Name three different data series and give examples of them
Answers
Answer:
short term data
long term data
useless data
Step-by-step explanation:
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Answer:
Data Sets Meaning
A data set is an ordered collection of data. While handling the data, the data set can be a bunch of tables, schema and other objects. The data are essentially organized to a certain model that helps to process the needed information. The set of data is any permanently saved collection of information that usually contains either case-level, gathered data, or statistical guidance level data.
Types of Data Sets
In Statistics, we have different types of data sets available for different types of information. They are:
Numerical data sets
Bivariate data sets
Multivariate data sets
Categorical data sets
Correlation data sets
Step-by-step explanation:
Numerical Data Sets
The numerical data set is a data set, where the data are expressed in numbers rather than natural language. The numerical data is sometimes called quantitative data. The set of all the quantitative data/numerical data is called the numerical data set. The numerical data is always in the numbers form, such that we can perform arithmetic operations on it.
Weight and height of a person
The count of RBC in a medical report
Number of pages present in a book.
Bivariate Data Sets
A data set that has two variables is called a Bivariate data set. It deals with the relationship between the two variables. Bivariate dataset usually contains two types of related data.
Example: To find the percentage score and age of the students in a class. Score and age can be considered as two variables
The sales of ice cream versus the temperature on that day. Here the two variables used are ice cream and temperature.
(Note: In case, if you have one set of data alone say, temperature, then it is called the univariate dataset) .
Multivariate Data Sets
A data set with multiple variables. When the dataset contains three or more than three data types (variables), then the data set is called a multivariate dataset. In other words, the multivariate dataset consists of individual measurements that are acquired as a function of three or more than three variables.
Example: If we have to measure the length, width, height, volume of a rectangular box, we have to use multiple variables to distinguish between those entities.
Categorical Data Sets
Categorical data sets represent features or characteristics of a person or an object. The categorical dataset consists of a categorical variable also called the qualitative variable, that can take exactly two values. Hence, it is termed as a dichotomous variable. Categorical data/variables with more than two possible values are called polytomous variables. The qualitative/categorical variables are often assumed to be polytomous variable unless otherwise specified.
Example:
A person’s gender (male or female)
Marital status (married/unmarried)
Correlation Data Sets
The set of values that demonstrate some relationship with each other indicates correlation data sets. Here the values are found to be dependent on each other.
Generally, correlation is defined as a statistical relationship between two entities/variables. In some scenarios, you might have to predict the correlation between the things. It is essential to understand how correlation works. The correlation is classified into three types. They are:
Positive correlation – Two variables move in the same direction (Either both are up or both or down)
Negative correlation – Two variables move in opposite directions. (One variable is up and another variable is down and vice versa)
No or zero correlation – No relationship between two variables.
Example: A tall person is considered to be heavier than a short person. So here the weight and height variables are dependent on each other.
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