Categorizing Data MathBitsNotebook.com Terms of Use   Contact Person: Donna Roberts
 Data - facts and statistics collected together for reference or analysis.

 Types of Data:
Data can appear in several forms:
• Data values can be numbers, referred to as quantitative data.
• Data values can be names or labels, referred to as qualitative data.
• Data values can be numbers which act as names instead of numbers (such as phone numbers with dashes: 300-453-1111), making them qualitative data.

Data values, of any kind, without their context are useless. A list of numbers
is of little importance if it is not known to what the numbers apply.
Quantitative Data
• Deals with numbers.
• Also referred to as
Numerical Data.
• Data which can be measured.

• Height, weight, area, volume, length, time, temperature, speed, cost, etc.
Quantitative → Quantity
 Example 1: Candy Bar
Quantitative Data:
 • weight 1.83 ounces • 280 calories • length 10 cm • width 3 cm • height 1.8 cm
 Example 2: Spanish Club
Quantitative Data:
 • 38 students • 3 field trips per year • average GPA 3.5 • 20 girls, 18 boys • 3 foreign exchange students
 Example 3: Cocker Spaniel Puppy
Quantitative Data:
 • adult weight 28 pounds • life span 15 years • height 15 inches • hip dysplasia ranking 115 *good • shelter price \$200
Qualitative Data
• Deals with names, labels, descriptions.
• Also referred to as
Categorical Data.
• Data which can not measured.
• Eye color, smells, car models, textures, tastes, favorites, candy bars, etc.
Qualitative → Quality
 Example 1: Candy Bar
Qualitative Data:
 • dark chocolate • contains peanuts • caramel smell • brown wrapper • nougat center
 Example 2: Spanish Club
Qualitative Data:
 • charity work • friendly atmosphere • vocal concerts • produce a Spanish Play • enjoy Spanish food
 Example 3: Cocker Spaniel Puppy
Qualitative Data:
 • color black • trusting • fluffy • baby smell • likes to be held

 Number of Variables in Data:

Univariate data means "one variable" (one type of data).
Bivariate data means "two variables" (two types of data).

 Univariate Data • Deals with one variable. • Major purpose is to describe. • No relationships or causes. Statistical Analysis: • measures of central tendency - mean, mode, median • outliers and interquartile range • range, maximum, minimum, variance, quartiles, mean absolute deviation, standard deviation • shape, center, spread or distributions Displays: • Dot Plots • Histograms • Box Plots • Quartiles • MAD, Standard Deviation Example: How many students in the freshman class own a skateboard?
 Bivariate Data • Deals with two variables. • Major purpose is to explain. • Relationships and causes. Statistical Analysis: • correlations • comparison, causes, relationships, explanations • analysis of 2 variables simultaneously • tables showing one variable depending upon the other variable • independent and dependent variables Displays: • Two-Way Frequency Tables • Scatter Plots • Line of Best Fit • Linear/Quadratic Regressions • Residuals Example: Is there a relationship between the number of skateboards a freshman owns and his/her final test score in Algebra 1?

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