Data Processing and Analysis in Research Methodology 

SH21PPHARM003 SWATHI BATTA Q: Quantitative data different temperatures and different prices, Discret data votes in elections followers in social media, Nominal data : groups of people hair colour, race Ordinal data: different types of incomes Continuous data : volts of electricity Q2: data with is expressed in numbers is classified according to class intervals Data which is classified according to simple and multiple is called as attributes classification in this classification data is classified according to male and female ratio

nominal data - country,gender,race,hair color etc ordinal data - high school , B.tech , M.tech , less than 50 k , 50 k to 100 k etc Discreet Data - no of customers , no of students , no of items etc . continuous data - height , weight , temperature , length etc Vibhor Mishra

1.Nomial data--Gender , Marital status , very hot ,hot , very cold , cold ,warm are all nomial data when consider indivisually. Ordinal data ----Very hot ,hot, warm, cold ,very cold when placed in order are ordinal data Discrete data ---No of consumers , no of students continuous data --height ,weight , length 2.The difference between classification according to attributes and classification according to class interval lies in its measureability . Qualitative attributes cannot be measured quantitatively or given numerical figures. However, quantitative phenomenon can be measured through some statistical units. BS21PMATH002

Salma Sultana Rahman ET21PCSE001 Ans.1 Nominal data : country, race , mode of transportation, hair colour Ordinal data : income level, education level, satisfaction rating Discrete data : number of children, shoe size, number of students Continuous data : temperature, age, time Ans.2 The difference between classification according to attribute and classification according to class interval is that attribute classification is qualitative and cannot be measured in numbers and class interval classification is quantitative which can be measured through numbers.

sb21phrm001, Vaskar Rajkhowa nominal data - employment status ( employed / unemployed) ordinal data - qualification Discreet Data - no of employees continuous data - height of a tree The difference between classification according to attributes and classification according to class interval is its quantification. Classifying statistical datas on the basis of attribute : Attractiveness of religion Classification according to class interval : No of class in a High School

bs21pphy001(Achyut Sarma Hazarika) Nominal data: is numerical in name only.Example-A person's marital status 1,2,3 or 4.1for single,2 for married,3 for widowed, 4 for divorced. Ordinal data: Examples High School,BS,MS,PhD Less than 50K,50K-100K, Continuous data: Values of height,weight,length. Discrete data: Test questions answered correctly,No of people in a class. Q2. Ans:The method of classifying statistical datas on the basis of attribute.Example: Classification according to religion,cast.etc. Data which is expressed in number is called classification according to class interval.Example Age group between 20-40 years,40-60 years etc.

Name: Bonjit Bondon Buragohain Roll no. BS21PMATH003 Ans. 1= Nominal Data: Preferred mode of public transportation ( Bus/Train). Ordinal Data: Interval scale, Discrete Data: Number of languages an individual speaks, The number of parts damaged during the transportation. Continuous Data: The weight of truck, the height of children. Ans. 2 In classification according to attribute classifying statistical data but in classification according to class interval the observation at arranged systematically into number of groups.

Name: JITEN NARZARI Roll No. SS21PSS001 Ans 1: Nominal data - eg. Marital status (Married/Single/Widowed) Ordinal data: eg. Movie ratings (1-5 star) and Exam rankings (1st, 2nd or 3rd) Discrete data: eg. Number of languages a person speaks Continous data: eg. Weight of the newly launched cars or new born babies. Ans 2: The difference between classification according to attributes and classification according to class interval lies in its measureability or capacity to be measured. Qualitative attributes (eg. woman's attractiveness) cannot be measured quantitatively or given numerical figures. However, quantitative phenomenon (eg. weight/height) can be measured through some statistical units.

DIPANKAR BORAH SB21PHRF001 Examples of Nominal Data : Nationality (Indian, British,Chinese etc) Relationship Status (Married, Single etc ) Blood group ( O positive, O negative, A negative etc) Employment status ( Employed, Unemployed, Retired) Examples of Ordinal Data : Educational Level( HSLC, HS, Graduation, PG) Age( Child, teenager, young adult, middle aged, retired) Income level (10-20k, 20-50k, 50-100k) Likert scales( very poor, poor, average, good, very good) Examples of Discrete Data: The number of parts damaged during transportation. Shoe sizes. The number of questions attempted. Examples of Continuous Data: The amount of time required to travel. The weight of a truck. The amount of rain. Classification of data on the basis of attribute: When facts are grouped according to the qualities (attributes) like religion, literacy, business etc Classification of data on the basis of class interval: The data which is expressed in numbers (quantitative data) is classified according to class intervals.

Pankaj kalita bs21pchem002 Ans1. Nominal data: country, gender, race, hair color etc. of a group of people etc. Ordinal data: socio economic status (“low income”,”middle income”,”high income”), education level (“high school”,”BS”,”MS”,”PhD”), income level (“less than 50K”, “50K-100K”, “over 100K”) etc. Discrete data: Number of students, the number of children, the shoe size etc. Continuous data: height, weight, length, time, temperature, age etc Ans 2. classification of data on the basis of attribute: The method of classifying statistical data on the basis of attribute is said to be classification by attributes or qualitative classification. classification of data according to class interval: Here classification is done according to class intervals, the observations are arranged systematically into a number of groups called classes.