We will collect and analyze statistical data related to our growth and health.
Prior to beginning this investigation, define the terms listed below. Be sure to use these words correctly while you discuss this investigation with your classmates.
One way math can be used in our health is to measure dimensions of the body and limbs. Common body dimensions that are measured include:
Once we understand these measurements, we can look at why these measurements can be important and how these measurements relate to each other and to other major factors like age and gender.
Also known as reach, it is the measurement of the length of a person’s arms, fingertips to fingertips.
Is measured as the distance between your hip line and the bottom of your feet. The hip line is located at the widest part of a person’s hips.
A person’s height is measured from the top of their head to the bottom of their feet, when standing.
The measurement of a person's natural pace, it is the distance between the heel of a person’s footprint and the heel of the same footprint two steps on.
Information about these measurements is published by various sources, such as the Australian Bureau of Statistics (ABS) and medical publications.
One way of understanding these measurements is by plotting ordered pairs onto a scatter plot. This makes it easier to recognize patterns in the data, especially whether or not these patterns appear to be linear.
Linear patterns reveal whether or not two measurements are connected to each other. For example, there is a linear relationship between a person’s height and arm span because, in general, as a person’s height increases so does their arm span.
This linear relationship can be seen through close and consistent grouping in a scatter plot.
If there is not a linear relationship between two pairs of data then the scatter plot shows a more random distribution. This is important to know because the presence of a linear pattern signals that the two sets of data correlate. However, it is equally important to note that correlation doesn't necessarily indicate a "causal link", but we'll look at this further on.
Reflect on the graphs below. Giving reasons, identify whether the correlation coefficient is positive, negative or zero in the following plots and describe how strong the relationship is (perfect, strong, weak, none):
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantitative variables. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.