Statistics for Data Science, Schemes and Mind Maps of Data Warehousing

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UNIT - II Statistics for Data Science SCHOOL OF Mechanical ENGINEERING AND TECHNOLOGY Data Science T. Y. BTECH

Basic statistics

• (^) Statistics: “a bunch of mathematics used to summarize, analyze, and interpret a group of numbers or observations.” *It is a tool. *Cannot replace your research design, your research questions, and theory or model you want to use.

Population and sample

• (^) We are much more interested in the population from which the sample was drawn. - Example: 30 GPAs as a representative sample drawn from the population of GPAs of the freshmen currently in attendance at a certain university or the population of freshmen attending colleges similar to a certain university.

Population and sample Population Sample sampling inference

Ungrouped & Grouped Ungrouped data: Data when data presented or observed individually. For example if we observed no. of children in 6 families 2, 4, 6, 4, 6, 4 Grouped data: when we grouped the identical data by frequency. For example above data of children in 6 families can be grouped as: No. of children Families 2 4 6 1 3 2 or alternatively we can make classes: No. of children Frequency 2 - 4 5 - 7 4 2^10

A variable is something that can be changed, such as a characteristic or value. For example age, height, weight, blood pressure etc Variable

Types of measurement 1

• (^) Discrete: Quantitative data are called

discrete if the sample space contains

a finite or countably infinite number

of values.

• How many days did you smoke during the last 7 days

Types of measurement 1

• (^) Continuous: Quantitative data are called continuous if the sample space contains an interval or continuous span of real numbers.
• (^) Weight, height, temperature
• (^) Height: 1.72 meters, 1.7233330 meters

Types of measurement

• (^) Ordinal
  • Ordinal data is a categorical, Statistical data type where the variables have natural, ordered categories and the distances between the categories are not known. These data exist on an ordinal scale ,
• An ordinal scale of measurement represents an ordered series of relationships or rank order. Likert-type scales (such as "On a scale of 1 to 10, with one being no pain and ten being high pain, how much pain are you in today?") represent ordinal data.

Types of measurement

• (^) Interval: A scale that represents quantity and has equal units but for which zero represents simply an additional point of measurement.
• The Fahrenheit scale is a clear example of the interval scale of measurement. Thus, 60 degree Fahrenheit or -10 degrees Fahrenheit represent interval data.

Types of measurement

• (^) Qualitative vs. Quantitative variables
  • Qualitative variables: values are texts (e.g.,Female, male), we also call them string variables.
  • Quantitative variables: are numeric variables.

Data Types For each dimension… Numerical Continuous Discrete Categorical Nominal Ordinal

Basic statistics

• (^) Descriptive statistics:
  • “are procedures used to

summarize, organize, and make

sense of a set of scores or

observations.”

Basic statistics

• (^) Inferential statistics:
  • “are procedures used that allow

researchers to infer or generalize

observations made with samples to

the larger population from which

they were selected.”