Firstly to prove that my correlation is a positive and is correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.
Stem and leaf Diagram
This is the stem and leaf diagram for the correlation between sick days’s and weight, to find the mode, mean and median now should be easier
Average of weight: (E2:E51) = 73.18
Average sick days: (F2:F51) = 3.16
That is one way of finding an average to support the correlation between weight and sick days. Here is another way:
Mode number of weight: 57
Mode number of sick days: 10
This average is saying spending that people who have weight of 57 and the number of sick days that the person will have is 10, this shows that correlation is correct and is positive.
Another way that I could use to support my correlation between weight and sick days I would find the mean weight and sick days:
Mean of weight: 58, 63, 64, and 65 =62.5
Mean of sick days: 12, 10, 10, and 12=11
Here it is also saying that if you weight is 62.5 then you will have 11 sick days which is showing the more you weigh the more number of sick days.
Median of weight: (E2:E17) = 63.5
Median of sick days: (F2:F17) = 12
Interquaritle range:
To find the interquartile range of weight:
Lower quartile range on weight:
¼(N+1) =
¼(11+1) =third term=60
Upper quartile range on weight:
¾(N+1) =
¾(11+1) =nine term= 89
Lower quartile range on sick days:
¼(N+1) =
¼(11+1) =third term=6
Upper quartile range on sick days:
¾(N+1) =
¾(11+1) =nine term=14
We know the average of the correlation and we can now prove that correlation between weight and sick days is correct that if less weight you gain the more number of sick days you will have
This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between weight and sick days is correct.
Correlation between: The more amounts of sick days the more amount of salary lost
Explanation on correlation
This is a following scatter graph on the following correlation and is a negative correlation between the more amount of sick days you will have the more amount of salary lost. The correlation is showing that if people have so many sick days then they will start to lose their salary. I am now going to show how I can prove that the following negative correlation is right or wrong by taking the mean median and mode and the average of the correlation. . I will be also having a stem and leaf diagram and an interquaritle range which will also prove my correlation.
Firstly to prove that my correlation is negative and correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.
Stem and leaf Diagram
This is the stem and leaf diagram for the correlation between sick days’s and weight, to find the mode, mean and median now should be easier
Average sick days: (F2:F51) = 3.16
Average of salary: (C2:C51) = 28683.67
That is one way of finding an average to support the correlation between sick days and salary. Here is another way:
Mode number of sick days: 10
Mode number of salary: 21000
This average is saying that people who have taken 10 sick days he will only get paid 21,000 this proves that my correlation is negative and that is correct.
Another way that I could use to support my correlation between sick days and salary. I would find the mean weight and salary:
Mean of sick days: 12, 10, 10, and 12=11
Mean of salary: 17, 000, 18, 000, 18, 500, 19,000=181500
Here it is also saying that if you have 11 sick days you will only get paid 181500 as your salary which is showing the more days you take off the less amount of money you get paid.
Median of sick days: (F2:F17) = 12
Median of salary: (C2:C17) = 21000
Interquaritle range:
Lower quartile range on sick days:
¼(N+1) =
¼(11+1) =third term=6
Upper quartile range on sick days:
¾(N+1) =
¾(11+1) =nine term=14
Lower quartile range on salary:
¼(N+1) =
¼(11+1) =third term=2100
Upper quartile range on salary:
¾(N+1) =
¾(11+1) =nine term=2750
We know the average of the correlation and we can now prove that correlation between sick days and salary is correct that if you take so many days off you will lose a lot of salary.
This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between salary and sick days is correct.
Correlation: The more hours spent in the gym the lesser amount of sick days.
Explanation on correlation
This is a following scatter graph on the following correlation and is a positive correlation between the more gym hours you will spend in the gym the less amount of sick days you will have. The correlation is showing that if people stay healthy then they will have less sick days. I am now going to show how I can prove the correlation by taking the mean and median and mode of certain numbers and then proving that the correlation is correct. I will be also having a stem and leaf diagram and an interquaritle range which will also prove my correlation.
Firstly to prove that my correlation is positive and correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.
Stem and leaf Diagram
This is the stem and leaf diagram for the correlation between sick day and weight, to find the mode, mean and median now should be easier
Average of Gym hours (D2:D17) =-3.375
Average sick days: (F2:F51) = 3.16
That is one way of finding an average to support the correlation between gym hours and sick days. Here is another way:
Mode number of gym hours: 14hrs
Mode number of sick days: 3
This average is saying spending 14hrs at the gym will make you only have three sick days which is supporting my correlation.
Another way that I could use to support my correlation between gym hours and sick days. I would find the mean of gym hours and sick days.
Mean of gym hours: 10, 12, 14, 14 = 15
Mean of sick days: 2, 3, 4,5=4
Here it is also saying that in 15hrs at the gym will make you healthier and only let you have four sick days.
Medium of gym hours :( D2:17) = 2
Median of sick days: (F2:F17) = 12
Interquaritle range:
Lower quartile range on gym hours:
¼(N+1)
¼(11+1) =third term=10
Upper quartile range on gym hours:
¾(N+1) =
¾(11+1) =nine term=14
Lower quartile range on sick days:
¼(N+1) =
¼(11+1) =third term=6
Upper quartile range on sick days:
¾(N+1) =
¾(11+1) =nine term=14
We know the average of the correlation and we can now prove that correlation between gym hours and sick days is that the more hours you spend in the gym the lesser the amount of sick days. This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between gym hours and sick days is correct.
Correlation: As you get older the more weight you gain
Explanation on correlation
This is a following scatter graph on the following correlation and is a positive correlation between age and weight, the older you get the more weight you gain. I am now going to show how I can prove the correlation by taking the mean and median and mode of certain numbers and then proving that the correlation is correct. I will be also having a stem and leaf diagram and an interquaritle range which will also prove my correlation.
Firstly to prove that my correlation is positive and correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.
Stem and leaf Diagram
This is the stem and leaf diagram for the correlation between sick day and weight, to find the mode, mean and median now should be easier
Average of age :( B2:B51) = 36.98
Average of weight: (E2:E51) = 73.18
That is one way of finding an average to support the correlation between age and weight. Here is another way:
Mode number of age: 18
Mode number of weight: 57
This average is saying people that aged 18 should weigh around 57 which shows that my correlation is correct the older you grow the more you weigh.
Mean of age: 17, 18, 19, 20=19
Mean of weight: 58, 63, 64, and 65 =62.5
Here it is also saying that people who are aged 19 while have a weight of 62.5 this is proving my correlation. The mean of the age and weight is proving that my correlation is correct.
Median of age: (B2:B17) = 21
Median of weight: (E2:E17) = 63.5
Interquaritle range:
To find the interquartile range of weight:
Lower quartile range on weight:
¼(N+1)=
¼(11+1) =third term=60
Upper quartile range on weight:
¾(N+1) =
¾(11+1) =nine term= 89
Lower quartile range on age:
¼(N+1) =
¼(11+1) =third term=19
Upper quartile range on age:
¾(N+1) =
¾(11+1) =nine term=32
We know the average of the correlation and we can now prove that correlation between age and weight is correct that as people get older they tend to weight heavier. This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between gym hours and sick days is correct.
Correlation: The older you become the more salary you are paid.
Explanation on correlation
This is a following scatter graph on the following correlation and is a positive correlation between the older you become the more your salary. The correlation is showing that if people who are older earn more money then younger people I am now going to show how I can prove the correlation by taking the mean and median and mode of certain numbers and then proving that the correlation is correct. . This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between gym hours and sick days is correct.
Firstly to prove that my correlation is positive and correct I will create a stem and leaf diagram which is a diagram that shows data in a systematic way and the mode median and mean can be found easily from this. Once the stem and leaf diagram is created I can then find the average, mean, mode and median of the correlation.
Stem and leaf Diagram
This is the stem and leaf diagram for the correlation between sick day and weight, to find the mode, mean and median now should be easier
Average of age :( B2:B51) = 36.98
Average of salary: (C2:C51) = 28683.67
That is one way of finding an average to support the correlation between age and salary. Here is another way:
Mode number of age: 18
Mode number of salary: 21000
This average is saying people that aged 18 only earn 21,000 which shows that my correlation is correct.
Mean of age: 17, 18, 19, 20=19
Mean of salary: 17, 000, 18, 000, 18, 500, 19,000=181500
Here it is also saying that people who are aged 19 earn 181500 this shows that my correlation is wrong by using the mean I cannot prove my correlation.
Median of age: (B2:B17) = 21
Median of salary: (C2:C17) = 21000
Interquaritle range:
Lower quartile range on age:
¼(N+1) =
¼(11+1) =third term=19
Upper quartile range on age:
¾(N+1) =
¾(11+1) =nine term=32
Lower quartile range on salary:
¼(N+1) =
¼(11+1) =third term=2100
Upper quartile range on salary:
¾(N+1) =
¾(11+1) =nine term=2750
We know the average of the correlation and we can now prove that correlation between age and salary that people who are older do get more salary it is now proven that my correlation is correct.
This is because the stem and leaf diagram has made it easier to fine the mean mode and median. I have also found the interquartal range I have now proven that the correlation between gym hours and sick days is correct.
