

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 (numerical data).
•
Data values can be names or labels, referred to as qualitative data (categorical data).
• Data values can be numbers which act as "names" instead of numbers (such as phone numbers with dashes: 3004531111), 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 (numerical) 
• 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

Quantitative Data:
• weight 1.83 ounces
• 280 calories
• length 10 cm
• width 3 cm
• height 1.8 cm 

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 (categorical) 
• 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 
Qualitative Data:
• dark chocolate
• contains peanuts
• caramel smell
• brown wrapper
• nougat center 

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:
• TwoWay Frequency Tables
• Scatter Plots
• Line of Best Fit
• Linear/Quadratic Regression Models
• 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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