Chapter 1: Data and Statistics

Statistics

Can refer to numerical facts such as averages, medians, percent's, and index numbers that help us understand a variety of business and economic situations

can also refer to the art and science of collecting analyzing and presenting interpreting data

Data

the facts and figures collected, analyzed, and summarized for presentation and interpretation

Data Sets

All the data collected in a particular study

Elements

The entities on which data are collected

Variable

A characteristic of interest for the elements

Observation

The set of measurements obtained for a particular element

Scales of Measurement Include:

Nominal, Interval, Ordinal, ratio

Scales

Determine the amount of information contained in the data, and indicates the data summarization and statistical analyses that are most appropriate

Nominal

Data are labels or names used to identify an attribute of the element, A non-numeric label or numeric code may be used

Ordinal

The data have the properties of nominal data and the order of rank of the data is meaningful A non-numeric label or numeric code may be used

Interval

The data have the properties of ordinal data and interval between observations is expressed in terms of fixed unit of measure

ALWAYS numeric (the difference in scores example) (b-a= difference)

Ratio

The data has all the properties of interval data and the ratio of two values is meaningful Variables such as distance, height, weight, and time use the ratio scale

Must contain a zero value indicating that nothing exist for the variable at that point (twice as much)

Categorical Data

Labels or names used to identify an attribute of each element

Often referred to as qualitative data

uses either nominal or ordinal scale of measurement

can be either numeric or non-numeric

Appropriate statistical analyses are rather limited

Quantitative data

Indicates how many or how much (discrete,continuous) ALWAYS numeric Ordinary arithmetic operations are meaningful for quantitative data

Discrete

Measure how many

Continuous

Measures how much

Cross-Sectional Data

collected at the same or approximately the same point in time

Time Series Data

Collected over several time periods

helps analydtd understand: what happened in the past, identify any trends over time, and project future levels for the time series

Data Sources

Internal company records, Business database services, Government agencies, Industry associations, special interest organizations, internet

Data Available from Internal Company Records

Employee records, production records, inventory records, sales records, credit records, customer profile

Data Available from Selected Government Agencies

Census Bureau, Federal Reserve Board, Office of Mgmt&Budget, Department of Commerce, Bureau of Labor Statistics

Experimental Studies

The variable interest is first identified, Then one or more other variables are identified and controlled so that data can be obtained about how they influence the variable of interest

Observational Studies

(non-experimental) no attempt is made to control or influence the variables of interest (like a survey)

Data Acquisition Considerations

Time Requirement, Cost Acquisition, Data Errors

Descriptive Statistics

Most of the statistical information in newspapers, magazines, company reports, and other publications consists of data that are summarized and presented in a form that is easy to understand

Such summaries of data, which may be tabular, graphical, or numerical, are refers to as descriptive statistics

Tubular Summary

NOT SURE WHAT THIS IS

Graphical Summary

I believe its just a graph but get clarification

Numerical descriptive Statistics

The most common numerical descriptive statistic is the average or mean (might need some clarification as well)

Statistical Inference

The process of using data obtained from a sample to make estimates and test hypothese about the characteristics of a population ex: Population, Census, Sample, Sample Survey,

Population

The set of all elements of interest in a particular study

Census

Collecting data for the entire population

Sample

A subset of the population

Sample Survey

Collecting data for a sample

Process of Statistical inference

1. Population consists of all efficiency apartments. Average rent is unknown

2. A sample of 70 efficiency apartments is examined

3. The sample data provide an average rent of 490.80

4. The sample average is used to estimate the population average

Data Warehousing

Capturing, storing and maintaining the data, is a significant undertaking

Data Mining

Converting the data into a useful information, the most effective systems use automated procedures to discover relationships in the data and predict future outcomes

Data Mining Applications

Major applications of data mining have been made by companies with a strong consumer focus such as retail, financial and communication firms they also relate product to the consumer in order to predict future purchases like the what you might like section on a shopping page

Data Mining Requirements

Statistical Methodology such as multiple regression, logistic regression an correlation are heavily used, also needed are computer science technologies involving artificial intelligence and machine learning a significant investment in time and money as well

Data mining Model Reliability

A statistical model that works well with a particular sample may not work with another data can be divided into a training set and test set there is however a danger of over fitting the model to the point that misleading associations and conclusions appear to exist careful observations and extensive testing is important

Ethical guidelines

Strive to be fair thorough, objective and neutral as you collect, analyze, and present data

As a consumer of statistics, you should also be aware of the possibility of unethical behavior by others

The American Statistical Association