Business Statistics: Introduction and Data Collection

Business Statistics: Introduction and Data Collection

• Business statistics is a branch of mathematics that transforms data into useful information for decision-makers.

• Statistics are used in many business applications, such as memos, research, technical reports, journals, newspaper articles, and magazines.

• An example of statistics in use is "Consumer payment with credit cards increased from 18% in 2020 to 25%, while payment in cash decreased to 14% from 21%."

• Statistics helps to make better sense of numbers.

• Statistics helps to process and analyze numbers, and reduce uncertainty in decision-making.

• Decision-makers use statistics to present and describe business data.

• Decision-makers use statistics to draw conclusions from large groups, using smaller subsets of data.

• Example of this includes calculating the weight of students in a class to understand the entire student body.

• Decision-makers use statistics to predict future business activities.

• Example of this includes predicting oil prices.

• Decision-makers use statistics to improve business processes.

• Types of Statistics:

  • Descriptive statistics: Collect, summarize, and describes data, e.g., mean, median, tables, charts.
  • Inferential statistics: Draw conclusions and/or make decisions; use sample data about a population.
    • Example of inferential statistics: estimating the population mean weight using the sample mean weight. Another example: testing if the population mean weight is 120 pounds.

• Data:

  • Data is a collection of observations, such as measurements, genders, or survey responses.

• Parameter:

  • Represents a numerical measurement describing a characteristic of a population, e.g., population mean weight.

• Statistic:

  • Represents a numerical measurement describing a characteristic of a sample, e.g., sample mean weight.

• Types of Data:

  • Categorical (Qualitative/Attribute): Names or labels that represent categories. Examples: marital status, political party, eye color. Shirt sizes are also categorical data.
  • Numerical (Quantitative): Represent counts or measurements.
    • Discrete: Counted items. Examples: number of children, defects per hour.
    • Continuous: Measured characteristics. Examples: weight, voltage, amount of milk.

• Quantitative data:

  • Numerical data (counts or measurements), includes discrete and continuous types.
  • The weights of supermodels, ages of respondents.

• Categorical data:

  • Represents names or labels(categories).
  • Genders of professional athletes, shirt sizes on uniforms.

• Levels of Measurement:

  • Nominal: Categories only (e.g., survey responses: yes, no, undecided).
  • Ordinal: Categories with some order (e.g., course grades: A, B, C, D, or F).
  • Interval: Differences are meaningful, but no natural zero point (e.g., years: 1000, 2000, 1776, and 1492).
  • Ratio: Intervals with a natural zero point (e.g., prices of college textbooks, where $0 represents no cost).

• Collecting Sample Data:

  • Collect data in appropriate ways; random selection is important.
  • Improper collection methods lead to useless data.

• Methods of collecting sample data:

  • Observational study: Observing and measuring characteristics without modifying subjects.
  • Experiment: Applying treatment and observing effects on subjects.

• Sampling methods:

  • Simple random sample: Every possible sample of the same size has equal chances of being selected.
  • Random sample: Each individual in the population has an equal chance of being selected.
  • Probability sample: Each member has a known chance of selection (not necessarily the same).
  • Systematic sample: Selecting every kth element after a random starting point.
  • Convenience sample: Using readily available data.
  • Stratified sample: Dividing the population into subgroups (strata) and sampling from each.
  • Cluster sample: Dividing the population into clusters, randomly selecting some and including all members of those selected clusters.
  • Multistage sample: Combine basic sampling methods in multiple stages.

• Personal computer programs:

  • Minitab and Microsoft Excel are used for statistical analysis.
  • Minitab is designed for accurate analysis.
  • Excel is versatile but might not be as effective in specialized tasks compared to specific data analysis software.