Are M & M's Created Equally?

The results fell a little above one for both Coke and Diet Coke in averages. On the other hand, the pipette and the burette had around the same measurements. Based on the graph, the pipette looked more precise because more points fell in the same area. However, based on the table, the pipette had the highest standard deviation for Diet Coke but the lowest for Coke. Together, its average ranged higher than the burette’s average of standard deviation.

Statistical analysis was performed with results as follows: the mean, or average number of candies per bag, was 55.648. The standard deviation, a measure of variance, was 2.8689. This means that the number of candies per bag can vary up to three candies. A histogram, a graph showing how the frequency of the data was distributed, was done to determine the characteristics of the data. The histogram appeared to be skewed to the left (negatively skewed). This means the data was not uniform.

We bought our Skittles from various stores. We then grouped the Skittles by flavor and documented how many of each flavor each individual bag contained. To be proficient we tested twenty of the regular sized Skittles packs and fifty-five of the fun sized packs. This gave us more than enough information to draw an accurate conclusion. To test our hypothesis, we found the P-value of each flavor for both size packs. Based off the P-values, we were able to determine if there is sufficient evidence to support our belief and Wrigley’s

The following Histogram shows that the distribution is approximately symmetrical about the mean, or bell-shaped. Therefore, using the Empirical Rule, I concluded that 47.5% of the lbs. of milk produced per month fell between 2270.5 lbs. and 964.10 lbs. This accounts for a very large portion of the total milk produced; as 68% fell within 1 standard deviation of 653.20 of the mean, and 95% fell within 2 standard deviations of the mean.

In this lab, M&Ms and Smarties will be compared in five different ways: mass, shell solubility, volume, density, and nutrition facts. For each comparison 10 Smarties will be used and 10 M&Ms will be used. This is to ensure that the results are more accurate, as each piece of candy is not the same size, weight etc… A total of 50

This experiment was to test what is in each part of a Big Mac. The test was to see what macromolecules are in each of the ingredients. The ingredients that were tested were lettuce, special sauce, pickle, onion, cheese, meat, and bun. The ingredients were tested with all four of the chemicals. They were Lugol’s solution which tested for starch, Benedict’s solution which tested for sugar, Sudan IV which tested for lipids, and Biuret reagent which tested for protein. Each chemical found if that ingredient contained either proteins, lipids, starch, and/or sugar.

The standard deviation for 1.0g of glucose was 0.866, for 1.5g, it was 1.173, for 2.0g, it was 1.163, and for 2.5g, it was 3.93. The fact that all these values had a low standard deviation means that most of the values are significantly close to the average. Furthermore, the t-test aids with comparing the means from two distributions. The t-test values were being compared to the data from using 1.0g of sugar. The t-test value of 1.5g of sugar was 1.0972, while the t-test value of 2.0g of sugar was 1.8617, and the t-test value of 2.5g of sugar was 2.4057.

Results will be derived from a chi-square analysis. We’ll conduct the experiment with the assumption that

Our class was split into 2 groups. One group collected Wingstem located in the sun, while the other gathered shaded Wingstem. We cut the stems of the plants about an inch above the ground and put each into a paper bag with a card that identified the location it was initially in. Blue cards represented shaded areas while yellow cards were sunlit locations. Each group collected 20 Wingstem, making 40 total plants. Once we arrived back to the lab, each person picked a Wingstem to inspect. First, we calculated the number of flowers that were visible on the plant, picked them off, and recorded the results on paper. Then, we would weigh the flowers (in grams) on a scale, recorded the results, then added the entire plant to measure the vegetation of the full Wingstem. Once the measurements were recorded, we then discarded the used plants in the trash. We repeated these steps for every Wingstem, being sure to record what location the plant was located in before the measurements started. After each plant was measured, we then added our results onto Excel in four different categories (location, flower number, flower mass, and vegetation mass). A few days later, we then put our data that was gathered into separate graphs and calculated the mean, standard deviation and standard error for each category. We then performed T-tests to determine any significant differences between

In part 1 of the lab, Peanut A appeared to be an off white color, had a spongy texture, and had an S shape. Peanut B appeared to be a pure white color, and had similar characteristics to Peanut A. However the peanut was more compressed and had a higher density than that

Have you ever been so excited to go to the movie theater but realize you waste almost all your money on food? It is way too expensive. It is not a healthy alternative. You are not aloud to bring your own food. People working at a movie theater should make an improvement on the food served their.

Also, trying different brands of butter, organic compared to regular skim milk, and also different brands of chocolate milk could also shift the end product in a different direction. However testing these different brands could change the outcome of the experiment, but most ingredients are very similar and might not alter the results all that much. In the end the experiment was successful and the end product provide the information that was necessary to finish the

In order to obtain a random sample, three bags of M&Ms were purchased from different locations. Each bag of M&Ms was a 1.69oz bag that was the standard plain version of the candy. The M&Ms were then sorted by color and this data was collected and compiled. These results were also added to a larger sample

The method that we used in our experiment deals with a feeding station. An outdoor feeding station was established in Colchester, Vermont on Saint Michael’s College campus. The station was located in a field across the street and behind an astronomy tower surrounded by trees. Our class chose to use red, green, and purple as the prey colors. After a group discussion took place on which colors would best fit this was our conclusion. We chose to use green as the palatable prey, purple as the mostly palatable, and red as the distasteful prey. The mostly palatable prey consisted of ¾ palatable, while the mostly distasteful prey only consisted of ¼ palatable. Flour and lard were the ingredients in the recipe that we used for the prey. The addition of quinine sulfate was given to the distasteful prey to add to the repulsive reaction of the predator (Banschbach, 2012). Next the prey was then colored according to the chosen colors by our class. The red coloring was created using two bottles of red dye in increments of 20 drops, while the green coloring was created using 100 drops of green dye in 25 drop increments. The purple coloring was created in class on the same day but the data for replicating the purple pigment has been misplaced. After the prey was colored the class cut the floury lard prey into roughly (6mm) sections long. They were then labeled in accordance to their palatability. We then randomized the arrays of

Testing statistical significance is an excellent way to identify probably relevance between a total data set mean/sigma and a smaller sample data set mean/sigma, otherwise known as a population mean/sigma and sample data set mean/sigma. This classification of testing is also very useful in proving probable relevance between data samples. Although testing statistical significance is not a 100% fool proof, if testing to the 95% probability on two data sets the statistical probability is .25% chance that the results of the two samplings was due to chance. When testing at this level of probability and with a data set size that is big enough, a level of certainty can be created to help determine if further investigation is warranted. The