Question 23M.2.SL.TZ1.1
| Date | May 2023 | Marks available | [Maximum mark: 13] | Reference code | 23M.2.SL.TZ1.1 |
| Level | SL | Paper | 2 | Time zone | TZ1 |
| Command term | Compare and contrast, Distinguish, Identify, State, Suggest | Question number | 1 | Adapted from | N/A |
Smartphone data from more than 700 000 individuals in 111 countries was used to estimate their activity levels. Data from more than 68 million days of activity was analysed, including the numbers of steps taken per day. The graph shows the distribution of numbers of steps per day for four countries.
[Source: Material from: Althoff, T., Sosič, R., Hicks, J., et al., Large-scale physical activity data reveal
worldwide activity inequality, published 2017, Nature, reproduced with permission of SNCSC.]
State the mode for the number of steps per day in Japan and USA, rounding your answers up or down to the nearest 1000 steps.
Japan: ..................................................................................................
USA: ....................................................................................................
[1]
Japan: 6000 and USA: 4000; (both needed)
Most candidates successfully answered parts (c), (g), (e) and (a). In (e), there were some surprising errors in understanding the data. For example, candidates stated such things as, "females took fewer steps than males" which showed complete lack of understanding of the data.
Distinguish between the distribution of activity in Saudi Arabia and the UK.
[2]
- higher mode for the number of steps in UK/4000 versus 3000 in Saudi Arabia;
- UK has more variation/is more spread out/greater standard deviation
OR
UK has a more normal distribution; - people in UK take more steps than people in Saudi Arabia;
Many candidates were successful on (b). Those who were unsuccessful failed to understand the question which asked about distribution of data rather than relative probability.
Walkability is a measure of how friendly an urban area is for walking. The researchers determined a walkability score for cities in the USA, based on such measures as block length, availability of sidewalks and distances between homes and destinations such as shops, workplaces or parks. They also calculated a coefficient of activity inequality for each city from the variation among individuals in number of steps per day. A coefficient of zero would indicate that all individuals took the same number of steps. The scattergraph shows the relationship between walkability and activity inequality for the 69 cities where smartphone data was available for at least 200 individuals.
[Source: Material from: Althoff, T., Sosič, R., Hicks, J., et al., Large-scale physical activity data reveal
worldwide activity inequality, published 2017, Nature, reproduced with permission of SNCSC.]
Identify the city with the highest and the city with the lowest walkability.
Highest: .............................................................................................
Lowest: ..............................................................................................
[1]
highest: New York and lowest: Charlotte; (both needed)
Suggest reasons for the relationship shown in the graph.
[2]
- majority of individuals are active/walk to places if walkability is high;
OR
high walkability encourages the habit of walking so the coefficient ofactivity inequality would be low; - with low walkability some individuals take exercise/go jogging and some do not;
- (with high walkability) people don’t need to drive increasing the incentive to walk;
Accept reasonable answer.
(d) was one of the most difficult questions on the exam because the relationship on the graph was complex. Many repeated the data without suggesting a reason.
Combining the data for all countries, including the body mass index (BMI) of each individual, the researchers grouped males and females according to their mean number of steps per day. Using the BMI of each individual, they calculated the percentage of males and females who were obese (BMI over 30) for each of these groups. The chart shows the data.
[Source: Material from: Althoff, T., Sosič, R., Hicks, J., et al., Large-scale physical activity data reveal
worldwide activity inequality, published 2017, Nature, reproduced with permission of SNCSC.]
Compare and contrast the data in the chart for males and females.
[2]
Similarities (Compare)
a. lower percentage of obesity with more steps per day in both males and females
OR
percentage obesity is most similar at 1000 steps
OR
correlation of steps to percent obesity plateaus after 8000 steps for both males and females;
Differences (Contrast)
b. the range difference of obesity percentage among different steps is bigger in females (9% - 31% versus 18% to 30%)
OR
walking has a greater impact on lowering obesity rates in females than males
OR
men show a greater percentage of obesity
OR
at 1000 steps per day there are more obese women than men;
One similarity and one difference required for two marks.
Suggest two hypotheses to account for the relationship between the mean number of steps per day and the proportion of people who are obese.
Hypothesis 1: ...........................................................................................................
...............................................................................................................................
Hypothesis 2: ...........................................................................................................
...............................................................................................................................
[2]
(any order)
- obesity causes people to be less active/take fewer steps;
- people who are less active/take fewer steps (are more likely to) become obese;
- People who are not obese tend to have healthier habits, including walking more;
Accept hypothesis and a null hypothesis for the two.
(f) showed some understanding of a hypothesis drawn from data, but many did not read the question closely enough. So, they stated hypotheses related to male-female differences. It was expected that the candidate write more information than just repeat the correlation. They needed a justification for the hypothesis. Credit was given for a correctly stated null hypothesis.
The scattergraph shows the coefficient of activity inequality and the percentage of the population that is obese in the 46 countries or regions for which data was available.
[Source: Material from: Althoff, T., Sosič, R., Hicks, J., et al., Large-scale physical activity data reveal
worldwide activity inequality, published 2017, Nature, reproduced with permission of SNCSC.]
State the relationship between activity inequality and obesity shown in the scattergraph.
[1]
as activity inequality rises percentage obesity rises
OR
Positive/direct correlation/relationship;
Using only evidence from the data in Question 1, suggest two strategies for reducing obesity in countries where this health problem is most prevalent.
[2]
- use public education to encourage people to walk more/become more active;
- improve city design to improve walkability;
- reduce distances between homes / shops / workplaces / parks;
- more sidewalks / make it easier for pedestrians to cross roads / other specific measure;
Do not accept answers that involve diet as that is beyond the scope of the data given.
(h) was quite difficult as it required a sophisticated interpretation of the data in the graphs. Candidates had problems making suggestions based on the evidence provided for h. They were able to think about other reasons.