1. Introduction
Chapter 1

2. Snapshot of chapter Statistical Concept Types of statistics
Data & Variable
Source of Data

3. Statistical Concept Why statistics ?
Statistics refers to numerical facts.
It’s a group methods used to collect, analyze, present and interpret data to make decision.
Decision made by using statistical methods are called educated guesses.
It helps us to explain things in a proper way.
Why statistics ?

4. Population - versus - Sample
Population consists of all elements, whose characteristics are being studied. Example ?
A portion of the population selected for study is called Sample. Example ?
Types of sample:
Census or sample survey: Survey that includes every member of population.
Representative sample: Sample that represents the characteristics as closely as possible Ex: Survey to find average income of resident of Dhaka.
Random sample: If all the sample size of population have the same chance of being selected. Ex: lottery

5. Types of statistics
Descriptive Statistics : Consists of approaches for organizing, displaying and describing data by using table, graph and summary measure.
Inferential Statistics: Consists of method that use sample results to help make decision or predicting about population.
Ex: You may want to explore the starting salary of graduate, by selecting 1000 public and private universities recent graduates.
* Data Vs Information

6. Data and Variable
Variable: Characteristics under study that assumes different values for different elements.
Data: Data are the observed values of a variable. Hence, we collect data in a sample.
Table 1: IBA, DU, JU are elements
(members), starting salary of graduates
is variables and the information given
in table is called Data or data set.
Table 1: Starting salary of graduate
Salary (in taka) JU
25,000 DU
18,000
IBA
40,000
See exercise 1.9 & 1.10

7. Types of variable Quantitative / Numerical Qualitative / Categorical
Discrete
Continuous
Qualitative / Categorical
Nominal
Ordinal

8. Quantitative / Numerical variable
Variable that can be measured by numerically is referred as quantitative variable. Data collected on a quantitative variable are called quantitative data. Ex: Price of home.
Discrete variables: A variable whose values are countable is called a discrete variable. (Certain values with no intermediate values)
Ex: Number of cow sold at cow haat in a day. Number of people visited in a bank in a week.
Continuous Variable: Variable that can assume any numerical value over a certain interval.
Ex: Time taken to complete final exam. The time taken may be 1 hour 44.5 minutes. Similarly height of a person, weight of potatos.

9. Qualitative / Categorical variable
Variable that can not be measured by numerically but can be classified into two or more non-numeric categories is called Qualitative variable.
Nominal variable: Variable that have two or more categories without having any kind of natural order. Variables with no numeric value, such as occupation or political party affiliation or male or female.
Ordinal Variable: Ordinal variable is a categorical variable for which the possible values are ordered.
Ex: Educational qualifications, Quality of KFC’s food – Best, good, worst etc.
Practice & 1.15 in page 12

10. Cross section versus Time series data
Variable
Data collected on different element
at same point of time or period is called
Cross section data.
Data collected on same element
for same variable at different
points of time called Time series data.
Company
2016 income
H & M
$ 5 million
Wal - Mart
$ 4.5 million
Best buy
$ 3 million
Elements
Variable
Year
Total private university in BD
2014 60
2015 75
2016 89
Ex: See exercise 1.20 and 1.21

11. Sources of data Primary : First hand evidence.
Example: Surveys, opinion polls, Interview.
Secondary : Books, Article, Other research data.
Data can be collected internal or external sources.

12. Lets go for some practice

13. 1.22 m f
f^2
m * f
m^2 * f 5 12 10 8 16 20 26 25
Sum=