Interpreting Graphs and Classifying Data

Interpreting Graphs and Classifying Data

Types of Data

  • Categorical Data: Non-numeric data that can be divided into groups.

    • Discrete Data:

    • Definition: Countable data, examples include nationality and gender.

    • Example: Number of people in different categories like ethnic backgrounds.

    • Ordinal Data:

    • Definition: Data that can be ordered or ranked but does not indicate the distance between entries.

    • Example: Clothing sizes (S, M, L, XL); these sizes can be ordered from smallest to largest but do not have fixed intervals.

  • Numerical Data: Quantitative data that can be measured.

    • Continuous Data:

    • Definition: Data that can take on any value within a range, often measured.

    • Example: Height, weight, and temperature, that can have infinitely many values.

    • Discrete Data:

    • Definition: Data that can only take on specific values, usually integers.

    • Example: Number of children in a family.

Types of Graphs

  1. Dot Plot: A simple way to show frequency counts of data points.

  2. Column Graph: Displays data with rectangular columns; useful for comparing different categories.

  3. Line Graph: Shows trends over time; points are connected by lines.

  4. Sector (Pie) Graph: Represents parts of a whole; each sector shows the proportion of each category.

  5. Divided Bar Graph: A bar graph that shows multiple categories stacked on top of each other within the same bar, useful to compare the total of multiple groups.

Examples of Classifying Data

  • Method of Travel to Work

    • Category: Categorical Data

    • Classification: Ordinal or Nominal depending on if it can be ranked (like public transport vs. personal vehicle).

  • Shoe Sizes

    • Category: Ordinal Data

  • Cranial Measurements

    • Category: Discrete Data

Classification Quick Reference

  • Nominal: Categorical data without a natural order (e.g., gender, nationality).

  • Ordinal: Categorical data with a natural ordering (e.g., rankings, satisfaction levels).

  • Discrete: Numerical data with countable values (e.g., number of students).

  • Continuous: Numerical data with uncountable values (e.g., height, weight).

Data collection

Cenus - entire population

sample - part of the population

Types of sampling

Simple random sampling - gathering a r