MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES


 Abel Benson
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1 MINUTE TO WIN IT: NAMING THE PRESIDENTS OF THE UNITED STATES
2 THE PRESIDENTS OF THE UNITED STATES Project: Focus on the Presidents of the United States Objective: See how many Presidents of the United States someone can name in a minute Data: Used Fathom to record our data, had a list of all of the Presidents of the United States that we used to check off each time a President was mentioned Variables: How many Presidents the person could name, their gender, their favorite subject, and if the person has taken any Honors or AP History courses
3 QUANTITATIVE DATA Collection 1 Box Plot Num ber_of_presidents_correct
4 QUANTITATIVE DATA Summary Statistics Mean: Presidents Std. Deviation: Presidents Minimum: 4 Presidents Q1: 9 Presidents Median: 14 Presidents Q3: 19 Presidents Maximum: 36 Presidents IQR: 10 Presidents
5 QUANTITATIVE DATA Shape, Center, Spread Shape: Right Skewed, Unimodal Center: At the median of 14 Presidents IQR: Of 10 Presidents Outlier: At 36 Presidents Range: Of (4, 36) Presidents
6 QUANTITATIVE DATA Outliers A= 9 (1.5 x 10) A=6 Presidents B= 19+ (1.5 x 10) B= 34 Presidents Range: (6,34) Presidents There is an outlier at 36 Presidents because 36 exceeds the range of the data which is (6,34) Presidents.
7 QUANTITATIVE DATA Shape: The shape of the histogram does not look Normal because the data is right skewed. Percent of Data Falling within 1, 2, and 3 Std. Deviations from the Mean: X= 14.4 Presidents S= 6.78 Presidents 1 Std. Deviation: 31/43= 72.1% 2 Std. Deviations: 41/43= 95.35% 3 Std. Deviations: 42/43= 97.67% The % Rule: (7.62,21.18) Presidents = 68% (0.84, 27.96) Presidents = 95% (5.94, 34.74) Presidents = 99.7% Although our shape of our histogram was not Normal because it was right skewed; the skew is very slight and can also be considered to be roughly symmetric which explains why our percentages are very similar and almost exact to the actual percentages.
8 QUANTITATIVE DATA Counts: Female: 18 Male: 25 Total: 43 Percentages: Female: 41.86% Male: 58.14% Total: 100%
9 QUANTITATIVE DATA
10 Summary Statistics of Gender and the Number of Presidents Correct Males: Mean: Presidents Std. Deviation: 7.24 Presidents Minimum: 7 Presidents Q1: 11 Presidents Median: 14 Presidents Q3: 19 Presidents Maximum: 36 Presidents IQR: 8 Presidents QUANTITATIVE DATA
11 Summary Statistics of Gender and the Number of Presidents Correct Females: Mean: 12 Presidents Std. Deviation: 5.39 Presidents Minimum: 4 Presidents Q1: 7 Presidents Median: 10 Presidents Q3: 16 Presidents Maximum: 21 Presidents IQR: 9 Presidents QUANTITATIVE DATA
12 Summary Statistics of Gender and the Number of Presidents Correct Row Summary: Mean: 14.4 Presidents Std. Deviation: 6.78 Presidents Minimum: 4 Presidents Q1: 9 Presidents Median: 14 Presidents Q3: 19 Presidents Maximum: 36 Presidents IQR: 10 Pesidents QUANTITATIVE DATA
13 QUANTITATIVE DATA Male Outlier(s) A= 11 (1.5 x 8) A= 1 President(s) B= 19 + (1.5 x 8) B= 31 Presidents Range (1,31) Presidents There is an outlier for the Male's at 36 Presidents because the number 36 exceeds the range of (1,31) Presidents. Female Outlier(s) A= 7 (1.5 x 9) A= 6.5 Presidents B= 16+ (1.5 x 9) B= 29.5 Presidents Range (6.5, 29.5) Presidents There are not outliers for the Female's set of data, all the data falls within the range of (6.5, 29.5) Presidents.
14 QUANTITATIVE DATA The shape of the Males graph is right skew and unimodal while the shape of the Females graph is symmetric and bimodal. The center of the Males graph is at the median of 14 Presidents, while the center of the Females graph is at the mean of 12 Presidents. The IQR of the Males graph is 8 Presidents, while the Std. Deviation of the Females graph is 5.39 Presidents. There is an outlier at 36 Presidents for the Males graph, while there are no outliers for the Females graph. The range of the Males is (7,36) Presidents, larger than the range of the Females which is (4,21) Presidents.
15 QUANTITATIVE DATA Gender does seem to have an effect on our quantitative data. This can be seen when analyzing the data for Males and Females along with looking at the shapes of the graphs. For example the shape of the Male graph is right skew and unimodal, while the Female graph is symmetric and bimodal. The difference in the shape of the graphs is because there was a greater range for the Males data than there was for the Females data (Males= 29 [range]; Females=17 [range]). Each summary statistic was different for both genders. Example: The Q1 for the Male graph is 11 Presidents; the Q1 for the Females graph is 7 Presidents.
16 CATEGORICAL DATA 1
17 CATEGORICAL DATA 1 All Academic S.S. Classes Mean: Presidents Std Deviation: 7.78 Presidents Min: 6 Presidents Q1: 7 Presidents Med: 10 Presidents Q3: 17 Presidents Max: 36 Presidents IQR: 10 Presidents At least One Honor's/AP Class Mean: Presidents Std Deviation: 5.75 Presidents Min: 4 Presidents Q1: 11 Presidents Med: 15.5 Presidents Q3: 19 Presidents Max: 29 Presidents IQR: 8 Presidents
18 CATEGORICAL DATA 1 All Academic S.S. Classes A = 7(1.5 x 10) A = 8 B = 17+(1.5 x 10) B = 32 Range = (8, 31) Presidents There is an outlier in this set of data because 36 does not fall in the range of (8, 31) At least One Honor's/AP Class A = 11(1.5 x 8) A = 1 B = 19+(1.5 x 8) B = 35 Range = (1, 35) Presidents There is no outlier in this set of data because all data falls in the range of (1, 35)
19 CATEGORICAL DATA 1 The shape of the Yes graph(students that have taken an AP/Honors History Course) is right skewed and unimodal while the No graph (Students that have not taken an AP/Honors History Course) is unimodal and symmetric. The center of the Yes graph had a center median of 15.5 Presidents, while the No graph has a center mean of 7.78 Presidents. The IQR of the Yes graph is 8,while the Std. Deviation of the No graph is Presidents. There is an outlier at 36 Presidents for the No graph, while the Yes graph has no outliers. The range of the Yes graph is (4,29) Presidents, which is smaller than the No graph with a range of (6,36) Presidents.
20 CATEGORICAL DATA 1 Taking an AP/Honors course seems to have an influence on our quantitative data. By looking at the shapes of the graph, you can see a difference in knowledge of presidents. You can see that the No graph is right skewed with a median of 10 Presidents, while the Yes graph is symmetrical and has a mean of Presidents. It seemed that taking an AP/Honors history course seemed to help with knowledge of presidents.
21 CATEGORICAL DATA 2
22 CATEGORICAL DATA 2 English: Mean: Presidents Std. Deviation: 4.43 Presidents Min: 4 Presidents Q1: 7 Presidents Med: 9 Presidents Q3: 9 Presidents Max: 15 Presidents IQR: 2 Presidents History: Mean: 20 Presidents Std. Deviation: 5.07 Presidents Min: 12 Presidents Q1: 5 Presidents Med: 19.5 Presidents Q3: 19.5 Presidents Max: 22.5 Presidents IQR: 14.5 Presidents
23 CATEGORICAL DATA 2 Math: Mean: Presidents Std. Deviation: 8.61 Presidents Min: 7 Presidents Q1: 8.5 Presidents Med:10.5 Presidents Q3: 19.5 Presidents Max: 36 Presidents IQR: 11 Presidents Science: Mean: 13.7 Presidents Std. Deviation: 3.59 Presidents Min: 8 Presidents Q1: 12 Presidents Med: 5 Presidents Q3: 17 Presidents Max: 19 Presidents IQR: 5 Presidents
24 CATEGORICAL DATA 2 Other: Mean: Presidents Std. Deviation: 9.74 Presidents Min: 6 Presidents Q1: 6.5 Presidents Med: 9 Presidents Q3: 19 Presidents Max: 27 Presidents IQR: 12.5 Presidents
25 CATEGORICAL DATA 2 The categorical variable does have an effect on our quantitative data because it changes the amount of the data when looking at each subject. Some subjects have a smaller range of quantitative data while other subjects have a larger range. The quantitative data was different for each subject because some students who's favorite subject was Math did better than the students who favorite subject was Other. Example: Comparing the Medians of the different subjects English: Mean of Presidents Math: Mean of Presidents History: Mean of 20 Presidents Science: Mean of 13.7 Presidents Other: Mean of Presidents
26 ANALYSIS OF CATEGORICAL VARIABLE 1 Click to add text Count: Yes: 22 People No: 21 People Percent: Yes: 51.16% No: 48.84%
27 ANALYSIS OF CATEGORICAL VARIABLE 1
28 ANALYSIS OF CATEGORICAL VARIABLE 1
29 ANALYSIS OF CATEGORICAL VARIABLE 1 Gender does have an affect on our categorical data when looking at the percentages of females that have not taken an AP or Honors History Course compared to the Males that have not take an AP or Honors History Course. Example: 14.63% of the Females have not taken an AP or Honors History Class While 36.59% of the Males have not taken an AP or Honors History Class. There is a great distance between the two percentages; great enough to have an affect on our categorical data. If we only looked at the Female Gender percentage we would not have enough data or a high enough percent as we would if we looked at the Male Gender percentage.
30 ANALYSIS OF CATEGORICAL VARIABLE 2 Count: History: 8 People Science: 10 People Math: 12 People English: 9 People Other: 4 People Total: 43 People Percent: History: 18.6% Science: 23.26% Math: 27.91% English: 20.93% Other: 9.3% Total: 100%
31 ANALYSIS OF CATEGORICAL VARIABLE 2
32 ANALYSIS OF CATEGORICAL VARIABLE 2 Gender does have an effect on our categorical variable. This can be concluded when looking at the stacked bar graph to the left. There were no males who had English as their favorite subject, so the females were dominant in that subject. There were 9 males who's favorite subject was Science compared to the 1 female who's favorite subject was Science. There was not a consistent or equal distribution of data of each gender for each subject; that is why gender does have an affect on our categorical variable of favorite subject.
33 CONCLUSION In our project there was no errors in any of our calculations or data. There might have been some bias when we were gathering our data because it seemed like the people who did not have an interest in history whatsoever, seemed to give up before the time ran out. These people also had a lack of self confidence in themselves and would easily get embarrassed because often Benjamin Franklin was named as being one of the Presidents of the United States (this is false, he was one of the founding fathers).
34 CONCLUSION Quantitative Data From our data we learned a lot of information. When looking at the summary statistics of our quantitative data we learned that the median number of presidents that were named was 14. We were surprised that the minimum of our quantitative data was four presidents, especially since their are a couple of Presidents that have the dame last name. We were shocked that the maximum number of presidents that was named was 36 out of the total 43 (yes there have been 44 Presidents, but Grover Cleveland was president twice during different terms).
35 CONCLUSION Quantitative Data Gender When looking at the Quantitative Data broken down by Gender, the shape of the Males graph is right skew and unimodal while the shape of the Females graph is symmetric and bimodal. The different shape of the graphs allowed us to conclude that gender does have an affect on our quantitative data. Categorical Data 1 The shape of the Yes graph (Students that have taken an AP/Honors History Course) is right skewed and unimodal while the No graph (Students that have not taken an AP/Honors History Course) is unimodal and symmetric. Whether or not the person has taken an Honors or AP History Class does have an effect on our quantitative data. Categorical Data 2 The categorical variable does have an effect on our quantitative data because it changes the amount of the data when looking at each subject.
36 CONCLUSION Analysis of Categorical Variable % of the people have taken an AP or Honors History Class, while 48.84% have not. Gender does have an affect on our categorical data. Analysis of Categorical Variable 2 The people of which we collected our data on, their favorite subjects were: History: 18.6%; Science: 23.26%; Math: 27.91%; English: 20.93%; and Other: 9.3%. Gender does have an effect on our categorical variable.
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