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Statical data in i
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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
2 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
3 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
4 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
The Science of Statistics
Monday, November 18, 2013
5 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
6 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
What Is Statistics?
Statistics is the science of data. It involves
collecting, classifying, summarizing, organizing,
analyzing, and interpreting numerical
information.
Monday, November 18, 2013
7 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Types of Statistical Applications in
Business
Monday, November 18, 2013
8 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Application Areas
Economics
Forecasting
Demographics
Sports
Individual & Team Performance
Engineering
Construction
Materials
Business
Consumer Preferences
Financial Trends
Monday, November 18, 2013
9 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Statistics: Two Processes
Describing sets of data
and
Drawing conclusions
(making estimates, decisions, predictions, etc.
about sets of data based on sampling)
Monday, November 18, 2013
10 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Statistical Methods
Statistical
Methods
Descriptive
Statistics
Inferential
Statistics
Monday, November 18, 2013
11 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
$
Monday, November 18, 2013
12 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
1. Involves
Estimation
Hypothesis
Testing
2. Purpose
Make decisions about
population characteristics
Inferential Statistics
Population?
Monday, November 18, 2013
13 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Fundamental Elements
of Statistics
Monday, November 18, 2013
14 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Fundamental Elements
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
Monday, November 18, 2013
15 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Fundamental Elements
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
Monday, November 18, 2013
16 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
17 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
18 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Processes
Monday, November 18, 2013
19 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Process
A process is a series of actions or operations that transforms inputs to outputs. A process produces or generates output over time.
Monday, November 18, 2013
20 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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.
Monday, November 18, 2013
21 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Types of Data
Monday, November 18, 2013
22 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Types of Data Types of
Data
Quantitative Data
Qualitative Data
Monday, November 18, 2013
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.
23 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
24 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
25 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Variables
Monday, November 18, 2013
26 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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.
Monday, November 18, 2013
27 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Collecting Data
Monday, November 18, 2013
28 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Obtaining Data
1. Data from a published source
2. Data from a designed experiment
3. Data from a survey
4. Data collected observationally
Monday, November 18, 2013
29 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
30 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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.
Monday, November 18, 2013
31 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Random Sample
Every sample of size n has an equal chance of
selection.
Monday, November 18, 2013
32 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
The Role of Statistics in
Managerial Decision Making
Monday, November 18, 2013
33 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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.
Monday, November 18, 2013
34 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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.
Monday, November 18, 2013
35 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Real-World Problem
Monday, November 18, 2013
36 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Statistical Computer Packages
1. Typical Software
SPSS
MINITAB
Excel
2. Need Statistical
Understanding
Assumptions
Limitations
Monday, November 18, 2013
37 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
38 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
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
Monday, November 18, 2013
39 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Key Ideas
Types of Data
1. Quantitative (numerical in nature)
2. Qualitative (categorical in nature)
Monday, November 18, 2013
40 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Key Ideas
Data-Collection Methods
1. Observational
2. Published source
3. Survey
4. Designed experiment
Monday, November 18, 2013
41 Statistics & Data Prof. Hemant Kumar Jain : [email protected]
Key Ideas
Problems with Nonrandom Samples
1. Selection bias
2. Nonresponse bias
3. Measurement error
Monday, November 18, 2013