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In our example, the population is increasing. When lambda is 1 the population remains stable. When it is below 1 the population is decreasing, and when lambda is above 1 the population is increasing. The table shows that the population is increasing. Looking at Figure 10.8 B we see that the population size is increasing. From year 1 to year 5 the population increased by 1037.3 individuals. Our lambda increased by .21 from year 2 to year 5. From year 2 to the next year was the biggest increase in lambda. After the first initial jump lambda continued to increase just by smaller increments every year. If we continued the table, we would notice that lambda would eventually reach a constant assuming our survival and fecundity rates remain constant.

To test their hypothesis, the experimenters used mice. Some mice had the ob/ob gene, some were diabetic, and others were wildtype. They also had mice of different ages in which they tested.  The experimenters then injected some mice with OB protein daily, some mice with saline, and some mice with zero injections. They then recorded information from the experiment such as how much the mice ate and their body weight.

The administration of the protein caused a reduction in the mice's body weight and adipose storage.  In addition, the mice also ate less. The body weight of the ob/ob was down 40% after 33 days and significantly different after only four days. The wild-type mice also had a reduction in body weight, body fat, and amount consumed. For the mice with diabetes, the injection did not cause a significant reduction in body weight or amount of food consumed, although they did lose some weight. 

In our example the population is increasing. When lambda is 1 the population remains stable. When it is below 1 the population is decreasing, and when lambda is above 1 the population is increasing. So in our table we know that the population is increasing. Looking at Figure 10.8 B we see that the population size is growing. From year 1 to year 5 it increased by 1037.3 individuals. Our lambda increased by .21 from year 2 to year 5. From year 2 to the next year was the biggest increase in lambda. After the first initial jump lambda continued to increase just by less every year. If we continued our table, we would notice that lambda would eventually reach a constant assuming our survival and fecundity rates remain constant.

One of the most important aspects of the science community is the ability to replicate processes and get the same results. In order to be able to achieve this goal, clear and concise writing is required. In Fall 2018 the Writing in Biology Class at the University of Massachusetts Amherst, conducted a project to observe differences between two figures. Figure 1 was to be created by me and Figure 2 was to be created by another student following my methods section to try to create a perfect replicate of the Figure I already created. The following section focuses on the subject of the figure, including why it was selected, and the factors that were attempted to be controlled in the writing of the methods section

  In this example lambda is increasing over time. From year 2 to year 5, it increases by .21. This shows that the population is increasing. This follows what the table also shows in the other columns. For example, looking at Figure 10.8 B we see that the population size is growing. From year 1 to year 5, it increased by 1037.3 individuals. Over the years that we looked at there was a big increase at first, and then lambda started to increase by a little. If we continued the graph we would eventually reach a point that lambda remains constant- assuming our survival and fecundity rates maintain constant. 

Introductory Statistics is a course that all UMass biology majors must take, but many students do not remember much from it. I took Introductory Statistics at my old school about three years ago. Thinking back, I do not remember a lot. In the class I took we were given formula sheets so a lot of the formulas we used were not necessary to remember. In addition, I remember almost everything we did was just punching things into a calculator. Some things I remember looking at were baseball stats and probability. For example, we spent some time looking at the Monte Hall problem. I also remember looking at distributions. We learned about the empirical rule and how statistics fit into it. For the empirical rule I remember 68.26% of data falls within 1 standard deviation, 95.44% in 2 standard deviations, and 99.73% in 3 standard deviations. Although I am a biology major, math has never been a really fun or interesting subject for me. 

Figures 1 and Figure 2 featured 23 differences between them that occurred due to certain factors that were not controlled carefully or explained precisely. The following section will discuss what factors caused the differences that were described in the results section.

One of the most important aspects in the science community is the ability to replicate processes and get the same results. In order to be able to achieve this goal, clear and concise writing is required. In Fall 2018 the Writing in Biology Class at the University of Massachusetts Amherst, conducted a project to observe differences between two figures. Figure 1 was to be created by me and Figure 2 was to be created by another student following my methods section to try to create a perfect replicate of the Figure I already created. The following section focuses on the subject of the figure, including why it was selected, and the factors that were attempted to be controlled in the writing of the methods section and.

The Methods project showed the importance of controlling factors, replicability in scientific works, and the importance of clear and accurate documentation of procedures. The methods project was about creating a figure and describing how it was created through writing. The original figure was then replicated by another student based on the exposition text of the methods. Through the project, the following findings resulted: differences in materials used, differences in location of objects, differences in colors, differences in camera position, differences in camera settings, differences in the range of the map and photographs, differences in weather and differences in the superimposed elements. The results showed that in order to allow for your work to be replicated accurately your methods have to be very specific and the factors to be controlled need to be followed strictly.