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Page 1 CLASSIFICATION CLASSIFICATION AND AND TABULATION OF DATA TABULATION OF DATA Dr. Dr. Bijaya Bhusan Nanda, Bijaya Bhusan Nanda, M. Sc (Gold Medalist) Ph. D. (Stat.) M. Sc (Gold Medalist) Ph. D. (Stat.) Topper Orissa Statistics & Economics Topper Orissa Statistics & Economics Services, 1988 Services, 1988 bijayabnanda@yahoo.com bijayabnanda@yahoo.com Lecture Series on Lecture Series on Biostatistics Biostatistics No. Bio-Stat_3 No. Bio-Stat_3 Date – 03.08.2008 Date – 03.08.2008 Page 2 CLASSIFICATION CLASSIFICATION AND AND TABULATION OF DATA TABULATION OF DATA Dr. Dr. Bijaya Bhusan Nanda, Bijaya Bhusan Nanda, M. Sc (Gold Medalist) Ph. D. (Stat.) M. Sc (Gold Medalist) Ph. D. (Stat.) Topper Orissa Statistics & Economics Topper Orissa Statistics & Economics Services, 1988 Services, 1988 bijayabnanda@yahoo.com bijayabnanda@yahoo.com Lecture Series on Lecture Series on Biostatistics Biostatistics No. Bio-Stat_3 No. Bio-Stat_3 Date – 03.08.2008 Date – 03.08.2008 LEARNING OBJECTIVES LEARNING OBJECTIVES ? The trainees will be able to do The trainees will be able to do meaningful classification of large meaningful classification of large mass of data and interpret the same. mass of data and interpret the same. ? They will be able to construct They will be able to construct frequency distribution table and frequency distribution table and interpret the same. interpret the same. ? They will be able to describe different They will be able to describe different parts of tables and types of table parts of tables and types of table Page 3 CLASSIFICATION CLASSIFICATION AND AND TABULATION OF DATA TABULATION OF DATA Dr. Dr. Bijaya Bhusan Nanda, Bijaya Bhusan Nanda, M. Sc (Gold Medalist) Ph. D. (Stat.) M. Sc (Gold Medalist) Ph. D. (Stat.) Topper Orissa Statistics & Economics Topper Orissa Statistics & Economics Services, 1988 Services, 1988 bijayabnanda@yahoo.com bijayabnanda@yahoo.com Lecture Series on Lecture Series on Biostatistics Biostatistics No. Bio-Stat_3 No. Bio-Stat_3 Date – 03.08.2008 Date – 03.08.2008 LEARNING OBJECTIVES LEARNING OBJECTIVES ? The trainees will be able to do The trainees will be able to do meaningful classification of large meaningful classification of large mass of data and interpret the same. mass of data and interpret the same. ? They will be able to construct They will be able to construct frequency distribution table and frequency distribution table and interpret the same. interpret the same. ? They will be able to describe different They will be able to describe different parts of tables and types of table parts of tables and types of table ? Concept of Variable Concept of Variable ? Ordered array Ordered array ? What is data Classification ? What is data Classification ? ? Objectives of Classification Objectives of Classification ? Frequency distributions Frequency distributions ? Variables and attributes Variables and attributes ? Tabulation of data Tabulation of data ? Parts of a table Parts of a table ? Type of tables Type of tables C O N T E N T C O N T E N T Page 4 CLASSIFICATION CLASSIFICATION AND AND TABULATION OF DATA TABULATION OF DATA Dr. Dr. Bijaya Bhusan Nanda, Bijaya Bhusan Nanda, M. Sc (Gold Medalist) Ph. D. (Stat.) M. Sc (Gold Medalist) Ph. D. (Stat.) Topper Orissa Statistics & Economics Topper Orissa Statistics & Economics Services, 1988 Services, 1988 bijayabnanda@yahoo.com bijayabnanda@yahoo.com Lecture Series on Lecture Series on Biostatistics Biostatistics No. Bio-Stat_3 No. Bio-Stat_3 Date – 03.08.2008 Date – 03.08.2008 LEARNING OBJECTIVES LEARNING OBJECTIVES ? The trainees will be able to do The trainees will be able to do meaningful classification of large meaningful classification of large mass of data and interpret the same. mass of data and interpret the same. ? They will be able to construct They will be able to construct frequency distribution table and frequency distribution table and interpret the same. interpret the same. ? They will be able to describe different They will be able to describe different parts of tables and types of table parts of tables and types of table ? Concept of Variable Concept of Variable ? Ordered array Ordered array ? What is data Classification ? What is data Classification ? ? Objectives of Classification Objectives of Classification ? Frequency distributions Frequency distributions ? Variables and attributes Variables and attributes ? Tabulation of data Tabulation of data ? Parts of a table Parts of a table ? Type of tables Type of tables C O N T E N T C O N T E N T Concept of Variable Concept of Variable ? Variable Variable A characteristic which takes on different A characteristic which takes on different values in different persons, place or values in different persons, place or things. things. Example Example : Diastolic/Systolic blood : Diastolic/Systolic blood pressure, heart rate, the heights of adult pressure, heart rate, the heights of adult males, the weights of preschool children males, the weights of preschool children and the ages of patients seen in a dental and the ages of patients seen in a dental clinic clinic Page 5 CLASSIFICATION CLASSIFICATION AND AND TABULATION OF DATA TABULATION OF DATA Dr. Dr. Bijaya Bhusan Nanda, Bijaya Bhusan Nanda, M. Sc (Gold Medalist) Ph. D. (Stat.) M. Sc (Gold Medalist) Ph. D. (Stat.) Topper Orissa Statistics & Economics Topper Orissa Statistics & Economics Services, 1988 Services, 1988 bijayabnanda@yahoo.com bijayabnanda@yahoo.com Lecture Series on Lecture Series on Biostatistics Biostatistics No. Bio-Stat_3 No. Bio-Stat_3 Date – 03.08.2008 Date – 03.08.2008 LEARNING OBJECTIVES LEARNING OBJECTIVES ? The trainees will be able to do The trainees will be able to do meaningful classification of large meaningful classification of large mass of data and interpret the same. mass of data and interpret the same. ? They will be able to construct They will be able to construct frequency distribution table and frequency distribution table and interpret the same. interpret the same. ? They will be able to describe different They will be able to describe different parts of tables and types of table parts of tables and types of table ? Concept of Variable Concept of Variable ? Ordered array Ordered array ? What is data Classification ? What is data Classification ? ? Objectives of Classification Objectives of Classification ? Frequency distributions Frequency distributions ? Variables and attributes Variables and attributes ? Tabulation of data Tabulation of data ? Parts of a table Parts of a table ? Type of tables Type of tables C O N T E N T C O N T E N T Concept of Variable Concept of Variable ? Variable Variable A characteristic which takes on different A characteristic which takes on different values in different persons, place or values in different persons, place or things. things. Example Example : Diastolic/Systolic blood : Diastolic/Systolic blood pressure, heart rate, the heights of adult pressure, heart rate, the heights of adult males, the weights of preschool children males, the weights of preschool children and the ages of patients seen in a dental and the ages of patients seen in a dental clinic clinic ? Quantitative Variable:- Quantitative Variable:- One that can be One that can be measured and expressed numerically. The measured and expressed numerically. The measurements convey information regarding measurements convey information regarding amount. amount. Example Example : : Diastolic/Systolic blood pressure, heart Diastolic/Systolic blood pressure, heart rate, the heights of adult males, the weights of rate, the heights of adult males, the weights of preschool children and the ages of patients seen preschool children and the ages of patients seen in a dental clinic in a dental clinic ? Qualitative Variable:- Qualitative Variable:- The characteristics that The characteristics that can’t be measured quantitatively but can be can’t be measured quantitatively but can be categorized. The measurement convey categorized. The measurement convey information regarding the attribute. The information regarding the attribute. The measurement in real sense can’t be achieved but measurement in real sense can’t be achieved but persons, places or things belonging to different persons, places or things belonging to different categories can be counted. categories can be counted. Example: Example: sex of a patient, colure and odour of sex of a patient, colure and odour of stool and urine samples etc. stool and urine samples etc.Read More
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FAQs on PPT - Classification and Tabulation, - Business Mathematics and Statistics - B Com
| 1. What is the importance of classification and tabulation in business? |  |
Ans. Classification and tabulation play a crucial role in business by organizing and presenting data in a structured manner. Classification helps in grouping similar data together, making it easier to analyze trends and patterns. Tabulation, on the other hand, presents the classified data in a systematic table format, allowing for quick comparisons and summaries. This helps businesses make informed decisions, identify market opportunities, and evaluate the performance of different variables.
| 2. How does classification differ from tabulation? | |
Ans. Classification and tabulation are two distinct processes in data management. Classification involves sorting and grouping data into categories based on their similarities or common characteristics. It helps in organizing data for analysis and interpretation. On the other hand, tabulation refers to arranging the classified data in a systematic table format, usually using rows and columns. Tabulation makes it easier to compare and summarize data, enabling businesses to extract useful insights and make informed decisions.
| 3. What are the steps involved in classification? | |
Ans. The process of classification involves several steps:
1. Identification of variables: Determine the variables or factors that need to be classified based on the research objective.
2. Defining categories: Establish distinct categories or groups based on the characteristics or attributes of the variables.
3. Sorting data: Organize the data into the predefined categories according to the identified variables.
4. Assigning codes: Assign unique codes or labels to each category for easy identification and analysis.
5. Checking for accuracy: Verify the correctness and completeness of the classified data to ensure reliable results.
6. Documentation: Document the classification process, including the variables, categories, and codes used, for future reference.
| 4. How can classification and tabulation benefit market research? | |
Ans. Classification and tabulation are essential tools in market research for analyzing and interpreting data. By classifying data into relevant categories, market researchers can identify consumer preferences, market segments, and target demographics. Tabulation allows for easy comparison and summarization, making it possible to derive meaningful insights from the data. This information can help businesses develop effective marketing strategies, target specific customer groups, and stay ahead of competitors in the market.
| 5. What are the limitations of classification and tabulation? | |
Ans. While classification and tabulation are valuable techniques, they do have some limitations. These include:
1. Subjectivity: The process of classification involves subjective decisions in defining categories and assigning data to them. This subjectivity can introduce bias and affect the accuracy of the results.
2. Loss of detail: Classification and tabulation often involve summarizing and grouping data, leading to a loss of detailed information. This may limit the depth of analysis and overlook specific patterns or outliers.
3. Changing variables: In a dynamic business environment, variables and their categories may change over time. This can make the classification and tabulation outdated or less relevant, requiring regular updates.
4. Limited analysis: Classification and tabulation provide a structured overview of the data but may not facilitate complex statistical analysis or modeling. Additional techniques may be required for in-depth analysis and forecasting.
5. Data quality: The reliability and accuracy of the classification and tabulation results depend on the quality of the underlying data. Incomplete or incorrect data can lead to misleading conclusions and decisions.
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