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  • Unknown Member

    Member
    September 11, 2021 at 10:41 PM

    The presented graph compares (car) thefts per thousand vehicles in Great Britain, France, Sweden and Canada for 10 years period from 1990 to 1999. It can be clearly seen that, throughout the period, in Sweden, the numbers of car theft (number of car thefts) was rising whereas the rates (rate) of theft in Great Britain, France and Canada had some oscillation (fluctuation).

    According to the graph, in 1990, the number of car theft of the (not required) France and Canada were nearly 6000 (6) and 7000 (7) respectively, whilst in Great Britain, the number was almost (more than)double than these countries in the same year. Over a decade, with some fluctuations in numbers of car theft in these three countries, Great Britain remained at the top with approximately 14000 (14) while Canada and France were at the bottom with about 50% (less) than that of Great Britain.

    On the other hand, though Sweden had about 1000 (1)more car theft than Canada in 1990, it experienced rapid surge over the years and reached 1.5 times more increment in the end of the period.

    Total words: 167

    Observations:

    Pls see the inaccuracies in bold above with corrections in the brackets.

    1. Per 1000 = the number itself

    2. In 1000s = in units of thousands

    3. So all the numbers are in single numbers and not thousands.

    4. numbers of car theft – ( number of car thefts )

    5. rates of theft – rate of theft

    6. almost double – almost more than double

    Feedback:

    It is well structured and organized though the numeric data deciphered from the graph is inaccurate. A good effort has been made to answer the question and describe the main points in the charts though the grouping could have been varied for a better understanding and comparision. Range and vocabulary are satisfactory with good use of language of variation. Minor Grammar errors of singular/plural are seen which does not detract the flow of the writing.

    Band Score : 1.5 / 3 ( only because of wrong data)

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