Statistics for Business
Chapter 1 Data and Statistics
Philosophy of Statistics
- Bayesians: Belief question;
- Classical/Error statistics: decision question;
- Likelihood: evidence question;
- AIC: prediction question
Applications in Business and Economics
Accounting
e.g. Client’s balance sheet acceptable? Sample.
Finance
e.g. Investment recommendations.
dividend yiled above average -> maybe underpriced.
Marketing
e.g. Manufacture purchase sales data -> promotions.
Production
e.g. x-bar chart control production process.
Economics
e.g. Make forecasts.
- Inflation rates
- Producer Price Index (PPI), the unemployment rates, manufacturing capacity utilization.
Data
- Q: What is data? What is not?
Facts and figures. //so true and complete matters…
(Below is not information, but the features of data itself.)
Elements, Variables, and Observations
Any data, it’s very important to understand what it is about — variables.
Scales of Measurement
- Nominal (even numeric, still nominal data)
- Ordinal (properties of a categorical data: categorical + can be ranked)
- Interval scale (interval meaningful)
- Ratio scale
Categorical and Quantitative Data
- Categorical data: can be grouped as categories (See Chapter 2)
- Arithmetic not meaningful.
- Quantative data (See Chapter 3)
Cross-Sectional and Time Series Data
Cross-Sectional: at same time.
Data Sources
Existing Sources
Data companies: Dun & Bradstreet, Bloomberg, and Dow Jones & Company are three firms that provide extensive business database services to clients.
Internet
Government agencies: e.g. the U.S. Department of Labor maintains considerable data on employment rates, wage rates, size of the labor force, and union membership.
Statistical Studies
- Experimental
- identify variables (interests)
- effects between variables
- control
- Observational
- no attempt to control the variables of interest
Data acquisition: requirements + time cost matters.
Data Acqusition Errors
Wrong data worse than no data!
Experienced data analysts take great care in collecting and recording data to ensure that errors are not made.
- Error during collection
- Interviewee misinterpret the questions;
- Recording error;
- Outliers;
- Meaningless error (e.g. 20+ years old people has 20 years work experience);
Descriptive Statistics
bar chart, histogram, etc.
Statistical Inferences
- Population and sample
- Census
- Sample survey
- Statistical inference
- Population mean is unknown -> Sample mean is known (Sample mean => Population mean)
- Margin of error? Confidence level?
Computers and Statistical Analysis
Data Mining
data warehousing
One of the definition of data mining:
the automated extraction of predictive information from (large) databases.
Relationships in the data and predicting future outcomes.
Be careful over-fitting.
Ethical
to be fair, thorough, objective, and neutral as you collect data, conduct analyses, make oral presentations, and present written reports containing information developed.
When you see statistics in newspapers, on television, on the Internet, and so on, it is a good idea to view the information with some scepticism, always being aware of the source as well as the
purpose and objectivity of the statistics provided.
e.g. Misleading analysis. Bulbs example. Should stated.
statistical practitioners should avoid any tendency to slant statistical work toward predetermined outcomes.
e.g. unrepresentative samples are used to make claims.