Chapter 1. Basic Conceptions - Nguyên Lý Kế Toán | Trường Đại học Tôn Đức Thắng

Chapter 1. Basic conceptions in statistics Chapter 1 - Content
• 1.1 What is statistics? The goal of statistics.
• 1.2 Basis conceptions in statistics.
• 1.3 Classification of data and variables
• 1.4 Types of measurement scale.
• 1.5 Descriptive statistics and statistical inference. 8 What is statistics?
The term statistics can refer to numerical facts
such as averages, medians, percents, and index
numbers that help us understand a variety of
business and economic situations.
Statistics can refer to the science of collecting,
organizing, and analyzing data.
The goal of statistics is to gain understanding from data. 9
1.2 Basic conceptions in statistics
In this part, we are going to learn some conceptions: • Data and data set • Population • Sample • Element • Observation • Variable. 10 Data and Data Sets
• Data are the pieces of information or figures
collected, analyzed, and summarized for
presentation and interpretation.
• All the data collected in a particular study are
referred to as the data set for the study. 11 Population, Sample, Element, Observations, and Variables -1  Population:
 A population is an entire group you are interested in studying.
 You can think of a population as a collection of persons,
things, or objects under study.  Sample:
 A sample is a subset of the population. 12 Population, Sample, Element, Observations, and Variables -1 Example 1:
Mrs. Smith wants to do a statistical analysis on
students' final examination scores in her math class for the past year.
Should she consider her data to be a population data set or a sample data set?
ANS: She has all of the data that pertains (liên quan đến)
to her investigation -> population. 12 Population, Sample, Element, Observations, and Variables -2 Example 2:
 A group of students surveys 100 students from their
freshman class to determine the number of pets in each student's household.
 The group plans to compute statistical findings on their
data and generalize these findings to the homes of all freshmen students.
Should the group consider their data to be a population data set or a sample data set? 12 Population, Sample, Element, Observations, and Variables -3
 Elements are the entities on which data are collected.
 A variable is a characteristic of interest for the elements.
• The set of measurements obtained for a particular
element is called an observation.
• A data set with n elements contains n observations. 9
Example: Elements, Variables, and Observations -4 Element Names Variables
• the data set contains 8 elements.
• five variables: WTO status, Per Capita GDP($), Trade
Deficit ($1000s), Fitch Rating, Fitch Outlook.
• observations: the first observation (Armenia)
Member, 5400, 2673359, BB-, Stable. 10 1.3 Classification of data -1
Categorical/qualitative data:
 Qualitative data take on values that are names or labels.
 Examples: The color of a ball (e.g., red, green,…), gender (male or female), Quantitative data: 
take on values that are numeric.
They present a measurable quantity.
 Examples: the number of students attending in the class,
heights (in centimeter) of female student in the class. 11 1.3 Classification of data -2
 Classify data as quantitative or categorical.
 Daytime temperature readings (in degrees
Fahrenheit) in a 30-day period.
 Heights (in centimeters) of plants in a plot of land.
 Number (0, 1, 2, or so on) of people attending a conference.
 Names (first and last) of a group of students who took an exam. 12 1.4 Scales of Measurement -1 
data can be classified as being on one of
four scales: nominal, ordinal, interval, and ratio.
 Nominal scale is used for labeling categorical data.
 Nominal scale is the measurement scale in which the
numbers serve as “tags” or “labels” to classify or identify the objects/categories.
 The numbers do not define the object characteristics. The
only permissible aspect of numbers in the nominal scale is “counting”. 13 1.4 Scales of Measurement -2
 A good way to remember all of this is that “nominal”
sounds like “name” and nominal scale is kind of like “names” or “labels”. • Here are some examples: 14 1.4 Scales of Measurement -3 
 Ordinal scale is used for labeling categorical data.
 In the ordinal scale, numbers present “greater than” or
“less than” measurement, such as preferences or ranking.
 With ordinal scale, the order of the values is what’s
important and significant, but the differences between two objects is not really know. 15 1.4 Scales of Measurement -4  16 1.4 Scales of Measurement -5 
 “Ordinal” is easy to remember because it sounds
like “order” and that is the key to remember “ordinal scale”. 17 1.4 Scales of Measurement -6 
 The interval scale has properties of ordinal scale with
the interval between observations is expressed in terms of . .
 The difference between two adjacent points is called unit of measure.
 The interval scale allows us to describe “how much more
or less” one observation than another. 18 1.4 Scales of Measurement -7 
 The interval scale doesn’t have a “true zero”.
Zero value is a convention. Thus, ratios of values of the variable are not meaningful. .
 With interval data, we can add and subtract, but can not multiply or divide. 19 1.4 Scales of Measurement -8 
The centigrade temperature scale represents
an interval scale of measurement.
 The distance between two adjacent points on this scale is  Temperature can be below  + = . No problem  is not twice as hot as ” 20