1What Is Statistics?
1. Collecting Data
2. Presenting Data
3. Characterizing Data
3, Charts & Tables
Presenting Data
4Average
Characterizing Data
5is the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information.
Statistics
6Types of Statistical Applications in Business
Application Areas
• Economics
– Forecasting
– Demographics
• Engineering
– Construction
– Materials
• Sports
– Individual & Team Performance
• Business
– Consumer Preferences
– Financial Trends
7Statistics: Two Processes
1. Describing sets of data and
2. Drawing conclusions (making estimates, decisions, predictions, etc. about sets of data based on sampling)
8Statistical Methods
1. Descriptive Statistics
2. Inferential Statistics
9Involves
• Collecting Data
• Presenting Data
• Characterizing Data
Purpose
• Describe Data
Descriptive Statistics
101. Involves
• Estimation
• Hypothesis Testing
2. Purpose
• Make decisions about population characteristics
Inferential Statistics
11Fundamental Elements of Statistics
1. Experimental unit
2. Population
3. Variable
4. Sample
5. Statistical Inference
6. 2. Measure of Reliability
12Object upon which we collect data
Experimental unit
13All items of interest
Population
14Characteristic of an individual experimental unit
Variable
15Subset of the units of a population
Sample
16Estimate or prediction or generalization about a population based on information contained in a sample
Statistical Inference
17Statement (usually qualified) about the degree of uncertainty associated with a statistical inference
Measure of Reliability
18Four 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
19Five 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
20is a series of actions or operations that transforms inputs to outputs. A process produces or generates output over time.
Process
21A process whose operations or actions are unknown or unspecified is called a
black box
22Any set of output (object or numbers) produced by a process is called a
sample
23Types of Data
1. Quantitative data
2. Qualitative data
24are measurements that are recorded on a naturally occurring numerical scale.
Quantitative data
25are measurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories.
Qualitative data
26Measured on a numeric scale.
Quantitative Data
27What type of data are these?
• Number of defective items in a lot.
• Salaries of CEOs of oil companies.
• Ages of employees at a company.
Quantitative Data
28Classified into categories
Qualitative Data
29What type of data are these?
• College major of each student in a class.
• Gender of each employee at a company.
• Method of payment (cash, check, credit card).
Qualitative Data
30Obtaining Data
1. Data from a published source
2. Data from a designed experiment
3. Data from a survey
4. Data collected observationally
31example of data from a Published source?
1. book,
2. journal,
3. newspaper,
4. Web site
32example of data from a designed experiment?
1. researcher exerts strict control over units
33example of data from a survey?
1. a group of people are surveyed and their responses are recorded
34example of data collected observationally?
1. units are observed in natural setting and variables of interest are recorded
35Samples
1. representative sample
2. random sample
36exhibits characteristics typical of those possessed by the population of interest.
representative sample
37of 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
38Every sample of size n has an equal chance of selection
random sample
39involves 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.
Statistical thinking
40results 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.
Selection bias
41results when the researchers conducting a survey or study are unable to obtain data on all experimental units selected for the sample.
Nonresponse bias
42refers to inaccuracies in the values of the data recorded. In surveys, the error may be due to ambiguous or leading questions and the interviewer’s effect on the respondent.
Measurement error
43Statistical Computer Packages
1. Typical Software
• SPSS
• MINITAB
• Excel
2. Need Statistical Understanding
• Assumptions
• Limitations
441. Typical Software
• SPSS
• MINITAB
• Excel
452. Need Statistical Understanding
• Assumptions
• Limitations
46Data-Collection Methods
1. Observational
2. Published source
3. Survey
4. Designed experiment
47Problems with Nonrandom Samples
1. Selection bias
2. Nonresponse bias
3. Measurement error