Help:

    Help is available! Email: ednielmaratas@gmail.com or ednielmaratas@jrmsu.edu.ph or call me @ (09463251377) so that I can help you or so that we can make arrangements to talk about the material.

    Collaboration:

    Students are encouraged to discuss classroom topics, course notes, handouts, readings, previous quizzes, and assignments. Experience has shown that discussing course materials generally leads to better success for all who take part in the discussion, provided that all parties are actively engaged in the conversation. However, each student is responsible for turning in his or her own separate assignments. Quizzes and the exam must be done individually.

    Grading:

    Cumulative method as indicated in student handbook.


    Course Outline:

  • Course Expectation and Objectives
  • Data & Statistics: An Overview
  • Visualizing data
  • Describing Distributions
  • Probability
  • Normal distributions
  • Sampling Distributions
  • Interval Estimation
  • Hypothesis Testing
  • Inferences
  • Multiple Regression
  • Thank you for reading! See you in the next session:


    I'd love to hear your thoughts about the Testing Normality of a data set. Feel free to leave your comment section below.



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