1. Chapter 1 Why Study Statistics?
BUS172
Instructor: Nahid Farnaz (Nhn)
North South University
BUS172:
Introduction to Statistics
Chapter 1 Why Study Statistics?

2. Dealing with Uncertainty
Everyday decisions are based on incomplete information
Decision makers cannot be certain of the future behavior of factors that affect the outcome
Why Study Statistics?
Numerical information is everywhere
Statistical techniques are used to make many decisions
Business decisions are made in an uncertain environment.
An understanding of statistical methods will help you make these decisions more effectively.
We make many decisions often based on incomplete information. More importantly, business decisions are made in an uncertain environment. Therefore, it is crucial that we understand how to collect, analyze and interpret data. In other words, we need to make sense of data. Sometimes computer software such as Excel or SPSS help us to quickly process, summarize and analyze data.

3. Importance of Statistics
To draw conclusions about the wider population.
Statistics is the study of how to make decisions about a population when our information has been obtained from a sample.
Some uncertainty will always remain.
Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc.

4. Key Definitions
A population is the collection of all items of interest that interest an investigator.
N represents the population size
For e.g. All students in your university
A sample is an observed subset of the population
n represents the sample size
For example: students majoring in Finance.
A parameter is a specific characteristic of a population
A statistic is a specific characteristic of a sample
Population is the complete set of all items. It is represented by “N”.
For example: All students in your university; All families living in Dhaka.
Sample is an observed subset of the population.
For example: Students majoring in Finance; Families living in Banani.

5. Population vs. Sample Population Sample a b c d b c ef gh i jk l m n
o p q rs t u v w
x y z
b c
g i n
o r u y
A parameter is a specific characteristic of a population
A statistic is a specific characteristic of a sample.
Values calculated using population data are called parameters
Values computed from sample data are called statistics

6. Example: Sample vs. Population
Suppose you want to find out the average age of voters in Bangladesh.
The population size is so large that we might take only a random sample, perhaps 500 registered voters and calculate their average age.
Since the average age is based on sample data, it is called a “statistic”
If the average age of the whole population was calculated, then the average age would be called a “parameter”.
Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc.

7. Random Sampling Simple random sampling is a procedure in which
each member of the population is chosen strictly by chance,
each member of the population is equally likely to be chosen,
and
every possible sample of n objects is equally likely to be chosen
The resulting sample is called a random sample

8. Descriptive and Inferential Statistics
Two branches of statistics:
Descriptive statistics
Collecting, summarizing, and processing data to transform data into information
Inferential statistics
provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge

9. Descriptive Statistics
Collect data
e.g., Survey
Present data
e.g., Tables and graphs
Summarize data
e.g., Sample mean =

10. Inferential Statistics
Estimation
e.g., Estimate the population mean weight using the sample mean weight
Hypothesis testing
e.g., Test the claim that the population mean weight is 120 pounds
Inference is the process of drawing conclusions or making decisions about a population based on sample results

11. Exercise 1.8
Determine if descriptive statistics or inferential statistics should be used to obtain the following information:
A graph that shows the number of defective bottles produced during the day shift over one week’s time.
An estimate of the percentage of employees who arrive to work late.
An indication of the relationship between years of employee experience and pay scale.
Answer:
Descriptive – to describe information about a one week sample.
Inferential – to estimate the true percentage of all employees who arrive late.
Inferential – to predict the relationshiop between years of experience and pay scale.