Categorical Variable Categorical. Categorical variables take on values that are names or labels. The color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier) would be examples of categorical variables. Quantitative. Quantitative variables are numerical. They represent a measurable quantity.

Based on these four sources, here are 12 common dreams and interpretations. Falling. Loewenberg calls this dream a “red flag from your subconscious.” ... Teeth falling out. The experts greatly disagree on this one. ... Showing up to work or school naked. ... Test-taking. ... Dying. ... Meeting a celebrity. ... Being chased. ... Partner is cheating.

Geological disasters Avalanches and landslides. Earthquakes. Sinkholes. Volcanic eruptions. Floods. Limnic eruptions. Tsunami. Blizzards.

Each can be quite powerful and cause severe damage to the environment and the people who live there. Hurricane, Typhoons & Cyclones. ... Earthquakes. ... Hurricanes, Typhoons, and Cyclones. ... Tsunamis. ... Floods. ... Mudslides. ... Avalanches.

Man-made disasters are the consequence of technological or human hazards. Examples include stampedes, fires, transport accidents, industrial accidents, oil spills and nuclear explosions/radiation.

Disasters related to extreme weather events (floods, cyclones, tornadoes, blizzards, droughts) occur regularly. Events related to extremes of the the earth s geology (earthquakes, volc anic eruptions) occur less frequently, but result in major consequences when they happen. Tsunamis often result from earthquakes.

Man made disasters. 1. Man-made disasters are the consequence of technological or human hazards. Examples include stampedes, fires, transport accidents, industrial accidents, oil spills and nuclear explosions/radiation.

Some Examples of Biological Hazards are: Mold and Fungi. Blood and Body Fluids. Sewage. Airborne pathogens such as the common cold. Stinging insects. Harmful plants. Animal and Bird Droppings.

Types of Chemical Hazards Hazard Types Examples Corrosiveacetic acid, sodium hydroxide, photographic fixer Reactive Oxidizers: nitric acid Organic Peroxides: benzoyl peroxide, methyl ethyl ketone peroxide Water Reactive: sodium metal, sodium borohydride Air Reactive: silane, t-butyl lithium Explosive: TNT, picric acid


Variables can be classified as categorical (aka, qualitative) or quantitative (aka, numerical). Categorical. Categorical variables take on values that are names or labels. The color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier) would be examples of categorical variables.

Examples of categorical variables are race, sex, age group, and educational level. While the latter two variables may also be considered in a numerical manner by using exact values for age and highest grade completed, it is often more informative to categorize such variables into a relatively small number of groups.

Categorical Data. Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.

When working with statistics, it's important to recognize the different types of data: numerical (discrete and continuous), categorical, and ordinal. Data are the actual pieces of information that you collect through your study. ... Most data fall into one of two groups: numerical or categorical. Numerical data.

In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.

The data fall into categories, but the numbers placed on the categories have meaning. For example, rating a restaurant on a scale from 0 (lowest) to 4 (highest) stars gives ordinal data. Ordinal data are often treated as categorical, where the groups are ordered when graphs and charts are made.

A categorical variable (sometimes called a nominal variable) is one that has two or more categories, but there is no intrinsic ordering to the categories. For example, gender is a categorical variable having two categories (male and female) and there is no intrinsic ordering to the categories.