Data - facts and statistics collected together for reference or analysis.


bullet 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
snickers
Quantitative Data:
• weight 1.83 ounces
• 280 calories
• length 10 cm
• width 3 cm
• height 1.8 cm
Example 2:
Spanish
Club
spain
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

cockerpuppy
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
snickers
Qualitative Data:
• dark chocolate
• contains peanuts
• caramel smell
• brown wrapper
• nougat center
Example 2:
Spanish
Club
spain
Qualitative Data:
• charity work
• friendly atmosphere
• vocal concerts
• produce a Spanish Play
• enjoy Spanish food
Example 3:

Cocker
Spaniel
Puppy

cockerpuppy
Qualitative Data:
• color black
• trusting
• fluffy
• baby smell
• likes to be held

 

bullet 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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