2 Statical Data

1 Statistics & Data Prof. Hemant Kumar Jain : [email protected]

Statistics & Data

Hemant Jain B.Sc (PCM), M.Sc (Phy), B. Tech (Telcom & Elec), MDBA , MS (Comp. Sc.) USA

Monday, November 18, 2013


Contents

1. The Science of Statistics

2. Types of Statistical Applications in Business

3. Fundamental Elements of Statistics

4. Processes

5. Types of Data

6. Collecting Data

7. The Role of Statistics in Managerial Decision

Making



Learning Objectives

1. Introduce the field of statistics

2. Demonstrate how statistics applies to business

3. Establish the link between statistics and data

4. Identify the different types of data and data-

collection methods

5. Differentiate between population and sample data

6. Differentiate between descriptive and inferential

statistics



The Science of Statistics



What Is Statistics?

Why?

1. Collecting Data

e.g., Survey

2. Presenting Data

e.g., Charts & Tables

3. Characterizing Data

e.g., Average

Data

Analysis

Decision-

Making



What Is Statistics?

Statistics is the science of data. It involves

collecting, classifying, summarizing, organizing,

analyzing, and interpreting numerical

information.



Types of Statistical Applications in

Business



Application Areas

Economics

Forecasting

Demographics

Sports

Individual & Team Performance

Engineering

Construction

Materials

Business

Consumer Preferences

Financial Trends



Statistics: Two Processes

Describing sets of data

and

Drawing conclusions

(making estimates, decisions, predictions, etc.

about sets of data based on sampling)



Statistical Methods

Statistical

Methods

Descriptive

Statistics

Inferential

Statistics



Descriptive Statistics

1. Involves

Collecting Data

Presenting Data

Characterizing Data

2. Purpose

Describe Data

X = 30.5 S2 = 113

0

25

50

Q1 Q2 Q3 Q4

$



1. Involves

Estimation

Hypothesis

Testing

2. Purpose

Make decisions about

population characteristics

Inferential Statistics

Population?



Fundamental Elements

of Statistics




1. Experimental unit

Object upon which we collect data

2. Population

All items of interest

3. Variable

Characteristic of an individual experimental unit

4. Sample

Subset of the units of a population

P in Population

& Parameter

S in Sample

& Statistic




1. Statistical Inference

Estimate or prediction or generalization about a

population based on information contained in a

sample

2. Measure of Reliability

Statement (usually qualified) about the degree

of uncertainty associated with a statistical

inference



Four Elements of Descriptive Statistical Problems

1. The population or sample of interest

2. One or more variables (characteristics of the population or sample units) that are to be investigated

3. Tables, graphs, or numerical summary tools

4. Identification of patterns in the data



Five Elements of Inferential Statistical Problems

1. The population of interest

2. One or more variables (characteristics of the population units) that are to be investigated

3. The sample of population units

4. The inference about the population based on information contained in the sample

5. A measure of reliability for the inference



Processes



Process

A process is a series of actions or operations that transforms inputs to outputs. A process produces or generates output over time.



Process

A process whose operations or actions are unknown or unspecified is called a black box.

Any set of output (object or numbers) produced by a process is called a sample.



Types of Data



Types of Data Types of

Data

Quantitative Data

Qualitative Data


Quantitative data are measurements that are recorded on a naturally occurring numerical scale.

Qualitative data are measurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories.


Quantitative Data

Measured on a numeric

scale.

Number of defective items in a lot.

Salaries of CEOs of oil companies.

Ages of employees at a company.

3

52

71

4

8

943

120 12

21



Qualitative Data

Classified into categories.

College major of each student in a class.

Gender of each employee at a company.

Method of payment (cash, check, credit card).

$ Credit



Variables



Variable

Categorical : values that can be placed in categories

Numerical : Values that represent quantity

Discrete : numerical values that arise from counting process.

Continuous : numerical values that arise from measuring process and depends upon the precision of measuring device.



Collecting Data



Obtaining Data

1. Data from a published source

2. Data from a designed experiment

3. Data from a survey

4. Data collected observationally



Obtaining Data Published source:

book, journal, newspaper, Web site

Designed experiment:

researcher exerts strict control over units

Survey:

a group of people are surveyed and their responses are recorded

Observation study:

units are observed in natural setting and variables of interest are recorded



Samples

A representative sample exhibits characteristics typical of those possessed by the population of interest.

A random sample of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of selection.



Random Sample

Every sample of size n has an equal chance of

selection.



The Role of Statistics in

Managerial Decision Making



Statistical Thinking

Statistical thinking involves applying rational thought and the science of statistics to critically assess data and inferences. Fundamental to the thought process is that variation exists in populations and process data.

A random sample of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of selection.



Nonrandom Sample Errors

Selection bias results when a subset of the experimental units in the population is excluded so that these units have no chance of being selected for the sample.

Nonresponse bias results when the researchers conducting a survey or study are unable to obtain data on all experimental units selected for the sample.

Measurement error refers to inaccuracies in the values of the data recorded. In surveys, the error may be due to ambiguous or leading questions and the interviewers effect on the respondent.



Real-World Problem



Statistical Computer Packages

1. Typical Software

SPSS

MINITAB

Excel

2. Need Statistical

Understanding

Assumptions

Limitations



Key Ideas

Types of Statistical Applications

Descriptive

1. Identify population and sample (collection of experimental units)

2. Identify variable(s)

3. Collect data

4. Describe data



Key Ideas

Types of Statistical Applications

Inferential

1. Identify population (collection of all experimental units)

2. Identify variable(s)

3. Collect sample data (subset of population)

4. Inference about population based on sample

5. Measure of reliability for inference



Key Ideas

Types of Data

1. Quantitative (numerical in nature)

2. Qualitative (categorical in nature)



Key Ideas

Data-Collection Methods

1. Observational

2. Published source

3. Survey

4. Designed experiment



Key Ideas

Problems with Nonrandom Samples

1. Selection bias

2. Nonresponse bias

3. Measurement error


Documents

2 Statical Data