The table lists the percentage of different household types in Canada from 1984 to 2020. Two main types of household are one person and one family household. The latter is divided into two categories: couple (no children, dependent children and non-dependent children) and lone parent.

Overall, the percentage of one-family household was much higher than that of one person household. Regarding couple households, there was a decrease in the percentage of dependent children couple in contrast to a rise for no children one.

In detail, between 1984 and 2020 one person doubled its value from 6% to 12%. Of all three types of couple, non-dependent children couple had relatively the same percentage over the period shown, while the value for dependent children dropped by 16% from 52% in 1984. There was an increase from 19% to 25% in the percentage of couple having no children. Lone parent showed the fastest change, with three times compared to its value in 1984.

The table lists the percentage of different household types in Canada *(incorrect) *from 1984 to 2020. *(This is not a line graph. There is no continuous data) *Two main types of household are one person and one family household. The latter is divided into two categories: couple (no children, dependent children and non-dependent children) and lone parent.

Overall, the percentage of one-family household *(incorrect) *was much higher than that of one person household. Regarding couple households, there was a decrease in the percentage of dependent children couple in contrast to a rise for no children one.

In detail, between 1984 and 2020 one person doubled its value *(incorrect) *from 6% to 12%. Of all three types of couple, non-dependent children couple had relatively the same percentage over the period shown, while the value for dependent children dropped by 16% from 52% in 1984. There was an increase from 19% to 25% in the percentage of couple having no children. Lone parent showed the fastest change, with three times compared to its value in 1984.

Most of your writing had incorrect expressions.

First, it is discrete time points, not continuous data.

You did not describe correctly what is being measured and counted. It is not household types.

So these expressions are all incorrect:

- percentages of one-family households.
- percentages of dependent children couple.
- percentages of household types.

I will try to explain in the next reply.

How to understand the table.

Let's suppose that this glass jar filled with jellybeans represents Canada. Each jellybean in the jar is one Canadian. If we count all the jellybeans in the jar, we get the total population.

But note that the jellybeans are different colors. The __color__ represents the type of family that the jellybean (person) belongs to. We can sort the jellybeans in different piles, according to the color. The number in each pile divided by the total population will be the percentage of people in that category.

Green: One person

Blue: Couples with no children

Yellow: Couples with dependent children

Orange: Couples with non-dependent Children

Purple: Single Parents

Red: Those without a designation (remainder)

So in this task we are counting __people__, and putting them into their family unit categories.

What percentage of all the jellybeans are in each pile? There are six piles: a pile of green jellybeans, yellow jellybeans (etc.)

That is what is entered in the table.

For example, suppose there are a total of 100 jellybeans in the jar. The year is 1984. Look at the entries in the table under 1984.

We count 6 green jellybeans, that is 6%. There are 6 out of 100 (or 6% of Canadians) that are in one-person households.

We count 52 yellow jellybeans. There are 52 people out of 100 or 52% of Canadians who are in a family with dependent children.

We count 19 blue jellybeans. There are 19 people out of 100 in Canada who are in families which are a couple with no children.

If we count all the yellow, blue, and orange jellybeans, that totals 81 jellybeans, or 81% of Canadians. These are the people in families headed by a couple.

If we add all the percentages in the table it is 6+19+52+10+4 = 91. That accounts for 91% or 91 out of the 100. That means there are 9 which have not been included in the table. We have no information about what kind of household these people belong to. These are in the red jellybean pile.

Of course, the size of each pile will be different each year the totals are added up. Some will increase in size, and some will decrease.

So what is the opening paragraph? Here is an example with the "overall" body paragraph.

The table lists the percentages of Canadians by the type of household they belong to in five different years: 1984, 1994, 2004, 2014 and 2020. There are two main types of household: single person and family. The family household is subdivided into two categories: headed by a couple (no children, dependent children and non-dependent children) and single parent.

Overall, the percentages were much higher for those in families than one-person households. The one-person household and single parent household categories grew relatively larger over time whereas the households of couples with dependent children decreased in size. Between 3 and 9 percent of the population were not accounted for in each of these time points.

Thank you so much for your intricate comments. I actually learned a lot from that.

hellosky1234Thank you so much for your intricate comments. I actually learned a lot from that.

I was a little worried that it would not be understood. Did you understand why your expressions were not correct? Did the illustration help?

Absolutely yes. Most of my writing task 1 essays were influenced by your writing style eventhough my skills were not really good. I would say that you are such a dedicated teacher. Many thanks!

Sorry to bother you AlpheccaStars, but I have one question:

AlpheccaStarsIf we add all the percentages in the table it is 6+19+52+10+4 = 91. That accounts for 91% or 91 out of the 100.

Is it necessary to do the calculation? Because I thought task 1 is supposed to report the main features and make comparison?

precautionIs it necessary to do the calculation?

According to some examiners, doing some simple primary school arithmetic can jump your score to the highest level.

For example, one main feature I often use is the total increase of different categories over a time period. If there is no "total" on the graph, you have to do sums to get it.

In this case, it is interesting that some data was missing. That can be a "main feature".