S1 249

Course Introduction

• Class starts 10 minutes after the scheduled time.

• No breaks during class, but students can use the bathroom if needed.

• Office hours: Sundays, 4 PM to 5 PM.

• Students can ask questions right after class.

Course Requirements

• Textbook is necessary; students don't need to purchase the ebook.

• Registration and payment are required for homework submission visibility.

• Students can bring laptops and notebooks to class but must show work during exams.

• Calculators are allowed but communication during exams is prohibited.

Examination Structure

• Exams consist of multiple choice questions and problem-solving, with more credit given to problems.

• All assessments will be in person and conducted using paper and pencil, no online tools allowed.

• High pass rate noted from previous classes.

• Department policies apply to grading.

Course Goals

• Aim to help students learn to find and interpret information using statistical techniques.

• Emphasis on practical application of knowledge gained during the course.

• Students will learn to conduct hand calculations alongside using technology.

Chapter 1 Overview: Introduction to Statistics

• Statistics is defined as a method to derive information from data.

• Importance of data for making informed decisions; without data, statistics cannot be done.

• Various fields utilize statistics: accounting, marketing, social sciences, medicine, etc.

Understanding Data

• How to assess data trends through past patterns (e.g., weather predictions, stock market forecasts).

• Statistics’ role in predicting behaviors or outcomes based on historical data.

• Emphasizes the importance of data in areas such as electoral processes.

Populations and Samples

• Definition of a population in statistics as a large group from which samples are drawn.

• Samples are smaller subsets intended to infer characteristics of the larger population.

• The significance of randomness and proper representation in sample selection.

Descriptive vs. Inferential Statistics

• Descriptive statistics: summarizing and presenting data to describe a sample.

• Inferential statistics: making predictions about a population based on sample data.

Variables in Statistics

• Definitions of parameters (characteristics of populations) and statistics (characteristics of samples).

• The distinction between categorical (qualitative) variables and numerical (quantitative) variables.

Types of Variables

• Categorical variables: can be nominal (no order) or ordinal (ordered).

• Numerical variables: can be discrete (countable values) or continuous (infinite values).

Data Structures

• Cross-sectional data: collected at a single point in time.

• Time-series data: collected over time to identify trends.

• Panel data: follows the same subjects over multiple time points.

Conclusion

• Understanding the definitions and applications of statistics is crucial for effective analysis in various fields.