Vaccination Investigation !!!

    A linear model is a graph in which all the data points are plotted in a scatter plot and a line of best fit is included. This line has a slope (m) and a y-intercept (b) that illustrate the general trend of the data points. If the independent variable is doubled, the dependent variable will be twice the independent variable plus the y-intercept.

    A proportional model is similar to a linear model because it also is a scatter plot that includes a line of best fit. What distinguishes a proportional model from a linear model is the fact that the line of best fit starts from the point (0,0). This means there is no y-intercept which in turn means that there is a direct proportional relationship between the data points and the line of best fit. There is a proportionality constant (A) that when multiplied by any of the independent variables will give you a value for the dependent variable. Here if the independent variable is doubled, the dependent variable will also double.

    An inverse proportional model has a curved line that has the least amount of error between the data points. For this model, a proportionality constant is the constant that is multiplied by the inverse of the independent variable. If the independent variable is doubled, the dependent variable will be halved.
    The slope of the Pressure vs. 1/Adjusted Volume graph is positive and is also a proportional model. On the average, the pressure increases by 1109 kPa for every inverse milliliter. On this graph, 0 mL would be where the pressure is infinite. This is because originally, pressure and volume have an inverse relationship where if one is increased, the other decreases. For the pressure vs inverse volume graph, we manipulated the graph so that these two variables can have a proportional relationship. The actual data never change, so a 0 volume will still correspond to an infinite pressure. 


Comments

Post a Comment

Popular posts from this blog

Reflection Post 2

     Designing Friday's experiment was very interesting and informative. Thinking everything through and making sure that each of us where on the same page was crucial to the design of our experiment. We had to be particular about our investigation in order to accurately replicate it, as well as have other scientists do the same. We distinguished independent and dependent variables in order to clearly indicate what we are measuring and any relevant variables that can't be changed. Establishing the control variables is very important because it is another indicator to compare one's results of their experiment with. Without it, the results can be quite contradictory or ineffective.      The investigation we designed is science because we are measuring a natural phenomenon that can also be easily replicated. It is like a science because what we are ultimately studying is the effect gravity has on the velocity of a small object over a short distance. The result...
Read more

Reflection 1: Umbrellaology

     Science is the study of nature and the universe around us, which can be further broken down into numerous branches. Such examples include Astrobiology, Chemistry, Physics, and others.      Umbrellaology is not a science. It doesn't explain the universe, nor are there any groundbreaking discoveries waiting to be made in this field. This idea is more closely related to Psychology as it seeks to explain correlation, likelihood, and statistical data among people and what type of umbrella(s) they own.     The difference between Umbrellaology and Zoology for example is that Zoology explains the organisms on our planet in its entirety. In Zoology, there are plenty of unknown phenomena as well as species that haven't been discovered yet. Also, organisms have been around for billions of years, have had significant impact on our world, and will most likely continue to do so for billions of years to come. That isn't the case with Umbrellaology. Umbrella...
Read more