Explanation:

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Explanation:

While creating Bar in bar chart, we turn off the stacked marks so that both the bars will start from 0.

Explanation:

Drop Sector to columns and Funded Amount to Rows.

From Analytics, drop the Median with Quartiles to your view or drop the reference line, select the Median.

Your view will look like this:

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Here, the Clothing funded amount is exactly similar to the Median.

Explanation:

Drop the Latitude and Longitude fields to Rows and Columns shelf respectively. Or double click on these fields.

Drop the Location field to Filter shelf, select the Location as Colombia.

Drop the Name field to the Label.

To show the cities go to the Map -> Map layer and Check the Cities.

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Drop the Deaths and Year fields to the Tooltip. Now your view looks like this:

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Zoom-in the map, you will find Purace is the nearest one in the Popayán city and Dona Juana was having 55 deaths.

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In WorldCupMatches table, we have the information of Away goals, and in the WorldCups

table, we have the information of the Winner team and goal scored.

In WorldCups, there is only row for each year. But in WorldCupMatches, there is a separate

row for each match. This is the perfect example to use blend as here we need to first

aggregate the data from WorldCupMatches table.

Let’s establish the blend relationship between these two data sources, on the basis of Country

and Year.

Let’s use WorldCups as the primary data source, so add the field from this data source to the

view. Drop Year to Rows and change it discrete. Drop Country and Winner field as well to the

rows.

In WorldCupMatches data source, create a calculated field :

Drop this field to the Text, and format it so that it will be shown in percentage.

Out of the given options, only in 1934 host country won the world cup

2010 is the correct answer.

Create a bar chart using field college and Math. Change the Math aggregation to AVG.

Drop the Verbal field to Y axis, to create the combined axis chart. And change the aggregation

to Avg.

Follow the same for Writing field.

Now from the Analytics menu, drop the Distribution band to the view, select the scope as

entire table, and select the value as 70 percentiles.

Your view will look like this:

70 percentiles are 547.8

Business and public affairs have all the individual subject values lesser than 547.8.

Refer:https://help.tableau.com/current/pro/desktop/en-us/dashboards_dsd_create.htm

Go to the section- Phone layouts and the Default dashboard

Open Regional Dashboard twbx file, and select the region as Northeast and state as New York:

Hover the trend line of Obesity correlation with exercise chart, 0.57 percentage of average

obesity increases when 1% average increase in the physical inactivity in theNortheast Region.

However, we have to find out for the New York State. Currently, when you have selected the

New York state, the trend lines have not been recalculated.

To recalculate it, right-click on the trend line and click on Edit All Trend Lines:

In Trend Line Options, select the Show recalculated line check box:

Hover the trend line of Obesity correlation with exercise chart, 0.40 percentage of average

obesity increases when 1% average increase in the physical inactivity in theNew York Statein

the Northeast Region.

Bar-in-Bar Chart and bullet graphs both are used to compare the measures against goals.

Select the measure “Death” and go to the Show Me and Click on Histogram

When we are binning the measure, it will create the dimension, so you get a new dimension as Death (bin). Right-click on this and click on Edit. Update the size of bins to 500.

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Drop the Country to the color, so our view will look like this:

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Now drop the Country to the filter and select 4 countries mentioned in our options.

Hover the bar, it shows the United States has reported 9 deaths.

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Explanation:

Add the Country population excel sheet as a New Data Source.

Select column 1960 to 2013, click on Pivot.

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Rename the new fields as Year and population.

Go to Data ->Edit Blend Relationship

Click on Custom and Add

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Ensure the blend relationship exists in Country & Country Name, and year & year between primary and secondary data sources.

Drop the year to the filters shelf and only enter 1963.

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Drop Country and Number of Records / volerup(Count) field to the view.

And drop population to the view from the secondary data source. Your view will look like this:

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Explanation:

First, find the distinct sports for each country:

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It will be created as a Measure, move this to Dimension

Get the distinct countries:

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drop these columns to the view, final view will look like this:

The X-axis represents a number of distinct sports. As the data set contains data from 2000 to 2012, so even if you don't include the filter, your answer would remain the same.

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Explanation:

Drop the Age to the columns and change the aggregation to AVG.

Drop Gold Medals field to filter and select All values ->

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Change 1 from 0.

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