SlideShare a Scribd company logo
2
Most read
3
Most read
11
Most read
Methods of Data Presentation Remark:   One of the most important aspects in any statistical investigation is the manner by which the researcher presents the data. Various Modes of Data Presentation I. Textual II. Tabular III. Graphical Displays
Methods of Data Presentation I. Textual Presentation  – the data are presented in the form of texts, phrases or paragraphs. - common among newspaper reports depicting  specifically the salient or important findings. Example:  The Philippine Stock Exchange composite index lost 7.19 points to 2,099.12 after trading between 2,095.30 and 2,108.47. Volume was 1.29 billion shares worth 903.15 million pesos (16.7 million dollars). The broader all share index gained 5.21 points  to 1,221.34. (From: Freeman dated March 17, 2005)
Methods of Data Presentation II. The Tabular Display  – a  more reliable and  effective way of showing relationships or  comparisons of data through the use of tables.   - the tables must be accompanied by a short  narrative explanation to make  the facts clearer and more understandable.
Methods of Data Presentation Example: The following newspaper report presents a  tabular data presentation . Country Peso United States Japan United Kingdom Hongkong Switzerland Canada Singapore 50. 7890 0.4140 72.5267 6.5116 28.7382 32.9756 28.7382
Methods of Data Presentation III. The Graphical Display – the most effective way of presenting data through the use of statistical graph. - can easily attract the attention as well as the interest of the reader.
Types of Graph I. Bar Graph  – uses rectangular bars the length of which represents the quantity or frequency of each type or category. II. Multiple Bar Graph – useful when the researcher wants to compare figures on two or more different occurrence. - a legend is especially helpful in guiding the  viewer in analyzing the data.
Types of Graph III. Pie Chart – used to present quantities that make up a whole. - constructed using percents and the slices of the pie are drawn in proportion to the different values of each class, item, group or category. IV. Line Chart – useful in showing trends over a  period of time.
Data Organization: The Frequency Distribution Table Definition:  Data in its original form and structure are called raw data. Example:  The following data represent the quarterly sales tax receipts (in thousand dollars) submitted to the comptroller of Gmoserville Township for the period ending March 2010 by all 50 business establishments in that locale: 10.3 11.1 9.6 9.0 14.5 13.0 6.7 11.0 8.9 8.4 10.3 13.0 11.2 7.3 5.3 12.5 8.0 10.1 11.8 10.2 11.1 9.9 9.8 11.6 15.1 12.5 11.5 6.5 7.5 10.0 12.9 9.2 10.0 12.8 12.5 9.3 9.3 10.4 12.7  10.5 9.3 11.5 10.7 11.6 8.6 7.8 10.5 7.6 10.1 8.9
Remark:  When these scores are arranged in either ascending or descending magnitude, then such an arrangement is called an array. Remark:  It is usually helpful to put the  raw data in an array because it is easy to identify the extreme values or the values where the scores most cluster.  Definition:  When the data are placed into a system wherein they are organized, then these partake the nature of grouped data. Definition:  The procedure of organizing data into groups is called a Frequency Distribution Table (FDT)
Example:  The following presents a frequency distribution table of the scores of fifteen Behavioral Statistics Graduate Students. Scores Frequency 20 – 29   5 30 – 39   4 40 – 49   3 50 – 59   2 60 – 69   1   15
Components of a Frequency Distribution Table I. Class Interval  – these are the numbers defining the class. - consist of the end numbers called the class limits namely  the lower limit and upper limit.  II.   Class Frequency (f)  – shows the number of observations falling in the class. III. Class Boundaries  – these are the so called “true class limits”  classified as:   -  Lower Class Boundary (LCB)  is defined as the middle  value of the lower class limits of the class and the upper  class limit of the preceding class.
Components of a Frequency Distribution Table -  Upper Class Boundary  is defined as the middle value  between the upper class limit of the class and the lower limit  of the next class. IV. Class Size  – the difference between two consecutive upper  limits or two consecutive lower limits. V. Class Mark (CM)  – midpoint or the middle value of a class interval.
Components of a Frequency Distribution Table VI. Cumulative frequency  – shows the accumulated frequencies  of successive classes. Types of Cumulative Frequencies A. Greater than CF (> CF)  – shows the number of  observations greater than LCB. B.  Less than CF (< CF)  - shows the number of  observations less than UCB.
The following are the suggested steps in constructing a Frequency Distribution Table. Determine the number of classes. For first approximation, it is suggested to use the Sturge’s Approximation Formula. K = 1 + 3.322 log  n where  K  = approximate number of classes n  = number of cases 2. Determine the range R, where R = maximum value – minimum  value 3. Determine the approximate class size C using the formula  C = R / K.  It is usually convenient to round off  C  to a nearest whole number
4. Determine the lowest class interval (or the first class). This class should include the minimum value in the data set. For uniformity, let us agree that for our purposes, the lower limit of the class interval should start at the minimum value. 5. Determine all class limits by adding the class size C to the limits of the previous class. 6. Tally the scores / observations falling in each class.

More Related Content

PPTX
PRESENTATION OF STATISTICAL DATA
PPTX
Data collection and presentation
PPTX
Presentation of data
PPTX
presentation of data
PPTX
Chapter 3: Prsentation of Data
PPTX
Data presentation
PPTX
Presentation of Data
PPTX
Presentation of Data
PRESENTATION OF STATISTICAL DATA
Data collection and presentation
Presentation of data
presentation of data
Chapter 3: Prsentation of Data
Data presentation
Presentation of Data
Presentation of Data

What's hot (20)

PPTX
Data organization
PPTX
Descriptive statistics
PPT
Introduction To Statistics
PPTX
Introduction to statistics
PPTX
Measures of variability
PPTX
Statistical graphs
PPTX
Analysis and interpretation of data
PPT
Data presentation 2
PPTX
Measure of central tendency grouped data.pptx
PPTX
Interpretation of Data.pptx
PDF
Introduction to Statistics
PPTX
DATA PRESENTATION METHODS - 1.pptx
PPTX
Frequency distribution
PPT
PPTX
Statistics in research
PPTX
Inferential statistics
PPTX
frequency distribution
PPTX
Quartile
PPT
Quantitative Data analysis
PPTX
Descriptive statistics
Data organization
Descriptive statistics
Introduction To Statistics
Introduction to statistics
Measures of variability
Statistical graphs
Analysis and interpretation of data
Data presentation 2
Measure of central tendency grouped data.pptx
Interpretation of Data.pptx
Introduction to Statistics
DATA PRESENTATION METHODS - 1.pptx
Frequency distribution
Statistics in research
Inferential statistics
frequency distribution
Quartile
Quantitative Data analysis
Descriptive statistics
Ad

Similar to Data organization and presentation (statistics for research) (20)

PPTX
Data Presenetation
PPTX
2.1 frequency distributions for organizing and summarizing data
PPTX
Intoduction to statistics
PPT
Source of DATA
PPT
Graphical presentation of data
PPTX
Statistics and probability lesson5
PPT
Chapter 2 250110 083240
PPT
Chapter 2 250110 083240
PPTX
Lesson 5 data presentation
PPTX
WEEK 3 and 4- Formulation and Presentation of Data.pptx
PPTX
Data Presentation biostatistics, school of public health
PPT
Stat11t chapter2
PPTX
day two.pptx
PPTX
Data presentation.pptx
PPT
1) Chapter#02 Presentation of Data.ppt
PDF
iSTAT1-The Frequency Distribution_Relative Frequency_Cummulative.pdf
PDF
Principlles of statistics
PPT
General Statistics boa
PPTX
UNIT II DESCRIPTIVE STATISTICS TABLE GRAPH.pptx
PPTX
Unit # 02. Organising & Displaying Data.pptx
Data Presenetation
2.1 frequency distributions for organizing and summarizing data
Intoduction to statistics
Source of DATA
Graphical presentation of data
Statistics and probability lesson5
Chapter 2 250110 083240
Chapter 2 250110 083240
Lesson 5 data presentation
WEEK 3 and 4- Formulation and Presentation of Data.pptx
Data Presentation biostatistics, school of public health
Stat11t chapter2
day two.pptx
Data presentation.pptx
1) Chapter#02 Presentation of Data.ppt
iSTAT1-The Frequency Distribution_Relative Frequency_Cummulative.pdf
Principlles of statistics
General Statistics boa
UNIT II DESCRIPTIVE STATISTICS TABLE GRAPH.pptx
Unit # 02. Organising & Displaying Data.pptx
Ad

More from Harve Abella (20)

PDF
Know Your Rights when you are Arrested
DOCX
8 reminders for ftf trial-witnesses
DOCX
7 reminders for ftf trial-judges
DOCX
6 reminders for ftf trial-counsels parties
DOCX
5 manual for lawyers and parties rules 22 and 24 (1)
DOCX
3 flowchart of rules 22 and 24
PPTX
2 procedure in trial courts - atty. lazatin presentation
PDF
1 publication rules22-24 (4)
PPT
P29: Basic Kinesics for the Investigator
PDF
P29 PRELIM NOTES
PPT
Basic Consti Law for Undergrads: Powers of congress
PPT
Basic Consti Law for Undergrads: Executive department
PPT
Basic Consti Law for Undergrads: Legislative department
PDF
Annulment Symposium
PDF
Justice Abad: Judicial Affidavit Slides
PPTX
Brgy. Labangon, Cebu City and the Threat to its Territorial Integrity
PPT
Management Prerogatives
PPT
Conducting Employee Investigations 2
PPT
Management Prerogatives
PPT
Conducting Employee Investigations
Know Your Rights when you are Arrested
8 reminders for ftf trial-witnesses
7 reminders for ftf trial-judges
6 reminders for ftf trial-counsels parties
5 manual for lawyers and parties rules 22 and 24 (1)
3 flowchart of rules 22 and 24
2 procedure in trial courts - atty. lazatin presentation
1 publication rules22-24 (4)
P29: Basic Kinesics for the Investigator
P29 PRELIM NOTES
Basic Consti Law for Undergrads: Powers of congress
Basic Consti Law for Undergrads: Executive department
Basic Consti Law for Undergrads: Legislative department
Annulment Symposium
Justice Abad: Judicial Affidavit Slides
Brgy. Labangon, Cebu City and the Threat to its Territorial Integrity
Management Prerogatives
Conducting Employee Investigations 2
Management Prerogatives
Conducting Employee Investigations

Recently uploaded (20)

PDF
Review of recent advances in non-invasive hemoglobin estimation
PDF
Chapter 3 Spatial Domain Image Processing.pdf
PPTX
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
PPTX
Cloud computing and distributed systems.
PPTX
Spectroscopy.pptx food analysis technology
PDF
Spectral efficient network and resource selection model in 5G networks
PDF
Advanced methodologies resolving dimensionality complications for autism neur...
PDF
Empathic Computing: Creating Shared Understanding
PPT
“AI and Expert System Decision Support & Business Intelligence Systems”
PDF
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
PDF
Agricultural_Statistics_at_a_Glance_2022_0.pdf
PDF
Reach Out and Touch Someone: Haptics and Empathic Computing
PDF
Blue Purple Modern Animated Computer Science Presentation.pdf.pdf
PDF
Dropbox Q2 2025 Financial Results & Investor Presentation
PDF
Mobile App Security Testing_ A Comprehensive Guide.pdf
PPTX
Big Data Technologies - Introduction.pptx
PDF
Unlocking AI with Model Context Protocol (MCP)
PDF
Building Integrated photovoltaic BIPV_UPV.pdf
PPTX
Programs and apps: productivity, graphics, security and other tools
PPTX
sap open course for s4hana steps from ECC to s4
Review of recent advances in non-invasive hemoglobin estimation
Chapter 3 Spatial Domain Image Processing.pdf
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
Cloud computing and distributed systems.
Spectroscopy.pptx food analysis technology
Spectral efficient network and resource selection model in 5G networks
Advanced methodologies resolving dimensionality complications for autism neur...
Empathic Computing: Creating Shared Understanding
“AI and Expert System Decision Support & Business Intelligence Systems”
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
Agricultural_Statistics_at_a_Glance_2022_0.pdf
Reach Out and Touch Someone: Haptics and Empathic Computing
Blue Purple Modern Animated Computer Science Presentation.pdf.pdf
Dropbox Q2 2025 Financial Results & Investor Presentation
Mobile App Security Testing_ A Comprehensive Guide.pdf
Big Data Technologies - Introduction.pptx
Unlocking AI with Model Context Protocol (MCP)
Building Integrated photovoltaic BIPV_UPV.pdf
Programs and apps: productivity, graphics, security and other tools
sap open course for s4hana steps from ECC to s4

Data organization and presentation (statistics for research)

  • 1. Methods of Data Presentation Remark: One of the most important aspects in any statistical investigation is the manner by which the researcher presents the data. Various Modes of Data Presentation I. Textual II. Tabular III. Graphical Displays
  • 2. Methods of Data Presentation I. Textual Presentation – the data are presented in the form of texts, phrases or paragraphs. - common among newspaper reports depicting specifically the salient or important findings. Example: The Philippine Stock Exchange composite index lost 7.19 points to 2,099.12 after trading between 2,095.30 and 2,108.47. Volume was 1.29 billion shares worth 903.15 million pesos (16.7 million dollars). The broader all share index gained 5.21 points to 1,221.34. (From: Freeman dated March 17, 2005)
  • 3. Methods of Data Presentation II. The Tabular Display – a more reliable and effective way of showing relationships or comparisons of data through the use of tables. - the tables must be accompanied by a short narrative explanation to make the facts clearer and more understandable.
  • 4. Methods of Data Presentation Example: The following newspaper report presents a tabular data presentation . Country Peso United States Japan United Kingdom Hongkong Switzerland Canada Singapore 50. 7890 0.4140 72.5267 6.5116 28.7382 32.9756 28.7382
  • 5. Methods of Data Presentation III. The Graphical Display – the most effective way of presenting data through the use of statistical graph. - can easily attract the attention as well as the interest of the reader.
  • 6. Types of Graph I. Bar Graph – uses rectangular bars the length of which represents the quantity or frequency of each type or category. II. Multiple Bar Graph – useful when the researcher wants to compare figures on two or more different occurrence. - a legend is especially helpful in guiding the viewer in analyzing the data.
  • 7. Types of Graph III. Pie Chart – used to present quantities that make up a whole. - constructed using percents and the slices of the pie are drawn in proportion to the different values of each class, item, group or category. IV. Line Chart – useful in showing trends over a period of time.
  • 8. Data Organization: The Frequency Distribution Table Definition: Data in its original form and structure are called raw data. Example: The following data represent the quarterly sales tax receipts (in thousand dollars) submitted to the comptroller of Gmoserville Township for the period ending March 2010 by all 50 business establishments in that locale: 10.3 11.1 9.6 9.0 14.5 13.0 6.7 11.0 8.9 8.4 10.3 13.0 11.2 7.3 5.3 12.5 8.0 10.1 11.8 10.2 11.1 9.9 9.8 11.6 15.1 12.5 11.5 6.5 7.5 10.0 12.9 9.2 10.0 12.8 12.5 9.3 9.3 10.4 12.7 10.5 9.3 11.5 10.7 11.6 8.6 7.8 10.5 7.6 10.1 8.9
  • 9. Remark: When these scores are arranged in either ascending or descending magnitude, then such an arrangement is called an array. Remark: It is usually helpful to put the raw data in an array because it is easy to identify the extreme values or the values where the scores most cluster. Definition: When the data are placed into a system wherein they are organized, then these partake the nature of grouped data. Definition: The procedure of organizing data into groups is called a Frequency Distribution Table (FDT)
  • 10. Example: The following presents a frequency distribution table of the scores of fifteen Behavioral Statistics Graduate Students. Scores Frequency 20 – 29 5 30 – 39 4 40 – 49 3 50 – 59 2 60 – 69 1 15
  • 11. Components of a Frequency Distribution Table I. Class Interval – these are the numbers defining the class. - consist of the end numbers called the class limits namely the lower limit and upper limit. II. Class Frequency (f) – shows the number of observations falling in the class. III. Class Boundaries – these are the so called “true class limits” classified as: - Lower Class Boundary (LCB) is defined as the middle value of the lower class limits of the class and the upper class limit of the preceding class.
  • 12. Components of a Frequency Distribution Table - Upper Class Boundary is defined as the middle value between the upper class limit of the class and the lower limit of the next class. IV. Class Size – the difference between two consecutive upper limits or two consecutive lower limits. V. Class Mark (CM) – midpoint or the middle value of a class interval.
  • 13. Components of a Frequency Distribution Table VI. Cumulative frequency – shows the accumulated frequencies of successive classes. Types of Cumulative Frequencies A. Greater than CF (> CF) – shows the number of observations greater than LCB. B. Less than CF (< CF) - shows the number of observations less than UCB.
  • 14. The following are the suggested steps in constructing a Frequency Distribution Table. Determine the number of classes. For first approximation, it is suggested to use the Sturge’s Approximation Formula. K = 1 + 3.322 log n where K = approximate number of classes n = number of cases 2. Determine the range R, where R = maximum value – minimum value 3. Determine the approximate class size C using the formula C = R / K. It is usually convenient to round off C to a nearest whole number
  • 15. 4. Determine the lowest class interval (or the first class). This class should include the minimum value in the data set. For uniformity, let us agree that for our purposes, the lower limit of the class interval should start at the minimum value. 5. Determine all class limits by adding the class size C to the limits of the previous class. 6. Tally the scores / observations falling in each class.