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.
