Topic: Pie Chart
Student Name: Javeria
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
Topic: Pie Chart
Student Name: Javeria
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
Prelude
PART (A) TYPES OF GRAPHS
Line graphs
Pie charts
Bar graph
Scatter plot
Stem and plot
Histogram
Frequency polygon
Frequency curve
Cumulative frequency or ogives
PART (B) FLOW CHART
PART (C) LOG AND SEMILOG GRAPH
A bar graph is a chart that uses either horizontal ,A Pie Chart (or Pie Graph) is a circular chart divided into sectors,It is also possible to draw bar charts .
Prelude
PART (A) TYPES OF GRAPHS
Line graphs
Pie charts
Bar graph
Scatter plot
Stem and plot
Histogram
Frequency polygon
Frequency curve
Cumulative frequency or ogives
PART (B) FLOW CHART
PART (C) LOG AND SEMILOG GRAPH
A bar graph is a chart that uses either horizontal ,A Pie Chart (or Pie Graph) is a circular chart divided into sectors,It is also possible to draw bar charts .
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
Frequency distribution, types of frequency distribution.
Ungrouped frequency distribution
Grouped frequency distribution
Cumulative frequency distribution
Relative frequency distribution
Relative cumulative frequency distribution
Graphical representation of frequency distribution
I. Representation of Grouped data
1.Line graphs
2.Bar diagrams
a) Simple bar diagram
b)Multiple/Grouped bar diagram
c)Sub-divided bar diagram.
d) % bar diagram
3. Pie charts
4.Pictogram
II. Graphical representation of ungrouped data
1, Histogram
2.Frequency polygon
3.Cumulative change diagram
4. Proportional change diagram
5. Ratio diagram
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Learning objectives
At the end of this section students are expected to:
• understand the nature of data
• organize and present data according to the need of
the activity
• present data in table and graphical ways for
information use.
2
4/17/2023
3. Data organization and presentation
• Statistics is used to organize and interpret research
observations and findings.
• Before interpretation & communication of the
findings, the raw data must be organized and
presented in a clear and understandable way.
Techniques used to organize and summarize a set of
data in a concise way.
– Organization of data
– Summarization of data
– Presentation of data
3
4/17/2023
4. Cont...
• Numbers that have not been summarized and
organized are called raw data
Descriptive statistic includes tables, graphical
/chart displays and calculation of summary
measures such as mean, proportions, averages
etc…
• The methods of describing variables differ
depending on the type of data (Numerical or
Categorical).
4
4/17/2023
5. Organizing data
Categorical data
• Table of frequency
distributions
– Frequency
– Relative frequency
– Cumulative frequencies
• Graphs
– Bar charts
– Pie charts
Continuous or discrete data
• Frequency distribution
• Summary measures
Graphs
– Histograms
– Frequency polygons
– Cumulative frequency polygons
Leaf and steam
Box and whisker Plots
Scatter plot
5
4/17/2023
6. Frequency distributions
• A frequency distribution is a presentation of the
number of times (or the frequency) that each value (or
group of values) occurs in the study population.
• Ordered array: A simple arrangement of individual
observations in order of magnitude.
• A simple and effective way of summarizing categorical
data is to construct a frequency distribution table.
• This is done by counting the number of observations
falling into each of the categories, or levels of the
variables.
• Consider for example, the variable birth weight with
levels ‘Very low ’, ‘Low’, ‘Normal’ and ‘Big’.
6
4/17/2023
7. Relative Frequency
• Sometimes it is useful to compute the
proportion, or percentages of observations in
each category.
• The distribution of proportions is called the
relative frequency distribution of the variable.
• Given a total number of observations, the
relative frequency distribution is easily derived
from the frequency distribution.
7
4/17/2023
8. Cumulative frequency
• Two other distributions are useful describing
particularly ordinal data.
• It tells nothing in nominal data.
E.g. You will never say 70% are below blue
color.
• The cumulative frequency is the number of
observations in the category plus observations in
all categories smaller than it.
• Cumulative relative frequency is the
proportion of observations in the category plus
observations in all categories smaller than it, and
is obtained by dividing the cumulative frequency
by the total number of observations.
8
4/17/2023
9. Table 2. Distribution of birth weight of newborns
between 1976-1996 at TAH.
BWT Freq. Rel. Freq(%) Cum. Freq Cum.rel.freq.(%)
Very low 43 0.4 43 0.4
Low 793 8.0 836 8.4
Normal 8870 88.9 9706 97.3
Big 268 2.7 9974 100_____
Total 9974 100
9
4/17/2023
10. Frequency distribution for numerical data
• Ordered array, further useful summarization may
be achieved by grouping the data.
• To group a set of observations we select a set of
continuous, non overlapping intervals such
that each value in the set of observations can be
placed in one, and only one, of the intervals.
• These intervals are usually referred to as class
intervals.
10
4/17/2023
11. • One of the first considerations when
data are to be grouped is how many
intervals to include
• The question is how best can we
organize such data. Imagine when
we have huge data set which may
not be manageable by eye.
4/17/2023 11
16. How to calculate class interval?
To determine the number of class intervals and the
corresponding width, we use:
Sturge’s rule:
K=1+3.322(logn)
W=L-S
K
where
K = number of class intervals n = no. of observations
W = width of the class interval L = the largest value
S = the smallest value
16
4/17/2023
17. Example
• Construct a grouped frequency
distribution of the following data on the
amount of time (in hours) that 80 college
students devoted to leisure activities
during a typical school week:
4/17/2023 17
19. The amount of time (in hours) that 80 college students devoted to leisure activities
during a typical school week
• Using the above formula,
K = 1 + 3.322 log (80)
= 7.32 7 classes
• Maximum value = 38 and Minimum value = 10
• w= Range/k = (38 – 10)/7= 28/7 = 4
• Using width of 5(common rule of thumb), we
can construct grouped frequency distribution
for the above data as:
4/17/2023 19
21. Mid-point and True-limits
Mid-point (class mark): The value of the interval
which lies midway between the lower and the upper
limits of a class.
True limits(class boundaries): Are those limits
that make an interval of a continuous variable
continuous in both directions
Used for smoothening of the class intervals
Subtract 0.5 from the lower and add it to the upper
limit
21
4/17/2023
22. Contd…
• Note. In the construction of cumulative
frequency distribution, if we start the cumulation
from the lowest size of the variable to the highest
size, the resulting frequency distribution is called
`Less than cumulative frequency distribution'
and if the cumulation is from the highest to the
lowest value the resulting frequency distribution
is called `more than cumulative frequency
distribution.' The most common cumulative
frequency is the less than cumulative frequency
4/17/2023 22
24. • Class interval: The length of the class, it is
given by the difference between class
boundaries for 1st class, the interval is 5.
• Note: As sample increases, and interval
reduced the sample distribution resembles
the population distribution
4/17/2023 24
25. – Class intervals should be continuous, non
overlapping, mutually exclusive and exhaustive
– Too few intervals results loss of information
– Too many intervals results that the objective of
summarization will not be met.
– Class intervals generally should be of the same
width (some times impossible)
– Open ended class intervals should be avoided
25
26. Exercise
• Construct a
grouped frequency
distribution and
complete the
following table for
the Age of patients
(years) in a diabetic
clinic in Addis
Ababa, 2010
4/17/2023 26
27. Age of patients (years) in a diabetic clinic in
Addis Ababa, 2010
Age
group
(Years)
Class
limit
Class
Boundary
Class
Mid
Point
Tally
Fr.
(fi)
Relative
Frequency
,
Fraction
(%)
Cumulative freq Relative Cum freq
<Method >Method <Method >Method
Total
4/17/2023 27
29. Data table
Guidelines for constructing tables
• Keep them simple
• Limit the number of variables
• All tables should be self-explanatory
• Include clear title telling what, where and
when
• Clearly label the rows and columns
29
4/17/2023
30. Cntd…
• State clearly the unit of measurement used
• Explain codes and abbreviations in the foot-
note
• Show totals
• If data is not original, indicate the source in
foot-note
4/17/2023 30
31. Graphical presentation of data
• Variety of graph styles can be used to present
data.
• The most commonly used types of graph are pie
charts, bar diagrams, histograms, frequency
polygon and scatter diagrams.
• The purpose of using a graph is to tell others
about a set of data quickly, allowing them to
grasp the important characteristics of the data.
• In other words, graphs are visual aids to rapid
understanding.
31
4/17/2023
32. Importance of graphs
• Diagrams have greater attraction than mere
figures.
• They give delight to the eye, add a spark of
interest and as such catch the attention
• They help in deriving the required
information in less time and without any
mental strain.
• They have great memorizing value than
mere figures.
• They facilitate comparison
4/17/2023 32
33. Bar charts
• Bar chart: Display the frequency distribution for
nominal or ordinal data.
• In a bar chart the various categories into which the
observation fall are represented along horizontal axis
and
• A vertical bar is drawn above each category such that
the height of the bar represents either the frequency
or the relative frequency of observation within the
class.
• The vertical axis should always start from 0 but the
horizontal can start from any where.
• The bars should be of equal width and should be
separated from one another so as not to imply
continuity
33
4/17/2023
34. Figure 1. Bar charts showing frequency distribution of
the variable ‘BWT’.
0
1000
2000
3000
4000
5000
6000
Very low Low Normal Big
BWT
Freq.
0
20
40
60
80
100
Verylow Low Normal Big
BWT
Rel.
Freq.
34
4/17/2023
35. Bar charts for comparison
• Multiple bar chart: In order to compare the
distribution of a variable for two or more
groups, bars are often drawn along side each
other for groups being compared in a single bar
chart.
• Sub division bar chart: If there are different
quantities forming the sub-divisions of the
totals, simple bars may be sub-divided in the
ratio of the various sub-divisions to exhibit the
relationship of the parts to the whole.
35
4/17/2023
36. Fig 2. Bar chart indicating categories of birth weight of 9975
newborns grouped by antenatal follow-up of the mothers
9
88.9
2.1
7.9
89
3.1
0
10
20
30
40
50
60
70
80
90
100
Low Normal Big
BWT
Percent
Yes
No
36
4/17/2023
38. Pie chart
Pie Chart: Displays the frequency
distribution for nominal or ordinal data.
• In a pie chart the various categories into
which the observation fall are represented
along sectors of a circle
• Each sector represents either the
frequency or the relative frequency of
observation within the class the angles of
which are proportional to frequency or the
relative frequency.
38
4/17/2023
39. Figure 3. Pie charts showing frequency distribution of
the variable ‘BWT’
Fig 3(b) Pie chart indicating relative frequencyof
categories of birth weight
0.4 8
88.9
2.7
Very low
Low
Normal
Big
Fig 3(a) Pie chart indicating frequencyof categories
of birth weight
43 793
8870
268
Verylow
Low
Normal
Big
39
4/17/2023
40. Histogram
• Histogram is frequency distributions with
continuous class interval that has been turned into
graph.
• Given a set of numerical data, we can obtain
impression of the shape of its distribution by
constructing a histogram.
• A histogram is constructed by choosing a set of
non-overlapping intervals (class intervals) and
counting the number of observations that fall in
each class.
. 40
4/17/2023
41. Histograms cont…
• The number of observations in each class
is called the frequency. Hence histograms
are also called frequency distributions
• It is necessary that the class intervals be
non-overlapping so that each observation
falls in one and only one interval.
4/17/2023 41
42. Histograms cont…
• Except for the two boundaries, class intervals
are usually chosen to be of equal width. If this
is not the case, the histogram could give a
misleading impression of the shape of the data
• In drawing the histogram , smoothening of
class interval is one of important point. We
subtract 0.5 from the lower and add it up to the
upper boundary of the given interval.
42
4/17/2023
43. Example
Distribution of the age of women at the time of
marriage
Age group No. of women
15-19 11
20-24 36
25-29 28
30-34 13
35-39 7
40-44 3
45-49 2
43
4/17/2023
44. Age of women at the time of marriage
0
5
10
15
20
25
30
35
40
14.5-19.5 19.5-24.5 24.5-29.5 29.5-34.5 34.5-39.5 39.5-44.5 44.5-49.5
Age group
No
of
women
44
4/17/2023
45. Fig 5. A histogram displaying frequency distribution of birth
weight of newborns at Tikur Anbessa Hospital
Birth weight
5200
4800
4400
4000
3600
3200
2800
2400
2000
1600
1200
800
2000
1800
1600
1400
1200
1000
800
600
400
200
0
Std. Dev = 502.34
Mean = 3126
N = 9975.00
45
4/17/2023
46. Frequency polygons
• Instead of drawing bars for each class interval,
sometimes a single point is drawn at the mid
point of each class interval and consecutive
points joined by straight line.
• Graphs drawn in this way are called frequency
polygons .
• Frequency polygons are superior to histograms
for comparing two or more sets of data.
46
4/17/2023
47. Fig.6. Frequency polygon of birth weight of 9975 newborns at Tikur
Anbessa Hospital for males and females
Birth Weight
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
%
50
40
30
20
10
0
SEX
Males
Females
47
4/17/2023
48. Box and Whisker Plot
It is another way to display information when
the objective is to illustrate certain locations
(skewness) in the distribution
Can be used to display a set of discrete or
continuous observations using a single vertical
axis – only certain summaries of the data are
shown
48
4/17/2023
49. Box plot cont...
A box is drawn with the top of the box at the third
quartile (75%) and the bottom at the first quartile
(25%).
The location of the mid-point (50%) of the
distribution is indicated with a horizontal line in the
box.
Finally, straight lines, or whiskers, are drawn from the
centre of the top of the box to the largest observation
and from the centre of the bottom of the box to the
smallest observation.
49
4/17/2023
50. Box cont....
The box plot is then completed
Draw a vertical bar from the upper quartile to
the largest non-outlining value in the sample
Draw a vertical bar from the lower quartile to the
smallest non-outlying value in the sample
Any values that are outside the IQR but are not
outliers are marked by the whiskers on the plot
(IQR = P75 – P25)
50
4/17/2023
51. Box plots are useful for comparing two or
more groups of observations
51
4/17/2023
52. Drawing Box-and -whiskers plot
Raw data
35, 29, 44, 72, 34, 64, 41, 50, 54, 104, 39, 58
Order the data
29 34 35 39 41 44 50 54 58 64 72 104
Median = (44 + 50)/2 = 47 = Q2
Q1 = 37
Q3 = 61,Min = 29 , Max = 104
52
4/17/2023
54. Scatter plot
Most studies in medicine involve measuring
more than one characteristic, and graphs
displaying the relationship between two
characteristics are common in literature.
When both the variables are qualitative then
we can use a multiple bar graph.
When one of the characteristics is qualitative
and the other is quantitative, the data can be
displayed in box and whisker plots
54
4/17/2023
55. Scatter plot ….
For two quantitative variables we use bivariate
plots (also called scatter plots or scatter
diagrams).
It is used to see whether a relationship existed
between the two measures.
A scatter diagram is constructed by drawing
X-and Y-axes
Each point represented by a point or dot()
represents a pair of values measured for a single
study subject =POSTIVE RELATION
55
4/17/2023
56. 0 2 4 6 8 10 12 14 16 18 20
0
10
20
30
40
50
60
Hours of Training
Negative Correlation as x increases, y decreases
x = hours of training
y = number of accidents
Scatter Plots and Types of Correlation
Accidents
56
57. 300 350 400 450 500 550 600 650 700 750 800
1.50
1.75
2.00
2.25
2.50
2.75
3.00
3.25
3.50
3.75
4.00
Math SAT
Positive Correlation as x increases y increases
x = SAT score
y = GPA
GPA
Scatter Plots and Types of Correlation
57
59. 1. Direction of Relationship
Positive
Negative
X
X
Y
Y
Scatter Diagram…
4/17/2023 59
60. 2. Form of Relationship
Linear
Curvilinear
X
Y
X
Y
4/17/2023 60
61. 3. Degree of Relationship
Strong
Weak
X
Y
X
Y
4/17/2023 61
62. Line graph
Useful for assessing the trend of particular situation
overtime. e.g. monitoring the trend of epidemics.
The time, in weeks, months or years, is marked along
the horizontal axis
Values of the quantity being studied is marked on the
vertical axis.
Values for each category are connected by continuous
line.
Sometimes two or more graphs are drawn on the same
graph taking the same scale so that the plotted graphs
are comparable.
62
4/17/2023
63. No. of microscopically confirmed malaria cases by species and month
at Zeway malaria control unit, 2003
0
300
600
900
1200
1500
1800
2100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months
No.
of
confirmed
malaria
cases
Positive
P. falciparum
P. vivax
63
4/17/2023
64. Line graph cont..
The following graph shows level of zidovudine
(AZT) in the blood of HIV/AIDS patients at
several times after administration of the drug,
for with normal fat absorption and with fat
mal absorption.
Line graph can be also used to depict the
relationship between two continuous
variables like that of scatter diagram.
64
4/17/2023
65. Line graph cont…..
Response to administration of zidovudine in two groups of AIDS
patients in hospital X, 1999
0
1
2
3
4
5
6
7
8
10
20
70
80
100
120
170
190
250
300
360
Time since administration (Min.)
Blood
zidovudine
concentration
Fat malabsorption Normal fat absorption
65
4/17/2023
66. Choosing graphs
Type of Data/or
Purpose
Appropriate Graphs
Metric/Numerical -Histogram (one continuous var)
-Frequency Polygon (one/more cont. var)
-Cumulative Freq Polygon (ogive curve)
-Box and whisker (one cont. and one cat.
Var)
-Stem and Leave (one cont. var)
-Scatter (two cont. var)
Categorical -Bar (one/more cat. var) (Simple/Multiple)
-Pie (one cat. var)
Trend -Line (one cont. and one cat. Var/two
cont)
4/17/2023 66