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Graphs and diagrams - Knowledge Hypermarket. III

A visual representation of the relationship between quantities

Now let’s work with the “Cloudiness” column. Based on the available data, it is very difficult to say what kind of cloudiness prevailed in May. The situation is simplified if, based on the available information, we create an additional table in which we present the number of days with the same cloudiness:

A visual representation of the relationship between certain quantities is provided by diagrams. If the values being compared add up to 100%, then pie charts are used.

The chart below does not indicate the number of days with a particular cloudiness, but shows what percentage of the total number of days are days with a particular cloudiness.

Days with certain cloudiness have their own sector of the circle. The area of this sector relates to the area of the entire circle in the same way that the number of days with a certain cloudiness relates to the entire number of days in May. Therefore, if no numerical data is given at all on the pie chart, it will still give some approximate idea of the relationship between the values under consideration, in our case, days with different cloudiness.

A large number of sectors makes it difficult to perceive information in a pie chart. Therefore, a pie chart is generally not used for more than five or six data values. In our example, this difficulty can be overcome by reducing the number of cloudiness gradations: 0-30%, 40-60%, 70-80%, 90-100%.

One glance at this chart is enough to conclude that in May cloudy days predominated, and there were very few clear days. To provide greater clarity, we were forced to sacrifice accuracy. In many cases, bar charts can provide both clarity and accuracy of information.

The corresponding values are plotted on the vertical value axis. Neither the value axis nor the bars should have breaks: the chart is used for a more visual comparison, and the presence of breaks defeats the very purpose of presenting results in the form of a chart.

Using the diagram above, you can not only compare the number of days with a particular cloudiness, but also indicate exactly how many days of which cloudiness there were during the period under consideration.

Radar charts are special in that they have their own axis for each point in the data series. The axes originate from the center of the chart.

Let's sum it up

1. Using graphs and charts (pie, column and radar), we were able to visualize a large amount of the same type of tabular information.

2. The graphs allowed us to trace the processes of changes in temperature, humidity and pressure. Diagrams - compare the number of days with a particular cloudiness and build a wind rose.

3. To make the information presented in one table more visual, we used three graphs and three diagrams.

4. To ensure clarity, in some cases we had to sacrifice the accuracy of the information.

Thus, the choice of one or another type of information model depends on the purpose for which we are creating this model.

Questions and tasks

1. The result of a sudden impact on the human body of any environmental factor is called injury. Based on the diagram representing the structure of childhood injuries, create an appropriate verbal description. Back it up with real-life examples.

2. Data from the Ministry of Health of the Russian Federation on changes over ten years (1992-2001) in the structure of morbidity in children under 14 years of age are presented in a bar chart:

What can you tell by analyzing this diagram?

3. In one of the television talk shows, the host showed the following chart and said: “the chart shows that compared to 2004, the number of robberies has increased sharply in 2005.”

Do you agree with the journalist's conclusion based on this diagram?

Practical work No. 9
“Creating charts and graphs” (tasks 1 - 3)

Task 1. Blood groups

Construct a pie chart of the distribution of people by blood type, if people with blood type 0(I) in the world about 46%, with blood group A(II) about 34%, groups B(III) approximately 17%, and people with the rarest group AB(IV) only 3%.

1. Based on the available data, create the following table in Microsoft Excel:

2. Select the table and click on the button Chart Wizard toolbars Standard.

3. In the first window Masters select type (Circular) and view (Volume version of the pie chart). Using a button View result see what the diagram will look like. Then click on the button Further.

4. The second window displays the selected range of cells. Click the button Further.

5. On the tabs of the third window Masters set additional chart parameters:

Set a title Distribution of people by blood groups; place a legend (legend) at the bottom of the diagram; On the Data Labels tab, select Share; click on the button Further.

6. In the fourth window Masters indicate the position of the diagram: the name of the new sheet or the current sheet. Specify the placement of the diagram on the existing sheet and click on the button Ready.

7. Blood_types.

Task 2. Wood reserves

It is known that the area of the Russian Federation covered with forest vegetation is 7187 thousand km. The total timber reserve in our forests is 74.3 billion m. The table shows data on the areas occupied by the main forest-forming species in Russia and their timber reserves.

Based on the available data, it is necessary to present the shares of tree species by occupied area and wood reserves using pie charts.

1. Based on available data, create in program M Microsoft Excel the following table:

2. Calculate the missing values using the formulas:
В8=В9-ВЗ-В4-В5-В6-В7,
С8=С9-СЗ-С4-С5-С6-С7.

3. Create a Pie Chart “Share of tree species in the total forest area of Russia”. For this:

1) select a range of cells A2:B8;

2) on a new sheet, create a pie chart with the necessary additional parameters.

4. Create a Pie Chart “Share of tree species in all-Russian timber reserves”. For this:

3) moving the mouse while holding down a key (Ctrl), select non-adjacent ranges of cells A2:A8 and C2:C8;

4) create a pie chart with the additional parameters you need.

5. Save the result of your work in your own folder in a file named Our_forest.

Date: 02/17/2010

Class: 7

Subject: .

The purpose of the lesson: Learn to work with spreadsheets, build graphs and diagrams based on table data, and perform practical work.

Lesson objectives:

1. Educational: formation of information culture of students, discipline, perseverance, work culture, positive motivation of the educational process.

2.Developing: development of basic mental functions, general educational skills of algorithmic thinking. Development of skills in working with spreadsheets, application of acquired knowledge in practice.

3.Educational: Improve knowledge when working with spreadsheets, creating graphs and diagrams for visual ideas about the relationship between quantities, application of acquired knowledge in practice.

Equipment: Textbook by L. Bosova “Informatics”, computer

Lesson type: combined

During the classes

I. Organizational moment.

Hello guys, sit down. My name is Tatyana Sergeevna, and today’s lesson will be taught by me. The topic of our lesson today is “ Graphs charts. A visual representation of the relationship between quantities" The goal of our lesson is to learn how to work with spreadsheets, build graphs and diagrams based on table data, and perform practical work.

II Checking homework

1 . Why are graphs needed?

2. Why are diagrams needed?

3. What does the graph allow you to track?

III. Learning new material

A visual representation of the relationship between quantities

Cloud cover in May 2006

A visual representation of the relationship between certain quantities is provided by diagrams. If the compared quantities add up 100%, then they use pie charts.

The diagram (Fig. 2.14) does not indicate the number of days with a particular cloudiness, but shows what percentage of the total number of days are days with a particular cloudiness.

Cloud cover in May 2006

numerical data, it will still give some approximate idea of the relationship between the quantities under consideration, in our case, days with different cloudiness.

One look at the diagram in Fig. 2.15 is enough to conclude that in May cloudy days predominated and there were very few clear days. To provide greater clarity, we were forced to sacrifice accuracy. In many cases, it is possible to ensure both clarity and accuracy of information bar charts (Fig. 2.16).

Column charts consist of parallel rectangles (bars) of the same width. Each bar shows one type of qualitative data (for example, one cloud type) and is tied to some reference point on the horizontal axis - the category axis. In our case, the reference points on the category axis are fixed cloud values. The height of the columns is proportional to the values of the quantities being compared (for example, the number of days of a particular cloudiness). The corresponding values are plotted on the vertical value axis. Neither the value axis nor the bars should have breaks: the chart is used for a more visual comparison, and the presence of breaks defeats the very purpose of presenting results in the form of a chart.

According to the diagram in Fig. 2.16, you can not only compare the number of days with a particular cloudiness, but also indicate exactly how many days of which cloudiness there were during the period under consideration.

Radar charts are special in that they have their own axis for each point in the data series. The axes originate from the center of the chart.

Let's summarize:

1. Using graphs and charts (pie, column and radar), we were able to visualize a large amount of the same type of tabular information.

2. The graphs allowed us to trace the processes of changes in temperature, humidity and pressure. Diagrams - compare the number of days with a particular cloudiness and build a wind rose.

3. To make the information presented in one table more visual, we used three graphs and three diagrams.

4. To ensure clarity, in some cases we had to sacrifice the accuracy of the information. Thus, the choice of one or another type of information model depends on the purpose for which we are creating this model.

IV .Practical part.

Work 9. Create charts and graphs

Task 1. Blood groups

Construct a pie chart of the distribution of people by blood group, if people with blood type 0(1) in the world are about 46%, with blood type A(P) about 34%, group B(W) are approximately 17%, and people with the rarest group AB(IV) only 3%.

1. Based on the available data, create the following table in Micro soft Excel:

2.Select the table and click on the button Chart Wizard toolbars Standard.

3. In the first window of the Wizard, select the type (Circular) and view (Volume version of a pie chart). Using a button View result see what the diagram will look like. Then click on the button Further.

4. The second window will display the selected range of cells. Click the button Further.

5. On the tabs of the third window of the Wizard, set additional chart parameters:

Set a title Distribution of people by blood groups;

T place a legend (legend) at the bottom of the diagram;

On the tab Data Signatures select Share;

6. In the fourth window? The wizards indicate the position of the chart: the name of the new sheet or the current sheet. Specify the placement of the diagram on the existing sheet and click on the button Ready.

7. Save the result of your work in your own folder in a file named Blood Groups.

Task 2. Wood reserves

It is known that the area of the Russian Federation covered with gingival vegetation is 7187 thousand k:-m 2. The total supply of wood in our forests is 74.3 billion m3. The table provides data on the areas occupied by the main forest-forming species in Russia and their timber reserves.

Based on the available data, it is necessary to present the shares of tree species by occupied area and wood reserves using pie charts.

1. Based on the available data, create the following table in Micro soft Excel:

2. Calculate the missing values using the formulas: В8=В9-ВЗ-В4-В5-В6-В7, С8=С9-СЗ-С4-С5-С6-С7.

3. Create a pie chart “Share of tree species in the total forest area of Russia.” For this:

1) select the range of cells A2:B8;

2) on a new sheet, create a pie chart with the necessary additional parameters.

4. Create a pie chart “Share of tree species in total Russian timber reserves.” For this:

3) by moving the wash while holding down the Ctrl key, select non-adjacent ranges of cells A2:A8 and C2:C8;

4) create a pie chart with the additional parameters you need.

5. Save the result of your work in your own folder in a file named Our forest.

Task 3. Climate

1. Based on the information contained in § 2.9 of your textbook, build charts in Microsoft Excel:

1) cut circular “Clouds in May 2006”;

2) volumetric circular “Clouds in May 2006”;

3) the usual histogram “Cloudiness in May 2006”;

4) petal “Wind rose in May 2006”.

2. Save the result of your work in your own folder in a file named Climate.

V . Summing up the lesson

1. What can we do with graphs and charts?

2. What do graphs allow you to see?

3. What determines the choice of one or another type of information model?

VI.Homework

§ 2.9 pp86-89.

VII .Org.moment

This concludes our lesson, goodbye.

It is impossible to quickly and efficiently process large volumes of the same type of information presented in text form. It is much more convenient to process such information using tables. But the perception of bulky tables also turns out to be difficult for humans.

Let's say you're preparing for a school local history conference where you're assigned to draw a climate portrait of the month of May. Throughout the month, you collected information about air temperature, pressure, humidity, cloudiness, wind direction and speed. You entered the relevant information into a pre-prepared table, and this is what you got (Table 13).

Table 13
Weather in May 2012

Of course, you can draw this table onto a large sheet of Whatman paper and demonstrate this impressive result to your classmates. But will they be able to perceive this information, process it and form an idea of the weather in May? Most likely no.

You have collected a large amount of information, it is accurate, complete and reliable, but in tabular form it will not be interesting to listeners, since it is not at all visual. You can make the information contained in the table more visual and easy to understand (visualize information) using graphs and diagrams.

Visual representation of the processes of changing quantities

The graph shows two coordinate axes at right angles to each other. These axes are scales on which the represented values are plotted. One quantity is dependent from another - independent. The values of the independent quantity are usually plotted on the horizontal axis (OX-axis, or abscissa axis), and the dependent quantity - on the vertical axis (OY-axis, or ordinate axis). When the independent quantity changes, the dependent quantity changes. For example, air temperature (dependent variable) can change over time (independent variable). So the graph shows what happens to y as x changes. A graph displays values as curves, points, or both.

The graph allows you to track the dynamics of data changes. For example, using the data contained in the 2nd column of Table 13, you can build a graph of temperature changes during the month in question. Using the schedule, you can instantly set the warmest day of the month, the coldest day of the month, and quickly calculate the number of days when the air temperature exceeded twenty degrees or was around +15°C. You can also indicate periods when the air temperature was quite stable or, conversely, underwent significant fluctuations (Fig. 35).

Similar information will be provided by graphs of changes in air humidity and atmospheric pressure, which can be constructed based on the 3rd and 4th columns of the table.

Change in air temperature in May 2012

Rice. 35

A visual representation of the relationship between quantities

Now let's work with the Cloudiness column. Based on the available data, it is very difficult to say what kind of cloudiness prevailed in May. The situation is simplified if, based on the available information, we create an additional table in which we present the number of days with the same cloudiness (Table 14).

Table 14
Cloud cover in May 2012

A visual representation of the relationship between certain quantities is provided by diagrams. If the compared values add up to 100%, then use pie charts.

The diagram (Fig. 36) does not indicate the number of days with a certain cloudiness, but shows what percentage of the total number of days falls on days with a particular cloudiness.

Days with certain cloudiness have their own sector of the circle. The area of this sector relates to the area of the entire circle in the same way that the number of days with a certain cloudiness relates to the entire number of days in May. Therefore, if the pie chart does not show any numerical data at all, it will still give some approximate idea of the relationship between the values under consideration, in our case, days with different cloudiness.

Cloud cover in May 2012

Rice. 36

One look at the diagram in Fig. 37 is enough to conclude that in May cloudy days predominated and there were very few clear days. To provide greater clarity, we were forced to sacrifice accuracy.

Cloud cover in May 2012

Rice. 37

In many cases, it is possible to ensure both clarity and accuracy of information bar charts(Fig. 38).

Cloud cover in May 2012

Rice. 38

Column charts consist of parallel rectangles (bars) of the same width. Each bar shows one type of qualitative data (for example, one cloud type) and is tied to some reference point on the horizontal axis - category axes. In our case, the reference points on the category axis are fixed cloud values. The height of the columns is proportional to the values of the quantities being compared (for example, the number of days of a particular cloudiness). The corresponding values are plotted on the vertical value axes. Neither the value axis nor the bars should have breaks: the chart is used for a more visual comparison, and the presence of breaks defeats the very purpose of presenting results in the form of a chart.

According to the diagram in Fig. 38 you can not only compare the number of days with a particular cloudiness, but also indicate exactly how many days of which cloudiness there were during the period under consideration.

Table 15

Rice. 39

The radar chart is special; it has its own axis for each point in the data series. The axes originate from the center of the chart.

It is impossible to quickly and efficiently process large volumes of the same type of information presented in text form. It is much more convenient to process such information using tables.

But the perception of bulky tables also turns out to be difficult for humans.

Suppose you are preparing for a school geography conference at which you are assigned to draw a climate portrait of the month of June. Throughout the month, you collected information about air temperature, pressure, humidity, cloudiness, wind direction and speed.

You entered the relevant information into a table prepared in advance, and this is what you got (part of the table):

You have collected a large amount of information, it is accurate, complete and reliable, but in tabular form it will not be interesting to listeners, since it is not at all clear.

Visual representation of the processes of changing quantities

The graph shows two coordinate axes at right angles to each other. These axes are scales on which the represented values are plotted.

Pay attention!

One quantity is dependent on the other - independent. The values of the independent quantity are usually plotted on the horizontal axis (X-axis, or abscissa axis), and the dependent quantity - on the vertical axis (Y-axis, or ordinate axis). When the independent quantity changes, the dependent quantity changes.

For example, air temperature (dependent variable) can change over time (independent variable).

Thus, a graph shows what happens to Y as X changes. A graph shows values as curves, points, or both.

The graph allows you to track the dynamics of data changes. For example, using the data contained in the \(2\)th graph, you can construct a graph of temperature changes during the month in question.

Using the schedule, you can instantly set the warmest day of the month, the coldest day of the month, quickly calculate the number of days when the air temperature exceeded twenty degrees or was around \(+15 °C\).

You can also indicate periods when the air temperature was quite stable or, conversely, underwent significant fluctuations.

Similar information is provided by graphs of changes in air humidity and atmospheric pressure, constructed on the basis of the \(3\)th and \(4\)th columns of the table.

A visual representation of the relationship between quantities

A visual representation of the relationship between certain quantities is provided by diagrams. If the compared values add up to \(100\)%, then use pie charts.

The chart does not indicate the number of days with a particular cloudiness, but it does show what percentage of the total number of days are days with a particular cloudiness.

Days with certain cloudiness have their own sector of the circle. The area of this sector relates to the area of the entire circle in the same way that the number of days with a certain cloudiness relates to the entire number of days in June. Therefore, if the pie chart does not show any numerical data at all, it will still give some approximate idea of the relationship between the values under consideration, in our case, days with different cloudiness.

One glance at the chart is enough to conclude that in June there were predominantly clear days, and there were very few cloudy days. To provide greater clarity, we were forced to sacrifice accuracy. In many cases, it is possible to ensure both clarity and accuracy of information bar charts.

In our case, the reference points on the category axis are fixed cloud values.

The height of the columns is proportional to the values of the quantities being compared (for example, the number of days of a particular cloudiness).

The corresponding values are plotted on the vertical value axis.

Neither the value axis nor the bars should have breaks: the chart is used for a more visual comparison, and the presence of breaks defeats the very purpose of presenting results in the form of a chart.

Radar chart special, it has its own axis for each point in the data series. The axes originate from the center of the chart.

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