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Data Types in Statistics: Categorical, numerical, and Ordinal

Data plays an important role in statistics and has become increasingly critical in recent years. This PowerPoint is designed to introduce readers to the importance of data in statistics and help them understand the different types of data used in tactics and how to measure them correctly.

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Data Types in Statistics: Categorical, numerical, and Ordinal

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  1. DATA TYPES IN STATISTICS: CATEGORICAL, NUMERICAL, AND ORDINAL WWW.ACADEMICASSIGNMENTS.CO.UK

  2. PURPOSE OF THE PRESENTATION Data plays an important role in statistics and has become increasingly critical in recent years. This PowerPoint is designed to introduce readers to the importance of data in statistics and help them understand the different types of data used in tactics and how to measure them correctly.

  3. RELEVANCE OF DATA TYPES IN STATISTICS Data in the statistics is the outcome that occurs while conducting research. Data plays a vital role in all theories and measurements. Different types of data in statistics are used for the analysis process. Data types are important concepts in statistics as they allow us to apply statistical measures to data and make inferences about it. Because particular verifiable measures can only be applied to specific forms of data, understanding the various data types is crucial.

  4. Categorical data Ordinal Data • Nominal • ordinal • Discrete Data • Continious Data Numerical Data • OVERVIEW OF THE TYPES OF DATA IN STATISTICS

  5. CATEGORICAL DATA

  6. Categorical data are information split into categories, these are information. The data is called categorical because the information is categorized based on available biodata such as race, age, gender and so on. This data include numerical values that only represent the number of items in each group. Categorical data for instance is used for different purposes Education level - job, training etc. Gender- for collection of biodata

  7. TYPES OF CATEGORICAL DATA: NOMINAL AND ORDINAL ORDINAL DATA NOMINAL DATA Nominal data is the simplest form of a scale of values. This data cannot be set or measured. The grouping method is used to analyze nominal data, which can be presented through the use of a pie chart. Ordinal data is a type of qualitative data in which factors are grouped into hierarchical scale ordinal data for tracking customer input, fulfilment, wealth, and so on. VISION

  8. HOW TO ANALYZE CATEGORICAL DATA

  9. NUMERICAL DATA

  10. Numerical data are widely employed data in statistical analysis and research. Furthermore, numerical data can be visualized using a variety of representations It is also known as quantitative data which is collected in form of numbers and can perform mathematical operations.

  11. TYPES OF NUMERICAL DATA • Discrete data whose values can be organized into a list. Discrete data often represents singular, past occurrences. When evaluating discrete data, you may look at specific figures such as units sold. • There are many examples of continuous data, such as height, weight, temperature, and length. There will be some changes in continuous data over time

  12. HOW TO ANALYZE NUMERICAL DATA

  13. ORDINAL DATA

  14. Ordinal data is commonly used in social sciences and surveys. Even though the underlying variable is complex, these variables are reasonably easy for respondents to select. Ordinal data are often utilized to obtain demographic information. This is especially common in industries such as finance, marketing, and insurance. Examples of ordinal data: Ordinal data is distinguished by the fact that it rates observations. For example, you may be aware that a high-income someone makes more than a lower-income individual, but you are unaware of the additional money they earn. You must consider factors such as expertise, degree of education, and work background while ranking certain rankings.

  15. HOW TO ANALYZE ORDINAL DATA Descriptive analytics are used in ordinal data analysis Ordinal data is usually collected through polls or questionnaires. The Likert Scale is used in this popular form of an ordinal question. A descriptive measurement is used to characterize a database set.

  16. DIFFERNCE BETWEEN CATEGORICAL, NUMERICAL AND ORDINAL DATA CATEGORICAL NUMERICAL ORDINAL A categorical variable with no inherent ordering of the categories. This includes lengthy surveys meant to gather personal information. A ordinal variable is analogous to an categorical variable. The distinction between the two is that the categories are clearly ordered. The numerical variables are evenly spaced. The survey engagement is simple and brief. This approach is used to compute statistics.

  17. WHEN TO USE EACH TYPES OF DATA Categorical data- When a study involves respondents' personal data, viewpoints, and experiences, categorical data is used. In business research, this categorical data is frequently used. Numerical data-The potential performance of arithmetic operations is represented by numerical data. Ordinal data – it is commonly utilized for this due to its ease of categorization and collation. The findings of this analysis are utilized to make conclusions about the respondents in relation to particular variables.

  18. CONCLUSION Different data types in statistics play important roles in helping academic researchers develop strategies. Categorical data provide information about the presence or absence of a particular characteristic, while numerical data provide quantitative information about a phenomenon. Ordinal data provide information about the order in which things occur. Each type of data has its strengths and weaknesses, and researchers need to understand the different types of data to make the most use of them.

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