Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and
claims in oral and written presentations. E/S
The ability to organize collected data and graphically represent it in a fashion that is not only
accurate but informative is a necessary skill. The ability to collect, organize, analyze and display
data is essential to a scientifically literate person. However, this skill transcends the boundaries
of science and is commonly used in and is fundamental to daily life. People are constantly
collecting data, (e.g. “How fast am I driving?”, “How far is it to the stop light?”, “What food do I
need to buy at the grocery store?”) analyzing data, (e.g. “Can I stop if the light turns yellow?”,
“Should I stop or will I be rear ended?”, “Am I committed to entering the intersection?”, “Can I
wait one more day to go shopping?), and acting on that data. In the examples above, there was
probably no graphical representation of data unless the person left a “line graph” from each of
their rear tires (which could be data that the police collect and analyze). Although the oral
presentation may not be fit to record here, there were probably no written presentations, unless
the police were analyzing the data from the scene. Often times due to the common nature of this
skill, teachers overlook its full importance. Science teachers will frequently concentrate their
efforts on teaching the students the sub-skills of collecting and graphing data to the detriment of
analyzing the data, and even making “data supported” arguments.
Students at all levels can and should be taught all aspects of this benchmark. There is no
legitimate reason to concentrate on one or two sub-skills in exclusion of the others. Being able
to (accurately) collect data and graph the data are necessary skills. However, lacking the skill to
make sense of what that data means or not being able to use the data to support or refute an
argument makes the skill of graphing just short of useless.
There are basically two types of data that a student can collect: qualitative or quantitative.
Qualitative data is extremely varied in nature. It can include virtually any information that is not
numerical. Examples of quantitative data include in-depth interviews, direct observations and
written documents. Quantitative data is numerical in nature. It is data that is measured or
identified on a numerical scale. Quantitative data can be analyzed using statistical methods and
the results can be graphically displayed using histograms, charts, tables and graphs.
For a detailed discussion on statistical analysis of data see TIPS Benchmark N.12.A.3
How the collected data is organized is dependent on the type of data being gathered. A data
table is often the first step in organizing and recording the collected data. Although a data table
can be used to organize qualitative data, the data table is essential when quantitative data is being
gathered.
When provided with a written description of an experiment, the students should be able to
determine what data will need to be collected and what if any data will need to be derived
(calculated from the data gathered). Data tables provide an organized method of recording the
data that has been collected. There are no absolute rules when it comes to the design and use of a
data table, but there are some commonly used conventions that should be followed when using a
data table. One of the first steps students should do when using a data table is to look at the
experiment, survey or situation they are to be observing and try to determine what data will need
to be recorded. The types of data and the relative quantity of data should first be determined
before the students start to design and draw the required table. For example, the following
questions may be considered: Will temperature and time be collected? If so, how long will the
data be collected, and at what interval will the collection take place? Is the data more complex in
nature with multiple variables being analyzed, and/or multiple trial runs?
The answers to the above questions will determine the number of columns and rows the data
table must have in order to meet the needs of the data being collected. How the data table is
organized to meet these needs is also very important. A data table has certain requirements:
1. Title: A descriptive title is needed to inform the reader as to what data has been
collected and what, if any, manipulation has been done to that data.
2. Column Label: Each column of the data table must be labeled, with units
noted, as to what data has been recorded. Example units include time (seconds),
temperature (degrees C), speed (km/hr), mass (kg).
3. Data: The actual data collected. As part of the planning phase the student
should have determined how many significant figures for each data value.
When setting up the data table, the Independent Variable should be in the left hand column,
while the Dependent Variable in the right hand column (Figure 1). If there were multiple trials,
the independent variable is not re-recorded for each run, but a new dependent variable column is
added to the right of the previous trial run (Figure 2). After the data has been collected, and some
mathematical/statistical manipulation is performed; the resulting value(s) is considered the
derived data and is placed in the far right-hand column of the data table. Sequencing the data in
this order, independent, dependent and derived will help the students when it comes time to
construct a graph. Ordering the data pairs (independent, dependent) is a convention used when
graphing data.
It is important that the students understand the difference between independent and dependent
variables. The Independent Variable, also known as the manipulated variable, is the variable
that the scientist chooses to control. In the case of the Ball Drop experiment, the independent
variable is the height the ball is dropped from, in a Temperature/Time experiment it would be the
time interval at which measurements are taken that is being manipulated (e.g. The change in the
temperature of a liquid is measured at one minute intervals). The Dependent Variable, also
known as the responding variable, is the variable that changes in response to the independent
variable. The height of the balls bounce is DEPENDENT on the height at which the ball was
dropped. As the independent variable is manipulated, the height changed, the dependent variable
responded accordingly.
The Effect of Drop Height on Bounce Height of a Rubber Ball
Height of Drop (cm)
Height of Bounce (cm)
5
4
10
6
15
11
20
13
25
16
30
21
Figure 1. Sample Data Table for One Trial Run
The Effect of Drop Height on Bounce Height of a Rubber Ball
Height of Drop
(cm)
Trial 1
Trial 2
Trial 3
5
4
3
2
Average Bounce
Height for all
Trials (cm)
3
10
6
6
5
5.6
15
11
12
11
11.3
20
13
14
14
13.7
25
16
15
16
15.7
30
21
20
21
20.7
Height of Bounce (cm)
Figure 2. Sample Data Table for Three Trial Runs
When attempting to graph the data from the above example, the following data pairs would be
used for graphing Trial #1 (5, 4), (10, 6), (15, 11), (20, 13), (25, 16) and (30, 21).
Graphs communicate data in a pictorial form and thus may allow the viewer to quickly and
clearly see trends and patterns that may exist in the data collected. There are many types of
graphs that students should be familiar with making, reading and analyzing. These graphs
include line, bar, histograms, scatter plots, and pie charts or circle graphs.
LINE GRAPH
Line graphs are best used when the there is a connection between each of the dependent
variable’s data points. (Figure 3, “The Effect of Drop Height on Bounce Height of a Rubber
Ball.”) In other words, when the independent variable is manipulated the dependent variable will
respond in a predictable fashion. In Figure 2 and Figure 3, we see that when the drop height for
the ball increases the bounce height also increases. There is a relationship between the drop
height and bounce height of the ball, students could reasonably ascertain the approximate
formula that would be able to mathematically predict what the given bounce height would be for
an untested drop height.
Bounce Height
(cm)
Effect of Drop Height on bounce height of a Rubber Ball
25
20
15
10
5
0
Trail 1
Trial 2
Trial 3
5
10
15
20
Drop Height (cm)
25
30
Average Bounce Height for
All Trials
Figure 3. A graph depicting “The Effect of Drop Height on Bounce Height of a Rubber Ball”
As with data tables, there are no hard and fast rules to graphing, but there are widely accepted
conventions that should be followed.
Title: The graph’s title should be descriptive of what data has been plotted. The title along with
the axis labels should allow the reader to develop a clear understanding what will be graphically
represented. An appropriate title for a graph of Figure #1 Ball Bounce Data Table might be: A
Graph of the Effect of Drop Height on the Bounce Height of a Rubber Ball. The title of the graph
is very similar to that of the data table as both are representing the same information, just in two
different formats.
X-axis: The horizontal axis used to plot the independent (or manipulated) variable. The
independent variable is the first data value in an ordered pair. If the data being plotted was from
the Ball Bounce Height Data Table shown above, the Drop Height would be plotted on the xaxis. If the experiment were a time verses anything graph, time would be plotted on the x-axis.
Y-axis: The vertical axis used to plot the dependent (responding) variable. Using the Ball
Bounce data table example, the Bounce Height would be plotted on the y-axis.
Axis Labels: Should be descriptive of what is being plotted and must include the measurement
units. For example, Drop Height (cm), Bounce Height (cm), Time (s), Distance Traveled (km).
Interval Scale: The distance between each hatch mark on the vertical or horizontal axes.
Although the axes do not need to have the same interval, the interval that is chosen for an axis
must be consistent over the length of the axis. This means that if an interval of 5 units is used
(e.g. 0, 5, 10, 15, 20) between two of the hatch marks, the interval can NOT be changed for a
different part of the axis. The interval value chosen should be one that is fairly easy to count by,
therefore, 1, 2, 3, 5 and multiples of 10 generally work well. The interval of the axis is largely
determined by the range in the data being plotted. As a general rule or point of preference, 4 or 5
interval marks allow for ease of reading the graph. In order to determine the interval, divide the
range by 4 or 5. For example: if the largest value is 62 and the smallest is 14, the range would be
62 – 14 = 48. 48 / 5 = 9.6. For the ease of counting, 10 may be an appropriate value for each
interval.
Determining the interval for the graph is an important skill. Two basic conventions concerning
the interval is that it is evenly spaced, and that there are typically only 4 and 6 interval marks.
The 1st interval mark is “less than” the 1st data point (i.e. if graphing the ordered pair (6, 4) the 1st
interval mark on the X-axis could be any number less than 6), but for ease of counting the
interval would probably be 1, 2, 3, 5, or 10 with the exact interval to be determined by the total
range of data for that particular axis and end after the last data point. If the data for the y-axis
ranged from 3 at the low end to 37 at the high end the total range would be 34 (37 – 3 = 34). In
this example an appropriate interval might be 5 units. The 1st interval mark would be zero (0) on
the scale and the last would be 40. Although in this example we have exceeded the suggested
number of interval marks, five (5) is an easy number to count by, and thus makes this an
appropriate interval. When drawing the graph, the entire space for the graph should be used, so
the interval should be appropriate to maximize the use of available space.
Line Breaks: Used to show when the axes of the graph are not a continuous number line from
origin to the final value. The scale on the axis is not required to start at the origin (0,0) and count
continuously to the final interval value. It is important to note that if there is any portion of the
axis range removed for ease of drawing the graph or plotting the data, the axis line MUST show
a line break symbol “≠” indicating that a portion of the axis is not drawn.
The Origin: Graphs usually start at the origin (0,0) however it is not required. Based on the
total range of data, the value of the last interval mark and the interval unit will all contribute to
the decision to start the graph at the origin, or to start it at some value close to but less than the
lowest number in the data range.
Bar Graphs
Line graphs are best used when the there is NOT a connection between each of the dependent
variable’s data points. For example the data collected in Figure #4 was from a survey and
consisted of such questions as your favorite movie, food and music for a given group of students.
In this case there is my not be a relationship between these variables. As with data tables and line
graphs there are no hard and fast rules, simply guidelines that should be followed when ever
possible. One major difference between Bar Graphs and Line Graphs is that the dependent and
independent variables can be graphed on either the X or the Y axis, depending on the desired
effect. If the variables are plotted in the traditional manner, x-axis for the independent and y-axis
for the dependent then the bars will have a vertical orientation. If the variables are reversed, the
bars will have a horizontal orientation. The orientation of the bars can be used to better illustrate
the data. A vertical orientation MAY be more appropriate when attempting to show price
differences between items, or which variable was more popular than another. A horizontal
orientation MAY be more appropriate when comparing such things as the difference between
distances traveled, or times spent on a task. The strength of a bar graph is to get a quick idea of
how the data is distributed, not necessarily to be able to determine the exact numerical value of
any particular bar.
Steps to making a Bar Graph
1. Draw the X and Y Axis
When setting up a Bar Graph, the 1st step is to draw the X and Y axis. If we graph the
sample data provided, Figure #4 “Sample Bar Graph Data”, the 5 favorite foods are the
independent variable for this data set, and thus would be plotted on the x-axis. The x-axis is
the horizontal and represents Independent Variable; the independent variable is the variable
that is chosen by the experimenter. The y-axis is the vertical axis and represents Dependent
Variable. In Figure #4, the dependent variable would be the number of respondents to each
food type.
Data Table of 5 Favorite Foods in My 8th Grade Class
Pizza
Hamburgers
Sushi Pop Tarts
Broccoli
14
6
27
25
24
Figure 4. Sample Bar Graph Data, “5 Favorite foods in My 8th Grade Class”
2. Set the Scale of the Graph
The scale for the X-axis will be the independent variable labels. In this case the food items;
Pizza, Hamburgers, Sushi, Pop Tarts, and Broccoli would be the labels that would divide the
x-axis. The y-axis scale would be set based on the number of data point or responses
recorded for each of the independent variables. See Figure #4 (Sample Data for a Bar Graph)
for the number of data points recorded for each of the independent variables. Generally, the
scale for the y-axis extends from zero to one interval mark greater than the highest recorded
number of respondents in the data collected. With the sample data provided, the greatest
number of respondents, 27, stated they prefer Sushi as their favorite food. With the interval
set at 5 per interval mark, the Y-axis must have a scale that extends from 0 to 30 in order to
appropriately accommodate the data to be plotted.
3. Plot the data on the graph
Make a bar extending from the x-axis up to the appropriate height on the y-axis for the data
corresponding to each of the independent variables. Make sure to double check the numbers
to be plotted, to avoid a common mistake of mis-plotting. If the value in question splits an
interval mark it may be necessary to estimate the exact height of the bar. Error in estimation
can lead to “data creep” which can be common when the interval is fairly large compared to
the how “tight” the data is. For example, Broccoli and Pop Tarts are tied for 2nd and 3rd most
popular foods with 24 and 26 respondents, respectively.
4. A Descriptive Title
With all carefully constructed graphs and data tables, a descriptive title is essential to help
eliminate possible confusion on the part of the reader. The title should provide the reader
with a reasonable understanding of what the graph is portraying.
Number of people
5 Most Favorite Foods in My 8th Grade Science
Class
30
25
20
15
10
5
0
pizza
hamburgers
sushi
Pop Tarts
Broccoli
Favorite Foods
Figure 5. Sample Bar Graph, “5 Most Favorite Foods in My 8th Grade Science Class”
Histograms
Histograms are similar to the Bar Graph. “The histogram is a form of bar graph where the
heights of the bars show the number of observations in an interval or group of numerical values.
The intervals can be ten units wide, as they are here, or any other value the creator desires. The
width of the interval will depend on how spread out the data is and how many data are present.
There’s no hard and fast rule, but it’s best to pick something easy to read; ten is better then nine,
five is better than six” (Take It to The MAT, April/May 2004, Elementary Edition). In a
histogram the lengths of the bars is equivalent to the number of observations in each interval.
Batting Average for Players On the Team
0.0 - .999
3
.100 - .199
7
.200 - .299
14
.300 - .399
5
.400 - .499
0
.500+
1
Figure 6. Sample Frequency Table for Constructing a Histogram
It is important to note in the sample data provided in Figure #6, that there is NO DATA for the
400-499 range of data. When this occurs it is important that the graph accurately reflect that
there was not data. In essence a bar with a height of ZERO (0) would be graphed. It is NOT
appropriate to skip this data range as the axis scale goes from 0 – 500+ the lack of data in one
interval needs to be reflected. It is as important to know where data is, as it is to know where the
data IS NOT.
Batting Averages for Team Members
Batting Average
.500+
.400 - .499
.300 - .399
.200 - .299
.100 -.199
0.0 -.99
0
5
10
15
Frequency
Figure 7. Sample Histogram, “Batting Averages for Team Members”
Pie Chart/Circle Graphs
Pie charts, also know as circle graphs are used when the data collect is to be displayed as a
percentage. The pie chart is circular in nature with pie-shaped wedges representing the percent of
data with the greater the percent indicated by a larger slice. The full pie represents 100%, one
half the pie is 50% and so forth. An effective and easy way for student use, and practice with pie
charts is to practice with Dyna Zike-style paper circles.
1. Starting with several different colored sheets of paper trace a moderately large
circle (i.e. 10cm – 20cm in diameter) on the top sheet.
2. With the paper stacked, cut out the circle, taking precautions that the papers don’t
slip.
3. Once the circles are cut out, cut the circles along a radius line (or fold) you have
made on the circle.
4. Include as many circles/colors stacked together as the pie chart has wedges or
variables to be graphed.
5. Interlock the circles by inserting each of the circles into the slit of the other circle.
6. Start spinning or turning the second-to-top most circle, while holding the top one,
so it will not rotate.
As each consecutive circle is rotated, more or less of the top most circle is covered. Although
the exact percentage of the pie-shaped wedge will not be accurate, a close approximation will be
easy to find. If the students have difficulty spinning the correct amount, a simple exercise of
having the student start at 50% and then work their way up or down in percent until they get to
the desired amount. It is fairly quick and easy if the students spin half the total remaining
percentage at a time. For example, if the students need one wedge of the graph to be 14 percent.
Start with a full circle. Spin in the 2nd circle to cover 50% of the graph. Once 50% is covered the
students can split the remaining 50% in half and spin that in, so now 75% of the circle is the
original color, the remaining 25% is now the 2nd color. We are still not at the desired 14%, so we
split the remaining portion in half and spin that in. The wedge is now at 12.5% or so. To get the
desired 14 % simply turn the piece just a smidge more to make that wedge slightly larger. At
this point that wedge is very close to being accurate. Put one small drop of glue on the back of
the top piece. This will hold the circles in place. At this point another circle can be spun in, to
represent yet another pie shaped wedge.
Land Ownership in Nevada
12%
7%
0%
2%
1%
U.S. Dept of Agriculture
US Dept. of Interior
4%
US Dept of Defense
Us Dept. of Energy
Tribal Lands
Stat Lands
Local Government and Private
Lands
74%
Figure 8. Sample Pie Chart, “Land Ownership in Nevada”
Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and
claims in oral and written presentations. E/S
Common misconceptions associated with this benchmark.
1. Students commonly fail to follow conventions when constructing and using data tables.
Some of the more simple mistakes consist of the student not taking enough time to analyze the
experiment or survey and fully understand what data they will be collecting. This inadequate or
improper planning can result in the data table being improperly drawn and labeled, thus forcing
students to squeeze in extra column(s) or row(s) to accommodate the required data. Another
common mistake is to not place the independent variable in the far left-hand column. Although
there are not hard and fast rules, failure to follow this convention can lead to confusion when
setting up the resulting graph.
For a brief explanation on how to make a date table see:
http://findarticles.com/p/articles/mi_m1590/is_2_59/ai_99554833
2. Students make errors when constructing line graphs because they fail to follow
conventions associated with labeling axes, determining scale, and write non-descriptive
titles.
One of the most common mistake students make is to reverse the ordered pairs when graphing
the data. The 1st number in an ordered pair is the independent variable and thus would be plotted
on the x-axis. The second number in the ordered pair is the dependent variable and is plotted on
the y-axis. This mistake can be fairly easily avoided IF the data table and graphing conventions
are followed. If a student fails to follow those conventions, confusion is almost inevitable and
mistakes will be made.
There are a slew of common mistakes when it comes to the actual drawing and labeling of the
graph and its axes. The most common errors include;
1. Not setting a proper scale or interval. Students tend to choose intervals that are either
to small for the data set (i.e. the data range is 35 or 40 units, and they set the interval to 1
or 2 units, thus making a graph that has 15, 20, or even more interval marks). Or making
the interval to large for the data set (i.e. the data range is actually fairly tight say 10 – 15
units, but the numbers large, such as 105 – 120 units total). This mistakenly leads the
students to start the graph at the origin, and set intervals that are too large (20, 40, or 50
units), in so doing the data that gets plotted is “smashed” together in such a way that it is
very difficult to read the data points, and graph the data. One of the easiest solutions to
the problem is to place a line break in the “offending” axis so that the graph can focus on
the portion where the data is concentrated.
2. Axes accurately labeled. This mistake can consist of something as simple as not
putting the units to the variable (i.e. Labeling the axis Time without one of the
appropriate units such as: S, Min, Hour, Days, Years) or giving a generic and
uninformative label such as “DATA” or “Dependent Variable.”
3. Uninformative title. The graph is titled in such a way that the reader has no clue as to
what the graph is about. The classic example is “Line Graph” or simply “Graph.” One of
the simplest solutions to this is to have the students practice writing titles for graphs that
incorporate the independent and dependent variables so that the title is descriptive and to
the point.
Graphs which are plotted to visually skew the results, lead to a false impression of the data. This
is often done when two independent variables are being compared, and the results did not yield a
substantial difference between the two independent variables being compared. Pie charts are
particularly prone to this sort of false representation when a complete (100%) set of data is not
graphed.
3. Students omit columns or rows in histograms if no data was collected for the value range.
Histograms suffer from all the common errors associated with bar graphs, but have one that is
unique to them. This unique error consists of not leaving blank columns (or rows) for any data
frequency of zero. This error leads to reader misinterpretation and can be easily avoided, if the
students understand that the lack of data in one interval of data, is really a bar with a value of
zero that still needs to be plotted.
4. Students misrepresent data in a pie/circle graph by inaccurately drawing wedges or
failing to show data that equals 100%.
Along with not having an accurate or descriptive title or labels, circle graphs are prone to
misrepresentation if they are accidentally or intentionally mis-drawn. This usually occurs if the
pie wedges do not add up to 100%, or if the percentages add up, but the actual size of each
wedge is not draw to scale. Due diligence is a way to correct this issue. If using the paper cut out
circles to generate the pie graph, some degree of error is inherent and may be overlooked, if the
intent of the graph is to get the “big picture” and not the exact percentages.
Students may mistakenly hold the idea that the wedges or parts of Circle/Pie graphs do not need
to equal to 100%. This form of graph is specifically designed to illustrate the percent to whole
ratio of all the variables being compared.
Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and
claims in oral and written presentations. E/S
Sample Test Questions
Use the following data table to answer questions 1 and 2.
The Effect of Drop Height on Bounce Height of a Rubber Ball
Height of Drop
(cm)
Trial 1
Trial 2
Trial 3
5
4
3
2
Average Bounce
Height for all
Trials (cm)
3
10
6
6
5
5.6
15
11
12
11
11.3
20
13
14
14
13.7
25
16
15
16
15.7
30
21
20
21
20.7
Height of Bounce (cm)
1. What is the relationship of bounce height to drop height for the Rubber Ball? For any
given drop height the ball will bounce approximately
a. 45% as high as drop.
b. 50% as high as drop.
c. 65% as high as drop.
d. 78% as high as drop.
2. Based on the data table listed above which variable is the Independent Variable?
a. Height of Bounce
b. Height of Drop
c. Average Bounce Height
d. Trial 1
Use the following graph to answer questions 3 and 4.
Batting Averages for Team Members
Batting Average
.500+
.400 - .499
.300 - .399
.200 - .299
.100 -.199
0.0 -.99
0
5
10
15
Frequency
3. How many players on the team have a batting average between .300 - .399?
a. 1
b. 5
c. 7
d. 9
4. Approximately how many players are on the team?
a. 15
b. 25
c. 30
d. 35
Use the following graph to answer questions 5 and 6.
Bounce Height
(cm)
Effect of Drop Height on bounce height of a Rubber Ball
25
20
15
10
5
0
Trail 1
Trial 2
Trial 3
5
10
15
20
Drop Height (cm)
25
30
Average Bounce Height for
All Trials
5. If the same ball used in the experiment were to be dropped from a height of 40 cm what
would you expect the approximate height of the bounce to be?
a. 20 cm
b. 30 cm
c. 38 cm
d. 44 cm
6. If the ball were to be dropped from 18 cm, which of the following would be the best
approximation of its bounce height?
a. 8 cm
b. 10 cm
c. 13 cm
d. 18 cm
Use the following graph to answer questions 7 and 8.
Land Ownership in Nevada
12%
7%
0%
2%
1%
U.S. Dept of Agriculture
US Dept. of Interior
4%
US Dept of Defense
Us Dept. of Energy
Tribal Lands
State Lands
Local Government and Private
Lands
74%
7. Who is the 3rd largest land owner in the state of Nevada?
a. State Lands
b. US Dept of Agriculture
c. US Dept of Defense
d. US Dept of Interior
8. Approximately how much of the land in Nevada is owned by the federal government?
a. 74%
b. 12%
c. 87%
d. 2%
Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and
claims in oral and written presentations. E/S
Answers to Sample Test Questions
1. (c)
2. (b)
3. (b)
4. (c)
5. (b)
6. (c)
7. (b)
8. (c)
Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and
claims in oral and written presentations. E/S
Intervention Strategies and Resources
The following is a list of intervention strategies and resources that will facilitate student
understanding of this benchmark.
1. Kids Zone Learning with NCES: Create a Graph
The National Center for Education Statistics has an easy-to-use website in which students can
create a variety of graphs. The site provides students with sample line, bar, area, xy, and pie
graphs. It assists the students in a step-by-step process to construct a graph with their data.
To access the Create a Graph website, go to http://nces.ed.gov/nceskids/createagraph
2. Take It To the MAT Newsletters
A complete index of the TITTM Newsletters that address the finer points in Mathematics
Education can be located at the website below. These newsletters are separated into grade bands
(elementary, middle, high) and by the content areas addressed. Each newsletter is a one to two
page article that focuses on some of the subtleties of math education. The section on Data
Analysis and Probability is most applicable to this Science Benchmark.
To access the TITTM Newsletters go to: http://rpdp.net/math.php
A comprehensive packet on how to make and interpret charts, graphs and diagrams
This 4 page packet is designed to be a review of how to make and interpret charts, graphs and
diagrams. Each section starts off with the “How to” portion followed by several interpretation
questions of the graph or chart type in question.
To access this resource go to: http://rpdp.net/adm/show.php?type=science&lvl=High+School