Replies: After you have made your post, you will be able to see your classmates' posts. Find 2 classmates who disagreed with at least some part of at least 1 of your answers to questions 4 and 5 and explain why your answers are correct. If after reading some of your classmates' posts, you change your mind about the right answers for questions 4 and 5, explain what you were thinking originally as well as what you think now and why. MY Discussion Thread (Written by you) 4. Explain what an outlier is. Then, if there are any outliers in the Project 1 Data Set, what are they? If there are no outliers, say no outliers. An outlier is an observation point that is distant from other observations. There are two outliers in the Project 1 Data Set, they are 37 and 49 those are distant from the remaining. 5. Which 4 temperatures in the data set look to be the most questionable or the most unrealistic to you? Explain why you selected these 4 points. The following 4 temperatures in the data set are appeared to be the most unrealistic. Max Temps in August 2015 106 108 109 111 Since the temperatures at the beginning of the fall generally expected to be low, but the four temperatures selected above are very higher than the other, so those appeared to be the most unrealistic. Joseph, 1. a) The first five points were 10,11,12,13,14. SD is 1.58. Sixth point is 514. SD is 204.95. The new point caused the SD to move from 1.58 to 204.95. By adding the sixth point so far removed from the the other points, the Mean changed which in turn changed the SD. b) I used the same 6 middle data points in both charts, but the difference between the lower data point and the upper data point in the second chart was much greater to give me a SD of 4. 2. The SD is zero because all the data points land on the same location. In other words, there is no deviation among the data points since the data points are all the same value. So the SD is zero. 3. I see the difference in the SD of the three data sets caused by the value of each data point. When the data points from each data set is totaled and calculated to get the mean, the mean is 50 in each case. But when the mean is subtracted from each data point in each data set to get the individual variation and then the variation is squared, this is where the biggest change occurs. Each data set returns a very different sum of squares which in turn returns varied standard deviations. 4. An outlier is a data point that is far removed from the rest of the data points in a data set. I used my histogram from the previous project to determine the outliers. There are two outliers, AZ at 47 and CA at 49. 5. AZ at 47, CA at 49, KS at 111, and NV at 111. I chose these four data points because they were on the extremes of my histogram. Caitlin, 1. A) When I first completed the group set, the standard deviation was 1.43. After adding the more extreme 6th point, the standard deviation shifted to 3.15. Because of the drastic shift in point range from the initial set to the final set, the standard deviation also drastically shifted in reaction to the change in the set, increasing by 1.72. B) In order to create the data set with a larger standard deviation, I created a broader range of data inputs, making larger gap in between each data input. Because of these larger gaps, I was able to cause an increase in the standard deviation from 1 to 4. 2. The standard deviation essentially represents how far the data inputs are spread out from one another. So because each data points are the same and there is no difference between each point, then the standard deviation equals zero. 3. The standard deviations of each of the three data sets are extremely different. The standard deviation of Data Set 1 is 54.77, which is close to half of the difference between each of the data set points. Data Set 2's standard deviation is 37.42 and Data Set 3's standard deviation is 32.40. The difference between Data Set 2 and Data set 3's standard deviation is 4, which is interesting because the difference between each point in Data Set 3 is half of the difference between each point in Data Set 2. So the standard deviation is dependent on the difference between the data input; therefore the standard deviation increases or decreases in reaction to the difference between the data set inputs. 4. An outlier is a data point(s) that is distant from the majority of the data points within the data set. There are two outliers in the Project 1 Data Set: 47 and 49. 5. The four most questionable or unrealistic data points in the Project 1 Data Set to me were from Alabama, Arkansas, Washington, and Wisconsin. The data points for Alabama and Arkansas were the two outliers, 47 and 49, which lead me question their validity considering the drastic difference between these numbers and the rest of the data points. I found Washington and Wisconsin's data points also unrealistic because I cannot imagine it becoming 109 and 111 degrees there during the summer time considering how far north these states are located. Jeancie, 1. A. My first SD was 0.34. After adding a number that was 3.1 more than my largest number my new SD became 1.46. This was a difference of 1.12 making a large gap on the chart from the mean. B. In the second chart I used numbers farther away from the mean to get a larger deviation of 4. I used a number that was four away from the mean. 2.It is 0 because it does deviate from the mean of 50. Standard deviation is the measure deviated from the mean. Since there is no number away from the mean this makes it 0. 3. The data set with the highest SD had points spread farthest apart and from the mean. The next two data sets had an SD that was closer in number however both tables showed numbers far removed from the mean. 4. An outlier is a number far removed from the data set. 47 and 49 are both outlier in the data set because they are below the Q1 of 79.5. 5. 47 for AZ and 49 for CA because these two places would be much warmer in August. 109 for CO and 104 for RI due to these two states would be cooler in temperature during August. Hayley, 1.a.) The new point which is much greater than the other one affects the mean and the standard deviation. The standard deviation is greatly increased because the extent of deviation for the group as a whole increased after adding the larger number to the set. 1. b.) In the first set I used a lot of the same numbers but in the second one I used a wider variety of numbers. In the second set I made the values a little larger at the end of the set so they would have a greater deviation from the mean than the first one. 2. The standard deviation is zero because the mean is almost equivalent to all of the data entries. This indicates the values do not deviate from the mean throughout the entire data set which is indicative of the values being all the same. 3. When the spread size is drastically spread out the standard deviation is greater because the numbers on either side of the mean are farther away. The sets with less spread have a smaller standard deviation because the numbers on both sides of the mean are closer to the mean itself. 4. An outlier is a number that lies outside the other observations. If a number is less than 1.5 from Q1 or greater than 1.5 from Q3 it is an outlier. In the data set, 47 and 49 would be considered outliers because they are less than 1.5 outside of Q1. 5. I would consider 47,49,88 and 11 as questionable values of the data set. These values seem questionable because most of the other values lie with in the 90s and 110s. The 47 and 49 definitely seem unrealistic considering those are the temperatures for Arizona and California the two hottest states in our country