# data[].attributes.body
Linh

The cases are the six regions of the world shown in the pie charts:

1. North America

2. South America

3. Europe

4. Africa

5. Central Asia

6. South East Asia

The variable is the percentage of water used for different purposes in each region.

This is a categorical variable (qualitative) since it categorizes water use into three groups:

Agricultural use (striped section)

Industrial use (white section)

Domestic use (black section)

Most allocated to: Agricultural use (highest percentage across all regions).

Least allocated to: Domestic use (lowest percentage across all regions).

Central Asia (88%) has the highest percentage of water used for agriculture.

Other high percentages: Africa (84%), South East Asia (81%), and South America (71%).

Europe (53%) has the highest percentage of water used for industrial purposes.

North America (48%) also has a significant amount allocated to industry.

5, Im not sure yet

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Thành

1. What are the cases in this graph?

North America

South America

Europe

Africa

Central Asia

Southeast Asia

2.What are the variables?

Industrial use

Agricultural use

Domestic use

3. In total, water is most/least allocated to which purpose?

Most allocated: Agricultural use

Least allocated: Domestic use

4. In which continent(s) is water used mostly for agricultural purposes?

Africa (84%)

Central Asia (88%)

South America (71%)

5. In which continent(s) is water used majorly for industrial purposes?

North America (48%)

Europe (53%)

# data[].attributes.body
MD

1. The cases refer to the six regions depicted in the chart:

North America

South America

Europe

Africa

Central Asia

South East Asia

Each case represents a geographical region and its respective distribution of water usage.

2.

The variable here is the percentage of water use, categorized into three types:

Industrial use

Agricultural use

Domestic use

3.

Most allocated: Agricultural use, which is the dominant category in most regions (e.g., 88% in Central Asia, 84% in Africa).

Least allocated: Domestic use, as it has the smallest proportion across all regions (e.g., 5% in Central Asia, 7% in Africa).

4.

Water is mostly allocated to agriculture in:

Central Asia (88%)

Africa (84%)

South East Asia (81%)

5.

Water is mostly allocated to industry in:

North America (48%)

Europe (53%)

6.

There are two main ways to approach the analysis:

1. By continent: Examining how water is distributed across each region.

2. By purpose: Comparing how different continents allocate water for agriculture, industry, and domestic use.

Preferred approach: I would focus on water use by purpose, as it allows for better comparison across different regions and highlights global trends.

# data[].attributes.body
nhi

cases: purposes of water used in different continents

variables: the proportion of water for each purpose

most: agriculture

least: domestic use

europe

purposes

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HLN Trân

-Cases: people in six areas of the world

-Variables: area, the purpose of consuming water

-

+Agriculture is the most popular use of water

+The number for people consume water for domestic use is the least

-The figure for water used for agriculture in Asia is the highest

-Europe majorly use water for industry

-purposes

# data[].attributes.body
Gia Thinh

1. cases: different purposes of water usage.

2. variables: six areas of the world, different purposes in water usage

3. In total, water is most allocated to agricultural use; the least goes to domestic use

4. Central Asia

5. Europe

6. Purposes

# data[].attributes.body
1.cases: six areas of the world

2. variables: different areas, the percentage of water used for different purposes

3. most: agricultural use

least: domestic use

4.central asia

5. Europe

6. direction: purposes

# data[].attributes.body
Ngọc Ánh

1. Cases: six areas of the world

2. Variable: percentage of water used for different purposes

3. Most allocated: Agricultural use

Least allocated: Domestic use

4. In central Asia, water is mostly used for agricultural purpose.

5. Europe majorly use water for industrial purpose

6. I will choose purposes as it's easier to group them (e.g: Water contributed mostly to agriculture use in SA, AFR, CA, SEA. (will be demonstrated more scrupulously in the writing)

# data[].attributes.body
1. NA, SA, Europe, Africa, CA, SEA

2. the purposes and six mentioned continents

3. Most : agricultural use

Least : domestic use

4. Africa, Central Asia, SEA

5. NA, Europe

6. comparing the water usage purposes across the different continents

# data[].attributes.body
Anh Chi

1. Cases: purposes of water usage in 6 continents

2. Variables:

Three purposes:

Six continents:

3. Most: agriculture / least: domestic use

4. Central Africa

5. Europe

6. Continents

# data[].attributes.body
mèo:

case: 15 students in a NEC foundation class

the amount of time on screen on average

the percentages of students who fall into screen time categories

one-way table, bar chart

# data[].attributes.body
Nguyên

1. 15 Students in a NEC Foundation class

2. Averarage screen time per week (categorical)

3. Illustrates the percentage of screentime students spent per day in a week

4. one way table/bar chart

# data[].attributes.body
Plinh

1,The cases in this survey are the 15 students in the NEC Foundation class who participated in the study.

2,The variable is the average screen time per day of each student over 7 days.

This is a quantitative (numerical) variable because it represents a measurable amount of time.

3,The percentages likely represent the proportion of students who fall into different screen time categories

It shows how the students’ screen time is distributed across different time ranges.

4,The data can be represented in several other ways, such as:

A frequency table

A pie chart

# data[].attributes.body
Minh Đức

1.

The cases in this survey are the 15 students in the NEC Foundation class.

2.

The variable in this survey is “average screen time per day” from the last week.

It is a categorical variable (qualitative), as the responses are divided into distinct categories (e.g., “1-2 hours”, “3-4 hours”, etc.) rather than numerical values on a continuous scale.

3.

The percentages represent the proportion of students who fall into each screen time category.

For example, if “More than 6 hours” is 26.7%, it means 26.7% of the 15 students reported an average screen time of more than 6 hours per day.

4.

Yes, there are multiple ways to present this data:

Bar Chart: A bar chart could show the frequency of students in each screen time category more clearly.

Table Format: Listing each category with the number of students and their respective percentages.

Histogram: If the data were numerical, a histogram could be used, but since this is categorical, a bar chart is more suitable.

Line Graph: If screen time were tracked over multiple weeks, a line graph could show trends over time.

# data[].attributes.body
Gia Thinh

cases: 15 students in a NEC foundation class quantitative variables: the hours per day in 7 days on students' screentime the percentage of different ranges of hours which took up the overall graph one-way table

# data[].attributes.body
Tú

1. Cases: 15 students in a NEC Foundation class.

2. Variable: The amount of time spent sorted into different groups, which is categorical variable.

3. The percentage displayed indicate the ratio of categories sorted by time spent on screen.

4. Since average time on screen is the only variable, we can use normal bar graph, line graph and one-way table to redemonstrate the data alternatively.

# data[].attributes.body
Đức Hưng

1:The cases are 15 students in a NEC foundation class

2:hours per day are on last week's Screen time

3:The percentages show that the average screen time per day in 7 days

4:idk

# data[].attributes.body
Thảo Nguyên

1. cases: 15 students in a NEC Foundation class

2. variable: the average Screen Time per day in 7 days->categorical variable

3. the percentages on the graph present the proportion of students who reported spending a certain amount of time on screen daily

4. other ways: one-way table, bar chart

# data[].attributes.body
1. The cases in this survey are the 15 students in the NEC Foundation class.

2. The variable is : How many hours per day are on your last week's Screen Time? This is a categorical variable.

3. representing the proportion of the 15 students who fall into each specified screen time range.

4. one-way table / bar chart (maybe)

# data[].attributes.body
Ngọc Ánh

1. Cases is 15 students in a NEC Foundation class.

2. Variable is the average Screen Time per day in 7 days. It is quantitative variable.

3. The percentage demonstrates the time spent per day in 7 day.

4. Yes. Other ways are one-way table, normal bar graph, line graph.

# data[].attributes.body
Thành

1. Students in a NEC class

2. - Variable is hours of screen time per day in the last week

- Categorical Variable

3. Proportion of students who selected each category of screen time usage.

4. Bar chart

# data[].attributes.body
nhi

cases: 15 students in nec foundation class

variable: hours of screen time per day in the last week (categorical )

percentages on the graph represents the proportion of students who selected each time category

yes: bar chart, one-way table

# data[].attributes.body
Anh Chi

1. The case is 15 students in a NEC Foundation class

2. - Variables: Average screen time per week

- Type: categorical

3. It shows the portion of choice (that choice/100)

4. one-way table (idk)

# data[].attributes.body
Kinh Thien

case: 15 students in NEC foundation class

variables: the number of hours spent

the proportion of students' time allocating for screen

other way: one-way table

# data[].attributes.body
HLN Trân

-Cases: 15 students in a NEC Foundation class

-Variable: categorical variable: the amount of time that they spent on screen per day last week

-The percentages present for the proportion of students and how many times they spent on screen

-Other way: one-way table

# data[].attributes.body
mèo

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