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Name/Notes Owner Created Size Views
Kids Vs. Adults Phone Screen Time By Karl F.
This compares the daily phone screen time in hours of people 21 and below against people 22 and above. This was obtained through asking 50 kids and 50 adults about the amount of time they spend looking at their phone, excluding calling and other devices such as computers and television. The majority of the kids for the sample were random teenagers I surveyed in Victoria Gardens, the rest were my school and church friends. The 50 adults for the majority of the sample were also random people I surveyed at Victoria Gardens, the rest were adults that attend my church and some neighbors. The results show that kids spend more time on their phones than adults, the kids' graph is symmetrical while the adults graph takes on more of a skewed right shape. There are no outliers for adults but there is one outlier of 12 hours a day for the kids graph, and the spread of the adults' graph is wider due to the bigger range of age that was accepted and depending on the adults' life circumstances and jobs.
karldoggo37Oct 7, 2019174B18
Marcella Gibson - Lab 2 - Frequency Female Educational Attainment
A population survey was conducted surveying 101.6 million females 25 years old or older in a certain country. According to the data obtained the chart shows 34.4 million females graduated from high school. This reflects the highest group of education obtained for females. Where advanced degrees was the lowest group for females with only 8.1 million. 12.9 million females did not graduate from high school. 19.2 million females obtained some college. 8.8 million obtained an Associates degree, and 18.4 million obtained a Bachelors degree.
mgibson60Oct 5, 2019174B14
Marcella Gibson - Lab 2 - Frequency Male Educational Attainment
A population survey was conducted surveying 85.6 million males 25 years old or older in a certain country. According to the data obtained the chart shows 27.2 million males graduated from high school. This reflects the highest group of education obtained for males. Where advanced degrees was the lowest group for males with only 8.1 million. 10.4 million males did not obtain a high school diploma. 16.9 million males had some college. 7.5 million obtained an Associates degree, and 17.9 million obtained a Bachelors degree.
mgibson60Oct 5, 2019174B8
Histogram of Age
This graph has the mean and median calculated if that helps any for question 4 about the proper average.
hodges.tianaSep 24, 2019174B7
Family Heights-Faith Kimmel
I am unsure how to make one graph using all four columns of data (mom, dad, kids) I have watched the training videos and played around with this for an hour. I would like to know how to combine this information into one graph with each member a different color.
faith.white_36Sep 15, 2019174B6
#5 bar plot that shows the relationship between community size and deaths
Tornadoes are more likely to hit rural areas. I have come to this conclusion because there is a significantly larger amount of deaths in the rural areas compared to the cities.
cls06970Sep 9, 2019174B6
Pie Chart With Data
this is from Nicole Queliz
emanuelatoshi1Sep 5, 2019174B5

Variable: var1



Decimal point is 1 digit(s) to the right of the colon.
Leaf unit = 1
1 : 6777777777777788888888888888888888888888888889999999999999999999999999999
2 : 0000000000000001111111112222222222223333
2 : 5567778999
3 : 0014
3 : 7999
4 : 0014
4 : 5
5 : 
5 : 8

Stem and Leaf Plot for number 1
print this
amen1240Aug 26, 2019323B14
Simple Linear Regressionvpham012Jul 24, 2019174B23
Histogram_Free Lunch_Mode 5_StatTalk4
Is the Histogram Normally distributed? Why or Why Not? What are the Mean and Standard Deviation for this data set?
lhandc04Jul 19, 2019174B27
Binomial Calculator
This is Module 6 Question #2
ejesse1287Jul 7, 2019174B27
Angela Whitiker Lab 6 (Scatter Plot)
A pediatrician wants to determine the relation that exists between a​ child's height,​ x, and head​ circumference, y. She randomly selects 11 children from her​ practice, measures their heights and head​ circumferences, and obtains the accompanying data. Plot Interpretation A close glimpse of the scatter plot data and the regression line reveals a positive upward trend of the Height measurement as a function of the Head circumference relationship. Analysis of the correlation coefficient ( r ) value derived from the Height vs. Head Circumference data validates the above observation. With a value ( r = 0.8881 ), a compelling case can be made that for the data under consideration a positive linear relationship exists between the two variables (Height vs. Head Circumference). Although the relationship is not altogether perfect ( for r = 1.000 ), nevertheless there exits a strong relationship. Regression Line Model Y = 0.184 X + 12.494 For the linear equation above representing the regression line where X (the independent variable) represents Height and Y (the dependent variable) represents Head Circumference. The slope value (m = 0.184) represents a positive upward relationship between the two variables where for each increase of one inch in Height, correspondingly there is going to be an increase of 0.184 inch in Head circumference. The value of 12.494 represents the value that would have existed given a height of 0 inch, but in this case or situation this value has no practical significance. In other words no normal child would exist with a body height of 0 inch. As had been already discussed previously for the Height vs. Head Circumference scenario, both the r value which indicate a positively correlation coefficient between the two variables and a visual inspection of the scatter plot validating that relationship, one could firmly conclude that the mean value of the Head Circumference could not be the same as the predicted value of the Head Circumference. Coefficient of Determination For the Height vs. Head Circumference scenario, the coefficient of determination was calculated and evaluated to be about 0.7887 ( or 78.87% ) suggesting the percentage of the total variation in the Head Circumference that is explained by the variation in the Height variable in the regression model. Alternatively, a large Coefficient of Determination value implies that the explained variation is a large portion of the total variation. Proportion of Variability R2 = r2 = (0.8881) 2 = 0.7887 ( or 78.87% ) Again for the present scenario, the Proportion of Variability value in the Head Circumference explained by the relation between Height and Head Circumference is 78.87%.
awhitikerMay 10, 2019174B79
Angela Whitiker Chocolate Chips Cookies Sampled per Brand
The data to the right represent the number of chocolate chips per cookie in a random sample of a name brand and a store brand. Statistical Numerical Analysis Summary (Below) Brand n IQR LF LL Q1 Q2 Q3 UL UF Values Median Store 13 7 10 15 20 23 27 33 38 Name 13 6 14 22 23 26 29 35 36 Data Interpretation a) For Store Brand, 25% of the cookies seems to contain 20 (first quartile) chocolate chips per cookie or less while 75% represent cookies with 20 chocolate chips per cookie or more. Additionally, 50% of the cookies contain 23 (second quartile) chocolate chips per cookie or less and 50% of the cookies are equal to or greater than 23 chocolate chips per cookie. Finally, 75% of the cookies contain chocolate chips per cookie estimated to be 27 (third quartile) or less while 25% of the cookies contain chocolate chips per cookie count above 27. For Name Brand, 25% of the cookies seems to contain 23 (first quartile) chocolate chips per cookie or less while 75% represent cookies with 23 chocolate chips per cookie or more. Additionally, 50% of the cookies contain 26 (second quartile) chocolate chips per cookie or less and 50% of the cookies are equal to or greater than 26 chocolate chips per cookie. Finally, 75% of the cookies contain chocolate chips per cookie estimated to be 29 (third quartile) or less while 25% of the cookies contain chocolate chips per cookie count above 29. While the boxplot visual for the Store Brand appears symmetrical, but a closer examination reveals that the data is slightly skewed to the right due in part to the corresponding median value location. As for the Name brand boxplot visual, it is without a doubt a plot that is convincingly skewed to the right. Analysis of the Upper Fence (UF) and Lower Fence (LF) data give us firm confidence that none of the data point above can be characterized as outliers because both the Upper Limit (UL) and Lower Limit (LL) values for both brands fall within the UF and LF limits listed above. Boxplots are an extraordinary and powerful tool in analyzing data relevant to a particular scenario because they provide a convenient way in gathering data in an orderly fashion for making critical decision. From a practical standpoint they provide a top down view in visualizing data clustered around a certain region that would have been rather difficult to capture using a different method. They also make it relatively easy to detect outliers depending how skewered the data is. b) Affirmatively there exists a difference between the chocolate chips count per cookie between the two brands. A close comparison analysis of the central tendency data for the two brands shows clearly that the mean value count for the Name Brand is higher in this regard than the Store Brand. For some people, this alone might be a dealbreaker. Say for instance for people who love chocolate, this information alone will motivate them to purchase or choose the Name Brand cookies over the Store Brand’s. Or vice-versa, if the person or individual does not like chocolate, when faced to make a decision between the two brands that person might be inclined to go with the Store Brand cookies. c) To answer this question, we have to take another look of the data above one more time. One parameter in particular that requires our attention for this task is the Interquartile Range or (IQR). Because IQR is a function of the spread of the data, therefore the higher the value the greater the spread and vice versa. With that in mind, data comparison between the Store Brand and Name Brand IQR values should lead us to conclude that the Name Brand’s cookies have a more consistent chocolate chips count per cookie than the Store Brand’s. Name Brand ==> IQR = 6 LL = 22 Q1 = 23 Median = 26 Q3 = 29 UL = 35; Store Brand ==> IQR = 7 LL = 15 Q1 = 20 Median = 23 Q3 = 27 UL = 33;
awhitikerMay 6, 2019174B108
Ivy Ogo Lab 3Frequency Histogram
The frequency histogram shows that there were 4 of the regions of this country who experienced between 100 and 199.9 crimes. 15 of the regions who experienced between 200 and 299.9 crimes. ​ 10 of the regions who experienced between 300 and 399.9 crimes. 10 of the regions who experienced between 400 and 499.9 crimes. 3 of the regions who experienced between 500 and 599.9 crimes. 8 of the regions who experienced between 600 and 699.9 crimes. 0 of the regions who experienced between 700 and 799.9 crimes. Between 800 and 1299.9 no crime is shown, and 1 of the regions experienced between 1300 and 1399.9 crimes. If I was a Police Chief, I would send more police officers to the 15 of the regions who their crime rate is between 200 and 299.9. Also, I would assign more police officers to the 1 of the regions who show the highest crime rate which is between 1300 and 1399.9.
sakura2011Apr 30, 2019174B34
ivy Ogo lab 3 Relative Frequency Histogram
the data shown in the Relative Frequency histogram shows the violent-crime rate by regions of a country. Violent crime which include murder, forcible rape, robbery, and aggravated assault. The relative frequency histogram shows that there were 8% of the regions of this country who experienced between 100 and 199.9 crimes. 29% of the regions who experienced between 200 and 299.9 crimes. ​ 20% of the regions who experienced between 300 and 399.9 crimes. 20% of the regions who experienced between 400 an9d 499.9 crimes. 6% of the regions who experienced between 500 and 599.9 crimes. 16% of the regions who experienced between 600 and 699.9 crimes. 2% of the regions who experienced between 700 and 799.9 crimes. Between 800 and 1299.9 no crime is shown, and 2% of the regions experienced between 1300 and 1399.9 crimes. If I was a Police Chief, I would send more police officers to the 29% of the regions who their crime rate is between 200 and 299.9. Also, I would assign more police officers to the 2% of the regions who show the highest crime rate which is between 1300 and 1399..
sakura2011Apr 30, 2019174B34

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