1.0 WORKSHOP - FOR STUDENTS

Finish at 4:00PM

Finish Module 1 notes! :)

1.0 - Data Classification

Two main types

1. Categorical - Observation belongs in a group

   • Nominal - Named variables with no order, eg Fav colour etc
   • Ordinal - Ordering eg: Age groups -> Could also be Binary (Pairs, Male/Female or <20 >20 years) For categorical Data, the best types of summaries are often:
   • Count
   • Proportion/Percentage
   • Mode
     • These can be illustrated by:
     • Bar/Pie Charts
     • Clustered Bar Charts
     • Frequency Tables
     • Contingency Tables

2. Numerical - Numerical Value

   • Discrete - Number of students, have clear defined values
   • Continuous - Can assume any infinite value as precise

The criteria for a graph:

1. A title
2. Clearly Labeled Axis
3. Explanatory Comment
4. Clarity

Frequency Tables

Summarizing data by displaying the number of times a unique value appears in a dataset

Contingency Tables

A method to determine whether one variable is contingent on another.

2.0 - Summaries of Numerical Data

2.1 - Measures of Centre

• Defined as the “Middle” value of the data.

Can be measured by either finding the

• Median (Literal value eg) $\{1,2,3,4,5\}=3$ or $\{1,2,3,4,900\}=3$ is often denoted as $\tilde{y}$ or $\tilde{\upsilon }$
• Mean (Average sum of values) denoted as $\bar{y}$ In stats we often use Roman letters for known sample statistics and use Greek letters for fixed unknown parameters

2.2 - Histograms

• Visual representation of data with a single numerical value of interest

Specific terms for describing the shape:

1. Symmetric ->Both sides are equal
   • Normal - Basic Curve
   • Uniform - Like a box
   • Triangular - Awa
2. Skewed
   • Has a “Tail”
   • Can be skewed left or right
3. Bimodal or Multimodal

Cheatsheet

DATA → / ↓	categorical	numerical	—
categorical	clustered bar charts	comparative box plots	bar charts or pie charts
numerical	comparative box plots	scatter plots	histograms
—	bar charts or pie charts	histograms	—