Chapter 1. Introduction

Chapter 1 (Korean)
Chapter 1 (Chinese)

1.1 κΈ°λ³Έ μ •μ˜ 및 κ°œλ…

  • A population(λͺ¨μ§‘단) is any specific collection of objects of interest. A sample is any subset or subcollection of the population, including the case that the sample consists of the whole population, in which case it is termed a census.

  • A measurement(μΈ‘μ •μΉ˜) is a number or attribute computed for each member of a population or of a sample. The measurements of sample elements are collectively called the sample data.

  • A parameter(λͺ¨μˆ˜) is a number that summarizes some aspect of the population as a whole. A statistic is a number computed from the sample data.

  • Statistics is a collection of methods for collecting, displaying, analyzing, and drawing conclusions from data.

  • Descriptive statistics is the branch of statistics that involves organizing, displaying, and describing data.

  • Inferential statistics is the branch of statistics that involves drawing conclusions about a population based on information contained in a sample taken from that population.

  • Qualitative data are measurements for which there is no natural numerical scale, but which consist of attributes, labels, or other nonnumerical characteristics.

  • Quantitative data are numerical measurements that arise from a natural numerical scale.

Grand Picture of Statistics

1.2 Overview

  • The sample average is an example of what is called a random variable: a number that varies from trial to trial of an experiment (in this case, from sample to sample), and does so in a way that cannot be predicted precisely.

  • A different samples have different levels of reliability.

  • Single estimation

  • The confidence interval: from the data we will construct an interval of values so that the process has a certain chance, say a 95% chance, of generating an interval that contains the actual population average.

  • probability

  • Sampling

  • ...

1.3 Presentation of Data

EXAMPLE 1. μž„μ˜λ‘œ μ„ νƒλœ 21λͺ… ν•™μƒλ“€μ˜ λ‚˜μ΄ 데이터이닀. 이듀 데이터에 λŒ€ν•œ λ„μˆ˜ λΆ„ν¬ν‘œλ₯Ό μž‘μ„±ν•˜λΌ.

18 18 19 19 19 18 22 20 18 18 17 19 18 24 18 20 18 21 20 17 19
age <- c(18, 18, 19, 19, 19, 18, 22, 20, 18, 18, 17, 19, 18, 24, 18, 20, 18, 21, 20, 17, 19)

# 1. Frequency Table
y <- table(age) ; y

# 2. Relative Frequency Table
prop.table(y)

# 3. Frequency Diagram
plot(y)

# 4. Relative Frequency Diagram
plot(prop.table(y))

μœ„μ˜ 데이터 μ„ΈνŠΈμ— λŒ€ν•œ λ„μˆ˜ λΆ„ν¬ν‘œ(frequency table)κ°€ μž‘μ„±λ˜μ—ˆλ‹€. 이 ν‘œμ—λŠ” x 의 μœ μΌκ°’μ΄ 첫 번째 행에 λ‚˜μ—΄μ΄ 되고, λ‘λ²ˆμ§Έ ν–‰μ—λŠ” 데이터 μ„ΈνŠΈμ— λ‚˜νƒ€λ‚˜λŠ” x κ°’μ˜ λΉˆλ„(frequency) f 값이 좜λ ₯λœλ‹€.

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