(Week1)

1 Statistics

-> Art of learning from data

-> Concerned with collection of data

-> Subsequent Description and Analysis

1.1 Population: Total collection of all the items/elements that we are interested in.

1.2 Sample: Subset of the population used for detailed study.

Example: Suppose we take water bottles of all the boys in a hostel and randomly select 25 bottles. It is concluded that the capacity of 25 water bottles is 1L/bottle. Hence, the Sample consists of 25 bottles and the population consists of all the water bottles from the boy's hostel.

Major Branches of Statistics

Descriptive Statistics: Statistics which is concerned with the description and summarization of the main features of the dataset.

Inferential Statistics: Branch of statistics used to conclude from the data.

2 Data

Facts and figures collected, analyzed, and summarized for presentation and interpretation.

Types of data:

Unstructured Data: Dataset that is not organized in a predefined manner. Unstructured data requires more work to process and understand.

Ex: YouTube comments, Image files etc

"Data scattered with no structure "

Structured Data: Data that has a standardized format for efficient access by software and humans alike. structured data is easy to analyze and understand.

2.1 Variable and cases

Case(observation)/Row: Observation for which data is collected. (For each case same attribute is recorded.)

Variable/Column: Attribute that varies across all units. (The same type of value for each case is recorded.)

For ex.

In the above table, the individual name and age is a case(observation). While all names and ages in a column are Variables.

Classification of data

Categorical Data(Qualitative variable):

When observations can be classified into one of the categories instead of being measured numerically.

(*cannot perform any meaningful mathematical operation*)

For example, we have students' data from different colleges. The gender of students is categorical data because we have M or F categories to select.

Numerical data(Quantitative variable):

Describes the numerical properties of the data. It will always be a numerical value.

(*Mathematical operations can be performed*)

Ex- We have a dataset of students from different branches of a college. The number of students in a branch is the numerical property of data as the number of students is 60(assumed).

Time series Data:

Data recorded over a period of time is called time series data.

Ex- Temperature of place in 10 days

Cross-Sectional data:

Data recorded at the same time in different cases is called crossectional data.

Ex- Temperature of Dehradun, Chennai and Kanpur on the same day

Scales of measurement of data

Nominal Scale: Measurement scale in which number serves as "tags" or "labels" only, to identify an object.

Ex- Discomfort of disease

1- mild

2-Moderate

3-Severe

Here 1=mild, 2=moderate and 3= severe in which 1,2,3 does not have numerical value rather it is used as labels for mild, moderate and severe.Here order or rank doesn't matter.

(*No ordering in the variables*)

Ordinal Scale: Data that exhibits properties of nominal data and the rank or order is meaningful is called ordinal scale.

(Distance between data is unknown)

Ex- Service rating by the customers

1-poor

2-good

3-excellent

here rank of rating matters therefore the scale is ordinal.

Interval Scale:

Similar to the ordinal scale and the distance(interval) between the values is expressed in terms of a fixed unit measure, the scale is called the interval scale.

Ex- Consider the temperature in the refrigerator is 6°C and the temperature outside the refrigerator is 35°C. The difference is 29°C.

Ratio Scale:

Similar to the interval scale and the ratio of the two values is meaningful, the scale is called the ratio scale.

Ex- For the marks of two students ratio is meaningful.

Thank you for reading my blog. (If any mistakes or corrections are required do comment below.)