Warm-Up Set your signed welcome letter on the stool. 1. What is your favorite color? 2. What is your favorite place to shop? 3. What is your favorite food?

Types of Data

Key Terms Categorical variables Quantity variables Nominal variables Ordinal Variables Binary data. Discrete and continuous data. Qualitative and Quantitative traits/ characteristics of data.

Categorical Data The objects being studied are grouped into categories based on some qualitative trait. The resulting data are merely labels or categories.

Examples: Categorical Data Eye color blue, brown, hazel, green, etc. Newspapers: The Sun, The Mail, The Times, The Guardian, the Telegraph. Smoking status smoker, non-smoker Attitudes towards the death penalty Strongly disagree, disagree, neutral, agree, strongly agree.

Nominal, Ordinal, and/or Binary Categorical data classified as Nominal, Ordinal, and/or Binary Categorical data Not binaryBinary Ordinal data Nominal data BinaryNot binary

Nominal Data A type of categorical data in which objects fall into unordered categories.

Examples: Nominal Data Type of Bicycle Mountain bike, road bike, chopper, folding,BMX. Ethnicity White British, Afro-Caribbean, Asian, Chinese, other, etc. (note problems with these categories). Smoking status smoker, non-smoker

Ordinal Data A type of categorical data in which order is important. Degree of illness- none, mild, moderate, acute, chronic. Opinion of students about stats classes- Very unhappy, unhappy, neutral, happy, ecstatic!

Binary Data A type of categorical data in which there are only two categories. Binary data can either be nominal or ordinal. Smoking status- smoker, non-smoker Attendance- present, absent Class of mark- pass, fail. Status of student- undergraduate, postgraduate.

Quantitative Data The objects being studied are ‘measured’ based on some quantity trait. The resulting data are set of numbers.

Examples: Quantitative Data Pulse rate Height Age Exam marks Size of bicycle frame Time to complete a statistics test Number of cigarettes smoked

Discrete or Continuous’ Quantitative data can be classified as ‘ Discrete or Continuous’ Quantity data Continuous Discrete

Discrete Data Only certain values are possible (there are gaps between the possible values). Implies counting. Continuous Data Is something that can be measured.

Discrete data -- Gaps between possible values- count Continuous data -- Theoretically, no gaps between possible values- measure

Examples: Discrete Data Number of children in a family Number of students passing a stats exam Number of crimes reported to the police Number of bicycles sold in a day. Generally, discrete data are counts. We would not expect to find 2.2 children in a family or 88.5 students passing an exam or crimes being reported to the police or half a bicycle being sold in one day.

Examples: Continuous data Size of bicycle frame Height Time to run 500 metres Age Time in a race: you could even measure it to fractions of a second, A dog's weight, The length of a leaf,

Variables Category Quantity Nominal Ordinal Discrete (counting) Continuous (measuring) Ordered categories Ranks. Relationships between Variables. (Source. Rowntree 2000: 33)

Why is this Important? The type of data collected in a study determine the type of statistical analysis used.

Quantitative or Categorical? Who is your favorite pro basketball player? Kobe, Lebron, Durant, Other

Quantitative or Categorical? What is your approximate height?

Quantitative or Categorical? How many times do you eat candy per week?

Quantitative or Categorical? Are you male or female?

Quantitative or Categorical? How many siblings do you have?

Quantitative or Categorical? What is your ethnicity?

Quantitative or Categorical? How many times have you been to lock out?

Quantitative or Categorical? What type of job do you want in the future?

Quantitative or Categorical? What size shoe do you wear?

Quantitative or Categorical? Do you plan on going to college?

Quantitative or Categorical? How old are you?

Quantitative or Categorical? Do you have a job?

Quantitative or Categorical? If you have a job where do you work?

How tall are you?

When presenting and analyzing data, there are many different ways to do that. You must pick the best one for the data.

Bar Graphs and Pie Charts Two-Way Tables and Marginal Distribution Relationships between Categorical Variables Conditional Distributions Dotplots, Describing Shape, Comparing Distributions, Stem plots, Histograms

Class Data Update