Tables Do Not Include Numerical Data

When working with statistical data, researchers demand to become acquainted with the data types used—categorical
and numerical information. The different data types are used in separate cases and require different statistical and visualization techniques.

Therefore, researchers need to understand the dissimilar data types and their analysis. This knowledge is what is used during the research procedure.

Numerical information as a case report is categorized into discrete and continuous data where continuous data are further grouped into interval and ratio data. These data types are significantly used for statistical assay or inquiry purposes.

What is Numerical Data

Numerical data is a data type expressed in numbers, rather than natural language description. Sometimes called quantitative information, numerical information is ever nerveless in number form. Numerical data differentiates itself from other number form information types with its power to carry out arithmetic operations with these numbers.

For example, numerical data of the number of male students and female students in a form may be taken, then added together to get the total number of students in the class. This characteristic is ane of the major ways of identifying numerical data.

What are the Types of Numerical Information?

Numerical data can accept 2 different forms, namely; discrete information, which represents countable items and continuous data, which represents data measurement. The continuous type of numerical data is further sub-divided into interval and ratio data, which is known to be used for measuring items.

• Discrete Data

Discrete Data
represents countable items and
can take both numerical and categorical forms, depending on usage. It takes on values that tin exist grouped into a listing, where the listing may either be finite or infinite.
Whether finite or infinite, detached data take on counting numbers like ane to x or 1 to infinity, with these groups of numbers existence countably finite and countably infinite respectively.

A more practical instance of discrete data will exist counting the cups of h2o required to empty a bucket and counting the cups of water required to empty an ocean—the former is finite countable while the latter is infinite countable.

• Continuous Data:
  

This is a
type of numerical data which represents measurements—their values are described as intervals on a real number line, rather than take counting numbers. For case, the Cumulative Grade Betoken Average (CGPA) in a v point grading system defines excellent students as those whose CGPA falls under four.50 – 5.00, second class upper equally 3.50 – 4.49, second class lower as 2.50 – 3.49, third class as 1.5 – 2.49, pass equally i.00 – 1.49 and neglect equally 0.00 – 0.99..

A student may score a bespeak iv.495, 2.125, three.5 or any possible number from 0 to v. In this case, the continuous data is regarded every bit beingness uncountably finite.

Continuous information may be subdivided into two types, namely; Interval & Ratio Information.

• Interval Data

This is a data type measured forth a scale, in which each betoken is placed at an equal distance from one some other. Interval data takes numerical values that can only take the addition and subtraction operations.

For example, the temperature of a body measured in degrees Celsius or degrees Fahrenheit is regarded as interval data. This temperature does not accept a zero point.

• Ratio Data

Ratio information is a continuous information type like to interval data but has a nil bespeak. In other words, ratio data is interval data with zero points.For ratio information, the temperature may not only exist measured in degrees Celsius and degrees Fahrenheit, but as well in Kelvin. The presence of zero-point accommodates the measurement of 0 Kelvin.

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General Characteristics/Features of Numerical Data

• Categories:
  There are two main categories of numerical data, namely; discrete and continuous data. Continuous data is and then further cleaved down into interval and ratio data.
• Quantitativeness:
  Numerical information is sometimes called quantitative data due to its quantitative nature. Unlike categorical data which takes quantitative values with qualitative characteristics, numerical information exhibits quantitative features. .
• Arithmetic Operation:
  One can perform arithmetic operations like addition and subtraction on numerical data. Truthful to its quantitative character, nearly all statistical assay is applicable when analyzing numerical data.
• Estimation & Enumeration:
  Numerical data tin can both be estimated and enumerated. In a case whereby the numerical information is precise, it may be enumerated.However, if it is not precise, the data is estimated. When computing the CGPA of a student, for instance, a four.495623 CGPA is rounded upwards to 4.fifty.
• Interval Difference
  :
  The deviation betwixt each interval on a numerical data calibration are equal. For case, the difference betwixt 5 minutes and 10 minutes on a wall clock is the same as the difference between 10 and fifteen minutes.
• Analysis:
  Numerical information is analyzed using descriptive and inferential statistical methods, depending on the aim of the research. Some of the descriptive-analytical methods include; mean, median, variance, etc. Inferential statistical methods like TURF analysis, tendency analysis, SWOT analysis, etc. are also used for numerical information analysis.
• Data Visualisation:
  Numerical information may exist visualized in unlike means depending on the type of data being investigated. Some of the data visualization techniques adopted by numerical information include; scatter plot, dot plot, stacked dot plot, histograms, etc.

What are the Examples of Numerical Information?

Numerical information examples which are commonly expressed in numbers include; census data, temperature, historic period, mark grading, annual income, time, height, IQ, CGPA, etc. These numerical examples, either in countable numbers every bit in detached data or measurement class like continuous information call all exist labeled as an example of numerical information

• Census:
  The Federal Regime periodically needs to conduct the census of a country to know the state’south population and demographics of this population. A caput-to-caput count of the country’s residents is washed using numerical data.

Knowing the Demography of a country assists the Government in making proper economical decisions. It is an example of countably finite detached information.

• Temperature:
  The temperature of a given body or identify is measured using numerical data. The torso temperature of a body, given to be 37 degrees Celsius is an example of continuous data.

This data blazon also puts into consideration the unit. Interval data, for example, can simply measure in degrees Celsius and Fahrenheit, while ratio data tin also measure in Kelvin.

• Age:
  The age of an private is counted using numerical data. Information technology is classified as quantitative because information technology can take up multiple numerical values.

Although numbers are infinite in the real sense, the number of years people spend in life is finite, making it countably finite discrete data. For instance, a person who is 20 years old today may end high school at 16, 4 years agone.

• Mark Grading:
  Numerical data is used when grading examination scores. Most times, these marks are uncountably finite and fall nether continuous data.

When applying for admission in a schoolhouse, for instance, your O level results may add together up to your score. Therefore, the admission lath may ask you to input your grades—A is v points, B is 4 points, C is 3 points, D is 2 points and E is 1 point. All these points are added together to make your total admission score.

• Annual income:
  The annual income of an individual or household is an instance of numerical data, used past businesses to know the purchasing power of their customers or each household in a community. This knowledge influences the price of their products.
  The almanac income of an individual or household is a countably finite discrete information.
• Time:
  The amount of fourth dimension information technology took a runner to run a race, for instance, is numerical information. It doesn’t matter whether information technology is beingness measured in hours, seconds, or minutes, it always takes a numeric value.
  Time is an instance of continuous data. It is regarded as interval data if measured on a 12-hour clock.
• Height:
  A person’s meridian could be any value (within the range of human heights), not just certain stock-still heights. This meridian takes a numeric value that varies in person and tin can increase equally fourth dimension goes on.

The height of a person, measured in centimeters, meters, inches, etc. is continuous data.

• IQ Test Score:
  Most IQ tests charge per unit a person’s IQ in terms of percentage. The percentage of IQ is derived from the participant’due south score in various sub-tests.

This score is non only quantitative but as well has quantitative properties. An IQ test score is an example of uncountably finite categorical information.

• Weight: Weight is a variable element in humans. A person might weigh 50kg while another might counterbalance 80kg.
  Unlike height that may not decrease, weight may increase and decrease in a person.
  

The weight of a person measured in kg is numerical information and may be an indication of fat or slim which is a categorical variable.

• CGPA: This represents a student’south Grade Point Average in his/her studies over a ready flow eastward.yard. i semester. The hateful of the GPA is used to find the CGPA of a student over a longer period e.g. ii sessions. CGPA is an example of interval data.
• The number of children:
  The number of children in a community, for instance, is a superset of the number of children in a dwelling. In other words, the number of children in each abode is what adds up to make the full number of children in counting.

This exhibits the characteristics of numerical information and is a countably finite detached information example.

• 
  The number of students:
  Similarly, the number of students in a class is a superset of the number of males and females in a class. That is, the number of males and females is what adds upward to make the total number of students in a class.

The number of students in a class is also a countably finite discrete data case.

• Results of rolling a dice:
  A die has six faces, with each face representing one of the numbers from 1 to 6. When you roll a dice, yous get two numbers which may add together upwards to one of ii, 3, four, 5, 6, 7, eight, 9, 10, 11, and 12.

Therefore, the results of rolling dice are a countably finite discrete data instance.

• Length:
  Permit us consider the length of a foliage for example, which is similar to the summit in man beings. A leaf’due south length could exist whatsoever value, not just a certain fixed length.

This tiptop takes a numeric value that varies in plants and can increment as the plant grows. The length of a leaf measured in centimeters is continuous data.

Numerical Data Variables

A numerical variable is a data variable that takes on any value within a finite or infinite interval (due east.g. length, examination scores, etc.). the numerical variable tin also be called a continuous variable because information technology exhibits the features of continuous data. Unlike discrete data, continuous information takes on both finite and space values.

In that location are two types of numerical variables, namely; interval and ratio variables.

An interval variable has values with interpretable differences, only no true nothing. A good example is a temperature when measured in degrees Celsius and degrees Fahrenheit.

The interval variables can be added and subtracted, just cannot exist multiplied and divided. The ratio variable, on the other mitt, does all this.

Interval Variable

The Interval variable is an extension of the ordinal variable, with a standardized divergence betwixt
variables in the interval scale. In that location are ii distributions on interval variables, namely; normal distribution and non-normal distribution

Normal Distribution

A real-valued random variable is said to be unremarkably distributed if its distribution is unknown. Nosotros consider two main samples of normal distribution and comport out different tests on them.

Matched Sample

Tests

• Paired t-test: This is used to compare 2 sample population means.
• Repeated measures ANOVA: This compares means across iii or more than variables, based on repeated observations.

Unmatched Sample

Tests

• Unpaired t-exam: This is used to compare two sample population means.
• ANOVA: This compares means across three or more variables, based on a single ascertainment.

Non-Normal Distribution

A real-valued random variable is said to be non-normally distributed if its distribution is known. We consider two main samples of non-normal distribution and carry out different tests on them

Matched Sample

Tests

• Wilcoxon rank-sum test: This is used to compare two groups of matched samples.
• Friedman two-mode ANOVA: This is used to compare the difference in means across 3 or more groups.

Unmatched Sample

Tests

• Wilcoxon rank-sum exam: This test is used when the requirements for the t-examination of 2 unmatched samples are non satisfied.
• Kruskal-Wallis exam: This is used to investigate whether iii or more groups of unmatched samples originate from the same distribution.

Ratio Variable

The ratio variable is an extension of the interval variable, with values with a true cipher, and tin can be added, subtracted, multiplied, or divided. The tests carried out on these variables are like to those of interval variables.

Numerical Data Assay

Numerical data analysis can be interpreted using two main statistical methods of analysis, namely; descriptive statistics and inferential statistics. Numerical analysis in inferential statistics can be interpreted with swot, tendency, and conjoint analysis while descriptive statistics make employ of measures of central tendency,

Descriptive Statistics

Descriptive statistics are used to describe a sample population using data sets collected from that population. Descriptive statistical methods used in analyzing numerical data are; mean, median, style, variance, standard deviation, etc.

Inferential Statistics

Inferential is used to make predictions or inferences on a large population based on the information collected from a sample population. Below are some of the methods used for analyzing numerical data.

• Trend assay:
  Trend analysis is an interval information analysis technique, used to describe trends and insights by capturing survey data over a certain menstruation.
• SWOT assay:
  SWOT is an acronym for Strengths, Weaknesses, Opportunities, and Threats. Strengths and Weaknesses are for internal analysis, while Opportunities and Threats are for external assay of an organization.
• Conjoint analysis:
  This is a market place research analysis technique that investigates how people brand choices.
• TURF analysis: This is an acronym for Total Unduplicated Reach and Frequency analysis, and is used to assess the market potential for a combination of products or services.

Uses of Numerical Data

• Population Prediction

Using Tendency analysis, researchers gather the data of the birth rate in a country for a certain menstruum and use information technology to predict future populations. Predicting a state’s population has a lot of economic importance.

• Marketing & Advertising

Earlier engaging in any marketing or advertising campaign, companies need to first analyze some internal and external factors that may affect the campaign. In well-nigh cases, they use a SWOT analysis.

• Research

Numerical data is very popular among researchers due to its compatibility with about statistical techniques. It helps ease the research process.

• Product Development

During the product development stage, product researchers utilize TURF analysis to investigate whether a new production or service will be well-received in the target market place or not.

• Teaching

Interval data is used in the instruction sector to compute the grading system. When computing the Cumulative Grade Signal Average of a pupil, the examiner uses interval data of the student’s scores in the diverse courses offered.

• Medicine

Doctors use the thermometer to measure out a patient’due south body temperature every bit function of a medical bank check-up. In most cases, body temperature is measured in Celsius, therefore passing as interval data.

Disadvantages of Numerical Data

• Preset answers that do not reverberate how people experience well-nigh a subject.
• “Standard” questions from researchers may lead to structural bias.
• Results are limited.

What is the best tool to collect Numerical Data?

Numerical data is one of the most useful data types in statistical analysis. Formplus provides its users with a repository of great features to go with it.  With Formplus’s web-based data collection tool, y’all have access to features that will assist you in making strategic business organization decisions. This way, you lot tin better business sales, launch better products and serve customers better.

Determination

Numerical information research techniques use inquiry strategies such as experiments and surveys. The findings may be predictive, explanatory, and confirming.

It involves the collection of data which is then subjected to statistical treatment to support or refute a hypothesis. Thus, numerical data collection techniques are used to gather data from different reliable sources, which deal with numbers, statistics, charts, graphs, tables, etc.

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