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x---equipment, internet resources, computer programs, multimedia, etc.).equipment, internet resources, computer programs, multimedia, etc.).
The materials we used for our experiment is the websites for information based on teeth and what causes discoloration, 3 cups, 3 types of sodas, 3 teeth, 1 classroom and a sink. Also we used our notebook and worksheets to write down our research and also so that we have more knowledge about our experiment. We also are going to use a tooth whitening chart to measure the whiteness of the teeth before and after they are put into the soda.
{http://ts1.mm.bing.net/th?&id=HN.608029561270765308&w=300&h=300&c=0&pid=1.9&rs=0&p=0&url=http%3A%2F%2Fwww.shutterstock.com%2Fs%2F%2522tooth%2Bdecay%2522%2Fsearch.html} {http://t3.gstatic.com/images?q=tbn:ANd9GcRUmx44kS5X0W_SCHzR2k9vQ-ZvsP8n8Sr3-FSKkrRwTw4pxnM5FA}{http://t3.gstatic.com/images?q=tbn:ANd9GcRUmx44kS5X0W_SCHzR2k9vQ-ZvsP8n8Sr3-FSKkrRwTw4pxnM5FA} Image result
2.Identify the control group and the constants in your experiments.
The constants in our experiment are the teeth, our location, the amount of soda in each cup, the location of the teeth, the temperature and the label put on the cup. The constants are very important to our experiment because all the constants affect our outcome. We kept the teeth in the sodas for 3 days because we weren't going to risk letting the teeth dissolve which later the Sunkist proved would be possible. In other words we had no control group over the sodas or the results of the teeth. The control group is something that does not change.

My hypothesis is the tooth in Sunkist will be yellow after the 2 day. The rest will be white #5 according to the tooth whitening chart after the first day. We also predict that the tooth in Sunkist will be white #8 on the fourth and last day. I think the tooth in Coca cola will be white #10 on the last day, the tooth in Sprite will be white #9 on the last day.
Our hypothesis is that the soda is going to change the color of the teeth. We also think that the teeth will change colors depending on the color of the soda we put it in. For example if we put a tooth in orange Sunkist the tooth might turn orange. We also think that Coca Cola will be the worst for your teeth. Also based on our research, we found out that soda also could cause sensitivity towards our teeth.
{http://t3.gstatic.com/images?q=tbn:ANd9GcSaGZm6pV3sxuJUlW3Ui_cwj2Jwq10ht2P6k8GRLlD8nuMADd7e} Image result for hypothesis

My hypothesis is the tooth in Sunkist will be yellow after the 2 day. The rest will be white #5 according to the tooth whitening chart after the first day. We also predict that the tooth in Sunkist will be white #8 on the fourth and last day. I think the tooth in Coca cola will be white #10 on the last day, the tooth in Sprite will be white #9 on the last day.
Our hypothesis is that the soda is going to change the color of the teeth. We also think that the teeth will change colors depending on the color of the soda we put it in. For example if we put a tooth in orange Sunkist the tooth might turn orange. We also think that Coca Cola will be the worst for your teeth. Also based on our research, we found out that soda also could cause sensitivity towards our teeth.
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 width="300" height="168" caption="Image result for tooth in soda"]]
2.Identify the independent variables and dependent variables in your hypothesis.
Our independent variable is the amount of soda we will use in each cup. Also the flavors of the sodas, which we will use the same amount for each of the cups. Next our dependent variable is the color the tooth while changes to. This would be our dependent variable because we cant control the color the tooth changes to.
3.How did you measure the validity of your hypothesis?
(In other words, how did you determine
that your hypothesis measures what it is SUPPOSED to measure?)
To measure the validity of our hypothesis we used a tooth whitening chart.The tooth whitening chart helps us see how soda effects our teeth. Also We will use this chart, so when the tooth comes out of the soda we will put it next to the chart and match it to see what color it is. Also we will compare the teeth that we used to see which soda is best for your teeth and which soda is the worst for your teeth. The tooth whitening chart will be the best to measure the validity of our hypothesis.//

My hypothesis is the tooth in Sunkist will be yellow after the 2 day. The rest will be white #5 according to the tooth whitening chart after the first day. We also predict that the tooth in Sunkist will be white #8 on the fourth and last day. I think the tooth in Coca cola will be white #10 on the last day, the tooth in Sprite will be white #9 on the last day.
Our hypothesis is that the soda is going to change the color of the teeth. We also think that the teeth will change colors depending on the color of the soda we put it in. For example if we put a tooth in orange Sunkist the tooth might turn orange. We also think that Coca Cola will be the worst for your teeth. Also based on our research, we found out that soda also could cause sensitivity towards our teeth.
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 width="300" height="168" caption="Image result for tooth in soda"]]
2.Identify the independent variables and dependent variables in your hypothesis.
Our independent variable is the amount of soda we will use in each cup. Also the flavors of the sodas, which we will use the same amount for each of the cups. Next our dependent variable is the color the tooth while changes to. This would be our dependent variable because we cant control the color the tooth changes to.
(In other words, how did you determine
that your hypothesis measures what it is SUPPOSED to measure?)
of our hypothesis.hypothesis.//

1.Describe the plan your team used to complete your Mission Folder. Be sure to explain the role of each team member and how you shared and assigned responsibilities. Describe your team’s process to ensure that assignments were completed on time and deadlines were met
We planned this based on our capabilities and interests in each task. Our group assigned each task according to what they showed most interest in. Jaden is in charge of the wiki because she showed the most interest in the wiki and knows the most about the wiki. Arianna showed an interest in collecting some research data and to write up the experimental design so that became her task. When we got off Task Samantha reminded us that we are going to get graded. Samantha became in charge of the editing because she showed and interest in taking what we typed up and making it more complex. Jaden would give us something to do. Philip liked to always have something to do so he was in charge of typing up our responses. Each team member did certain pages and/or certain parts of pages during this project. We encouraged each other to keep going so we wouldn't end up with incomplete work. We all worked together to complete each assignment by communicating and sharing our ideas. We also sometimes asked for others opinions. We each tried our best to stick to the calendar on school loop. Arianna would ask for help and we would. When Samantha asked for our opinion on one page, we were honest even if we didn't like it. Jaden would help us stay on track and we would do the same if she wasn't. Philip would always ask for something to do, even if he didn't know how to do it.
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"]]{http://t0.gstatic.com/images?q=tbn:ANd9GcRm08sUWaxg-YWAzVMjLEMEplB_PfrkR4pIDuJCUTRJPn9MeDti} 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1.Describe the plan your team used to complete your Mission Folder. Be sure to explain the role of each team member and how you shared and assigned responsibilities. Describe your team’s process to ensure that assignments were completed on time and deadlines were met
We planned this based on our capabilities and interests in each task. Our group assigned each task according to what they showed most interest in. Jaden is in charge of the wiki because she showed the most interest in the wiki and knows the most about the wiki. Arianna showed an interest in collecting some research data and to write up the experimental design so that became her task. When we got off Task Samantha reminded us that we are going to get graded. Samantha became in charge of the editing because she showed and interest in taking what we typed up and making it more complex. Jaden would give us something to do. Philip liked to always have something to do so he was in charge of typing up our responses. Each team member did certain pages and/or certain parts of pages during this project. We encouraged each other to keep going so we wouldn't end up with incomplete work. We all worked together to complete each assignment by communicating and sharing our ideas. We also sometimes asked for others opinions. We each tried our best to stick to the calendar on school loop. Arianna would ask for help and we would. When Samantha asked for our opinion on one page, we were honest even if we didn't like it. Jaden would help us stay on track and we would do the same if she wasn't. Philip would always ask for something to do, even if he didn't know how to do it.
{http://ts1.mm.bing.net/th?id=HN.608012604733718800&w=207&h=185&c=7&rs=1&qlt=90&o=4&pid=1.7} 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"]]

1.Interpret and evaluate your results and write a conclusion statement that includes the following: Describe what you would do if you wanted to retest or further test your hypothesis. Evaluate the usefulness of the data your team collected. What changes would you make to your hypothesis and/or experimental design in the future, if any?
color like white.white
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2
off the chart red
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BW1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRElaznzZu3toXa2lrI2nFomkj0CBw0K4KsJSCqZK2ClXWYoGki0SNw0KwIspYAyBqydhCaJhI9AgfNiiBrCYCsIWsHoWki0SNw0KwIspYAyBqydhCaJhI9AgfNiiBrCYCsIWsHoWki0SNw0KwIspYAyBqydhCaJhI9AgfNiiBrCcB3MELWDkLTRKJH4KBZEWQtAVhZQ9YOQtNEokfgoFkRZC0BkHUofjY7a1Ftb13Wbh5eszqHTqjNQ3AkavOEdaQLK4b2GzfRrhwhazmArIPyyNLuRx5ORBCJ8tQ9naZU9el6TsWokVfZkiNkLQeQtZFVF2afmJOGIBLlf/6j24Pr4xRZZ7lusyVHyFoOIGsdFWNcgQn9v5wyCEEkyvaz01VZ56VusSVHyFoOIGsd3oGDTg+e+C2CyJOP+pfpZD2u+HLrcoSs5QCy1lJS1O+dvuf9e8hkBJEorxWdC1l3fCBrLXMLB7+Tsbwxcw2CSJRdvSfqZD2x8BrrcoSs5QCy1rIs46L30jb/pcc2BJEo9RljIOuOD2St5d7Ono96bj+evgNBZMlraesh66gAstbyWHLl8YwdCCJR9ndbDllHBZC1yjn5xfu7LPsoYzuCSJT93ZZB1lEBZK1yXv5IX2rtkR73IIhE2Zu6BLKOCiBrlcmZY3/bpfbt7psQRKLsSa02yhqn7nVAIGuVSZlj9qcs+9+u6xBEomzpfK1R1rbkGJ2yzoofHDTm9yzx+puampr83pJwz8gDWWu2QUY8kVJzMPU2BJEo9Z0rIGu79OiUc32352/L/kQXd9ed8XGJmht6fE1NTT4P469q8XSJ1x9BZUPWmjcYh/8q+cbnu6xEEInyQPICnazxQU6mJMQXZWQ/M+LcgHvO92oGj3onrffmuLgk9WYlXr/f5/Pztvb41H9D1uEnqKxH5w/1dq54ussyBJEo93W+Tifr/v1usiXHKJR1fI8ePdyze3oqL9j5uKfhbU/D22M2besx+/quF02Ni49vuZXi4hKvxtYeX1Mzfp/Pr172lqi7I9xKvMX2jmyaQNZaapPKH0upRBCJcn/yDTpZ4/OsTemVk1i9M2ftc/neV4te+OSifUdK179UcOve3NlrMhMS45pvpK6bNWtpFmplrbnI30C/i9J+8B2MWmZ2u3Br8rx7k69DEFlS33mOTtZ25RiFsu6TH/eHPfF/f7XTvz+I/+ad1K/eSvnyT/H+Fzo9cmd8UlKzrHn9tq6Mg8q6dVndpH3z0dF9EqystYzNH7YqafrtnWciiETRyrrvuEl25RiFsi5K6/TitO4fze5+4oac4zcUHb+h6K/XZb/10+73T0hNildk3brhwfs3tKyDrKAh6zOmjc+zvvasol/0LEYQibL5rAGqrNshxyiUdWFSyrP9Rhwcfd6BkgvUvDTi7O1F/ZtlzS2muX+G3AYJYmvI+oxp+2u9Lskpuj63J4JIlI3np6xb2mPS1NHtkGMUyjqnU8ZdGbPucM3QZVXP/0iK68QMrtZeod3B1p5yrdkJUe/qtKwbGxsbGxshay3JrsWsh9deEm5ivVeyHjXd8xdE5pieOnVqXV1dZJ7LIhUVFXPmzBE9BQcqMiUKZd07Lm1IQl7QJMR2CutTt5/XDx0KBAKBQEA6WZ/Jf7PalnX6kKVs9J9tJ32pImuWeeuw0ZdF4JiGrK2AikyJQllLyd59+xoaGtqxstacb+jwWeEWHzlMsk7vN5MNbWCDDtlLv72QdSlVE4kegYNmRZC1BLx+6NDrhw7Z3bP2+Jq0p4k7qGvrjxwmWSfnVLPs51j28/aSc7dW1pHZCYGsrYCKTIGs5aA9e9YhzlLRnuvS+mP9Oll7QoxBtnYeuUXWbT5gCNqQNev9S5bzku24VkPWpVRNJHoEDpoVQdYS0J49a8MbpYwpntWexaKqObRAjT+08cjBVtZtPpuWULJOz7mCFR2I6XfYXnIe0sma9VoRgWMasrYCKjIFspaD9uxZB3Wi4S8y/d6SoLfk/tTH7/W0/DPU7YM/skbWugc8E1knZy+MHfhW3JAjthJTsBuyVqBpItEjcNCsCLKWgPbsWQfdrAj65/NG+Wrva1wbW39k9d5tP2AIQsmaZW2JH3YkYfhRW4nJ2QJZK9A0kegROGhWBFlLQLvOs+Y/naTE6/d5gm9WaK9U4P8QyLAQtvzIqplNHjA4IWWd+2Bi8VG7Yb1qIWsFmiYSPQIHzYogawlo73nW2r2HFrmGeBtQt0PReiufL9jZHjYeWf+WZfAHDEJQWecWnR1T8GTicL/dQNYqNE0kegQOmhVB1hLQ7vOspSaErEtiCvfHD33XboyyxtkgdEBFpkDWctC+86xlJ6is03MnxRQ+HTvwDVuJ6fMcZK1C00SiR+CgWRFkLQH4bBDNeXuTWMFTMf3/aCus6BmjrPMGzYrAMQ1ZWwEVmQJZy4G8nw1yJoTaBmH5j7O+L9lL4V7IWoWmiUSPwEGzIshaArBnrX2DkeXsZEW/tZf8XUZZ47NB6ICKTIGs5QB71q2yLhzFXJtYwV7bMcg6Msc0ZG0FVGRKdMo6tvOgoGnPY2k/1jp84DsYufOsM5ezvIdsh5d13oAZkTmmIWsroCJTolDWsQk58X3/q9vYj3SJz9sWF5fIGLP1l9GWP+fizMA3xWhJPmsGy97Ccu62l8xVkLUCTROJHoGDZkVRJ+ukvJiCB3qPP3L+/CY1PUtejc1aExeXxBiz8JVdrfB/ZB02IGt+23osy7yVnVVnL/zKOmLHNGRtBVRkSjTKulPvmNRbWOrmgee+MXX2v6bO/lfe8BdZ6rrYlOvi4hIYY6G+3Lx1vW38iz3D54pqPwfD5/OrNzE+iCUga/3iOv1ylna9zSxQZZ03pCJixzRkbQVUZEo0yjqlT8yYg6z0BBv/fZ/5Ta7Z37ELP2fnfRA79KG4TurK2rANwgtc88H7LZeDfzaG4VM0gj2IOZC1kXRXKetabi8Z81n60ryCyyN5TEPWVkBFpkSjrDtlxiRVs8TbWVJ9Uvf6xK6bWNJ6lrgyNmF2kJV1y3eicP7WbGS3Ojf4R4RynzAX6kHMgaytU1ZWRuplBllbARWZEo2yTi6MGfI0G3U4texI+rh308e9m/KT91nxK7F9N2tW1q0iVbTbxpejhJK1z6N/KLMN8NBA1pC1g9A0kegROGhWFHWyTnDFnOVN7buz19AdapILt8VmrIjrpJ4Nol9ZhxKthW0QzQK63baGrCFrB6FpItEjcNCsKOpknZiTUnh7Wt/VunTOWd72qXvGjxFluvV0iDcYtbsdQR/EHMgasnYQmiYSPQIHzYqiTtYJveKSBwdNbGx8WJ+6/UDWkLWD0DSR6BE4aFYUbbKWEsgasnYQmiYSPQIHzYogawmArCFrB6FpItEjcNCsCLKWAMgasnYQmiYSPQIHzYogawmArCFrB6FpItEjcNCsCLKWAMgasnYQmiYSPQIHzYogawmArCFrB6FpItEjcNCsCLKWAMgasnYQmiYSPQIHzYogawmArCFrB6FpItEjcNCsCLKWAMgasnYQmiYSPQIHzYogawmArCFrB6FpItEjcNCsCLKWAHwHI2TtIDRNJHoEDpoVQdYSgJU1ZO0gNE0kegQOmhVB1hIAWUPWDkLTRKJH4KBZEWQtAZA1ZO0gNE0kegQOmhVB1hIAWUPWDkLTRKJH4KBZEWQtAZA1ZO0gNE0kegQOmhVB1hIQVbKeN2/e2hZqa2sha8ehaSLRI3DQrAiyloCokrUKVtZhgqaJRI/AQbMiyFoCIGvI2kFomkj0CBw0K4KsJQCyhqwdhKaJRI/AQbMiyFoCIGvI2kFomkj0CBw0K4KsJQCyhqwdhKaJRI/AQbMiyFoCIGvI2kFomkj0CBw0K4KsJQCyhqwdhKaJRI/AQbMiyFoCIGvI2kFomkj0CBw0K4KsJQCyhqwdhKaJRI/AQbMiyFoCIOugXDYp76E7U/c/2lmXDZu7jrl2RP8xV4s+nktLIWtroCJTIGs5gKyNVEzofXhh6tEl+vxueWfvHUljrh3RdXDNqJFXiT6kIWtLoCJTIGs5gKyNvDyt62dz04y5vzxZlfW5mYtFH9KQtSVQkSmQtRxA1jpuHF706dlj/1lyti5/GDEYsjaFpolEj8BBsyLIWgLwHYw6ns0f9f3gicY0FJ2jlfVPksQrALK2AioyBbKWA6ystZTn9TvWv/THoZONudc1SCfrccWXiz2mIWsroCJTIGs5gKy1zC8a8mbXO49232oMZG0FmiYSPQIHzYogawmArLWs6nXpkbS7Az2367I/dRlkbQWaJhI9AgfNiiBrCYCstexMnh9I3/7XjB26PN1tOWRtBZomEj0CB82KIGsJgKxVzinqtye5OpBxnzH7IWtr0DSR6BE4aFYEWUsAZK1ybn7xM12WH8vYZsy9KddB1lagaSLRI3DQrAiylgDIWmVS5hhfau37PTYbszV5nlHWog9pyNoSqMgUyFoOIGuVKZljf9NlxZvdNhqzKmk6ZG0FmiYSPQIHzYogawmArLUr630pS1/teocxRlnjLxiDQtNEokfgoFkRZC0BkLVKSf6IXSlVB1LXGGOU9dg+80Qf0pC1JVCRKZC1HEDWKmPzh+9Ivv6/u9xizKbOsyFrK9A0kegROGhWBFlLAGStMjJ/yIbOP9vbZbExGzpfo5M1PiI1KDRNJHoEDpoVQdYSAFlrWZo8+cGUGx9MuUmXOzrP1Mla9PFcWgpZWwMVmQJZywFkrdKvX7/paaO9yRVbkq/VZWPnn2tl3XfcJNHHc2kpZG0NVGQKZC0HkLVK3759R+bmr+z2k7oulxqjlbXog7kZyNoKqMgUyFoOIGuVwsLC3NzcSZk5KzKKjLmr9wBF1pfNHiT6YG4GsrYCKjIFspYDyFqloKAgOzvb5XKVZmdcktX3kqwBulxX1mfgqAUs6+6QKdrMetQMGzElMsc0ZG0FVGQKZC0HkLVKXl5eVlaWy+VKzzmXZf2SZe+yl7Q1rPdK1qOme/6CyBzTkLUVUJEpkLUcQNYqubm5LpfL5XKxwU+zMUdtJ32pImuWeeuw0ZdF4JiGrK2AikyBrOVA1HcwenxNCn5vib17+TyMlXj9tu6npw1Zp2a7WcFzrOigvfT/tVbWeYNmReCYhqytgIpMgazlQMzK2uNTJe3x+Ty27x9GWbP0dSz7BdtxrdbKOjI7IZC1FVCRKZC1HAiXteYqn9fr55fbJV6/3+fzq9eUeP1+r6flVs3Xlqj/tqz9ULJOTT+PnfU4yzlgL669kLUCTROJHoGDZkWQtQSI2rNWtkE0xvb4mjSr7VY5cwpuWVFrVtaaix6fVV2HlHXm5TH9X4sd9L6txOQ/rpM167UiAsc0ZG0FVGQKZC0HQt9gVNbEims507b8Q7/dYZR167Lazg54KFknum6OG/ROp6F+W4kt2A1ZK9A0kegROGhWBFlLgPCzQVpW0Wcga/ub3qFkzXK2Jwz3JxYftZWYnC2QtQJNE4kegYNmRZC1BAiRdYnX3yJYVbaabZASr1+7R92mrNtn66CyznQNYnmPJQ7/0G5Yr1rIWoGmiUSPwEGzIshaAgStrNUz99Qtae4NRo3Kg8q6ZfdD9waj5fMAg8s6e2RM4b7EYR/YDWStQtNEokfgoFkRZC0BwrdBWrD+7qADBJV1muuCmMKnOw15226MssbZIHRARaZA1nIAWbfKOms8K9wfM+B/7aXPf0PWKjRNJHoEDpoVQdYSQG57WVoAAAalSURBVEbWESXEynocy38ipt9BW2GFTxtljb9gpAMqMgWylgPIWrNnPYrlPcr6vGAvBU9C1io0TSR6BA6aFUHWEgBZa2Q9nGVvY4XP2Eveo0ZZR+aYhqytgIpMgazlALJulXXWYOa6gxXssR3IugWaJhI9AgfNiiBrCYCsuT+KyahiuQ/YDi/rvAEzInNMQ9ZWQEWmQNZyAFlrZZ2YfgXL2siyvfaS+QutrPFNMaKn4EBFpkDWcgBZa2Xds1c/1nMR63WLvWQsU2WdN6QiYsc0ZG0FVGQKZC0HkLVW1i6XK7XnOazrNbbTIuuILatLIWtroCJTIGs5gKx1sna5XKkZ5yRkXJSYcVFixkXJvS/VplvutOApuKZbYeWw4kmRPKYhayugIlMgazmArI2yVsjOzs7Kyurbt696g7KyMlIvM8jaCqjIFMhaDkR9B6NYrMha+abzPn36QNbWoWki0SNw0KwIspYArKxDyVoBsrYFTROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGQNWTsITROJHoGDZkWQtQRA1pC1g9A0kegROGhWBFlLAGRtUdZlZWULFy6cPXv22WS45pprKisrRU/RyqWXXlpdXY2K2gAVmaJUNH78eNGSoAdkbUXW06dPr6ysnDRpUjENJk6cuGjRopkzZ4oepJUZM2aQqqi4uJhgRXV1daioDSZOnFhXVwdTBweyNpX19OnTCR7QpF7ziqlFT9GKUhGp/9cIVkTN1Mp/zGDqkEDWbch69OjRCxcupHZAk1rA0nzNo6K2oVnR3LlzCwsLRbuBMJB1KFmPHj2a1AFdTO+XVvxebwrBX4MWLVpEbY2PBbUlIOugsp46dSrBA5rUa57g7/XY+mgbmmt8mNoqkLVO1gMHDpw/fz61A5rUGp/max4VtQ21ioqLi7H1YQ/IWitrbH2Ygq0PU/BrkClKRTiZ2h6QtUp5eTnBA5qUhgi+5qmZmtoClmZF2PpoD5C1As76aBuar3lSFRXTW+PT/DUIWx/tBLIuKysjeECTes0T/L2e5ikNpCqi+WsQtj7aT5TLWvnTRNGHcStYwJqCikyhWRG2Ps6UaJY1tj7ahuZrnlRFxfR+DcLWR4clqr6Dcd68eWtbuP322ysrK2eQYeXKlXV1daKnaGXJkiV1dXWoqA1QkRVWrlyJrQ9niM6V9fjx49euXTuXEpjHFGojUZtnLtWRRL/cOwrRKWvGGLVjCPOYQm0kavMwjNSxgayJgHlMoTYStXkYRurYRK2sFy9eLHoEDsxjCrWRqM3DMFLHJmplDQAAMgFZAwCABEDWAAAgAZA1AABIAGQNAAASAFkDAIAEQNYAACABkDUAAEhANMra42tqwe8tET1Nidff1IrPI3IWj08/gvCu9CMJrUttQ9uFwIqCzCP4cNI8vaYM4UdRxyD6ZO3xqQdMidcv/uAp8foFK1rB42tq8nk8Pu0wgrsKNpLAujy+5gK0XQisKOg8Yg8ndSTtHNRecdISdbIOaSNRUJG1AlcPja7IyLqVEq+/uQoaFbXOQ6Mfpq2CRkUdgWiTte5Y1hzlouB+bxX+MtO+soh0ZZS16Lpai6FRkXYKwf20Pr12IPEVdQgga0KHjvJrv+gRaMua/4GIuvRqFF1RyLW00MNJ3fCgUFEHIcplTeyXMvHjtCFrGtsgup9EeiLdpqvwitrcBBZ7ODU/u/CKOg7RJmvDmzGUjhxiK2siXdFZWQd5QqEVmRQQ+cNJ+3+V+uw0jqKOQFR9B2MzmhOJhO8Rc9MIHUc7R5P2ZAdxwwUbSVxd/Elxrc8uqqLg84g9nLQz8ctpCoe47ETdyhoAAGQEsgYAAAmArAEAQAIgawAAkACtrN986/Dsn1eInggAAIABrazffe/96hp8FTEAANBDK+u//OWju+o3i54IAACAgZM8jzy2KyUlRfRQAAAAeLSmPnXq1O13rLv4kktFDwUAAKCVgsJCvaxnzZq1bfuOtLQ00bMBAABgjLGEhATv5i16WZeXly9cVLlq9RrR4wEAAGCMsTnXzr3l1pXNsm5sbGxsbFRkXV5evm79hpmzfiZ6QgAAiHYmTJxUv+Xu8vJydvLkydcPHQoEAoFAQJX19OnTV6/5z7vqN2dnZ4seFQAAopGUlJRVq9esW79h+vTpzbLeu29fQ0ODdmWtcP31N9y3Y+fsn1f07z9A9NgAABAtFBQWll951Y6dv1y4qFIV8v8DXnV/Hon5YJsAAAAASUVORK5CYII=]]2.Analyze{graph for wiki.PNG}
2.Analyze the data
3.Explain any sources of error and how these could have affected your results
A source of error was that we forgot to use water in the experiment. Using water in our experiment would have effected it by giving a contrast between water and soda. This would have possibly changed our experiment drastically or minimally. Another source of error was that we did not test our experiment more that once (the scientific method(3)). This could have effected our experiment if we did something incorrect it could have changed our results and the data would be more accurate. Lastly, a source of error was that we didn't have all our materials because we were supposed to have 4 sodas, but we only had used 3. This is a source of error because it minimizes our amount of results.

2
off the chart red
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]]2.Analyze the data
3.Explain any sources of error and how these could have affected your results
A source of error was that we forgot to use water in the experiment. Using water in our experiment would have effected it by giving a contrast between water and soda. This would have possibly changed our experiment drastically or minimally. Another source of error was that we did not test our experiment more that once (the scientific method(3)). This could have effected our experiment if we did something incorrect it could have changed our results and the data would be more accurate. Lastly, a source of error was that we didn't have all our materials because we were supposed to have 4 sodas, but we only had used 3. This is a source of error because it minimizes our amount of results.