Polkadot+Zebras

flat = = =Members: = @DesireeP, @JadeK, @HaileyS, @JodiW = = = = = = =M&M Lab=

As you work through the procedures below, create definitions IN YOUR OWN WORDS of the following: Hypothesis- an educated guess that someone makes to answer a question Data gathering- the process where someone gathers data based on a certain detail or procedure Multiple trials- many different tests that a person has to go through during a scientific experiment Variance- when something is different from something else Average- the center of a group of combined numbers Median- finding the middle number Mode- finding the most often used, or found Histogram- a diagram that can be used to compare data Pie chart- chart/diagram that shows differences in Inference- a guess based on many different pieces of data

Pre-lab 1. Obtain a bag of M&M's. What do we want to know about this bag of M&M's? (Write all the possible questions you can think of.) Include the possible answers to the questions (these are known as hypotheses). How many M&Ms are in a bag? What different colors are in the bag? Are any M&Ms broken? 2. How can we find the actual answers to the questions? Give all possible ways to answer the questions. This leads to data collection. The only way to find the answers to the first two questions is to open the bag of M&Ms, to answer the third question, we could either feel the bag, or open it. Lab 1. Find a partner to work with. 2. Get 1 bag of M&M's per group. 3. Decide on one question you would like to answer about your bag of M&M's. Write it down. (Do not open your bag.) Your question: Are any of them broken? 4. Guess what the answer to your question might be (hypothesize). What is your hypothesis? (Do not open the bag.) Your hypothesis: I hypothesize that there will be broken M&Ms in the bag, because they have been transported from place to place, and picked up and moved by a lot of people. 5. Open your bag of M&M's and answer your questions (data collection/experimentation). After opening the bag, we found that five M&Ms were broken. 6. Also, be sure to count the total number of M&M's in your bag and the number of each color M&M in your bag. 7. Obtain the data from two other groups. Data:


 * Total number of M&Ms in bag ||
 * Number of red in bag ||
 * Number of green in bag ||
 * Number of brown in bag ||
 * Number of blue in bag ||
 * Number of yellow in bag ||
 * Number of orange in bag ||
 * Number of orange in bag ||


 * 1 || 2 || 3 ||
 * 51 || 53 || 59 ||
 * 11 || 6 || 9 ||
 * 7 || 10 || 5 ||
 * 7 || 10 || 7 ||
 * 12 || 8 || 9 ||
 * 5 || 5 || 11 ||
 * 9 || 14 || 18 ||

Data analysis: 1. Looking at the numbers in the data table, are the numbers of total M&M's and the numbers of certain colors of M&M's the same in every bag? No, because no two bags are made exactly the same. 2. From our data, what would be an accurate way to determine the number of M&M's in a random bag I pick up at the grocery store? Use the following terms in your answer: Variance, Average, Median, Mode, Histogram, Pie chart, Inference. An accurate way to determine the number of M&Ms in a random bag picked up at the grocery store would be to put the colors and how many of each color there were into a __histogram__. From the histogram you would want to find the mode, or most often found color, and the __average__ number of M&Ms, along with the average number of colors of M&Ms. Once you find the average number of the different colors of M&Ms, you can then create a __pie chart__ and see the __variance__ in the different colors. Once you collect all this information, you can combine all the information you collected to make an __inference__ on how many M&Ms would be in a random bag of M&Ms picked up at the grocery store.

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