MIS Exam
What Is Statistics
Statistics = collecting, organizing, summarizing, analyzing, interpreting data for decision making.
Data – recorded facts (numbers or labels).
Data set – all data in a study.
Elements (entities) – items measured (people, products).
Variables – characteristics of elements (age, major).
Observations – all variable values for one element (row).
Nominal | Labels only; no order | Major, gender, car type |
Ordinal | Categories have order | Class rank, satisfaction |
Interval | Numeric, equal differences, no true zero | Temperature °C, years |
Ratio | Numeric, equal differences, true zero | Income, age, distance |
If ratios are meaningful → Ratio scale
→ If zero doesn’t mean “none” → Interval
Categorical (Qualitative) – labels or names.
Quantitative – numeric.
Discrete – counts (0,1,2...).
Continuous – can take any value (weight, time).
Cross-sectional – multiple elements, one time.
Example: salaries of 50 employees in 2025.Time-series – one element across time.
Example: monthly sales Jan–Dec.
DATA SOURCES
Internal company records
Business databases
Government agencies
Industry associations
Special-interest organizations
Internet
New data = statistical studies:
Observational – observe, no control.
Experimental – manipulate variables.
Descriptive statistics – summarize data (tables, charts, mean).
Statistical inference – use sample data to infer population.
Population – all elements of interest.
Sample – subset of population.
Census – data from entire population.
Sample survey – collect data from a sample.
Analytics – turning data into insight.
Descriptive analytics – what happened.
Predictive analytics – what’s likely.
Prescriptive analytics – what should we do.
Big Data – huge data sets.
Data warehousing – central storage combining sources.
Data mining – automated pattern discovery.
Made by companies with large data warehouses.
Requires careful validation & testing.
Software used – Excel.
“Ethical Guidelines for Statistical Practice” developed by the American Statistical Association (ASA).