# data[].attributes.body
STEP 1:  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,  3, 3, 3, 3, 3 ,3, 3 , 4, 4, 4, 4, 4, 5, 5, 5, 5 ,5, 5, 6, 6, 6, 6, 6, 6, 6
STEP 2 :  median is 2
STEP 3 ; (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2 ) 2,  (3, 3, 3, 3, 3 ,3, 3 , 4, 4, 4, 4, 4, 5, 5, 5, 5 ,5, 5, 6, 6, 6, 6, 6, 6, 6)
Q1 = 1
Q2= 2
Q3 =  4

# data[].attributes.body
Step 1: Put the numbers in order.
Step 2: Find the median.
Step 3: Place parentheses around the numbers
above and below the median.
Step 4: Find Q1 (median of lower part of half) and Q3 (median of upper part of half)

# data[].attributes.body
Step 1: Put the numbers in order.
Step 2: Find the median.
Step 3: Place parentheses around the numbers
above and below the median.
Step 4: Find Q1 (median of lower part of half) and Q3 (median of upper part of half)
Step 5: Subtract Q1 from Q3 to find the interquartile range.

# data[].attributes.body
8

# data[].attributes.body
12 - 4 = 8

# data[].attributes.body
Q1 = 4
Q2 = 8
Q3 = 12

# data[].attributes.body
as there is no exact value, the key of finding the mean  with a continuous  data is to assign a mid- value for x in each interval: 5, 7, 9, 11, 13
then calculate the mean with this values:
total weight x freq: (5 x 5) + (7 x 10) + (9 x 8) + (11 x 5) + (13 x 2)= 248kg
total frequency: 5 + 10 + 8 + 5 + 2= 30
248 divided 30 = 8.267
mean= 8.267

# data[].attributes.body
as the mode is the one that ocuurs most often, in this case the table showing the frequency of each avergae weight can tell us the mode, which is 6-8 kg because it occcured most often amount of times

# data[].attributes.body
in this case the data is grouped in on part by the weight  and in the other part by  the frequency (the amount of times that average weight has been recorded).

# data[].attributes.headline
to find the middle value you simply have to get the value that is in the middle, after the data has been placed by order by size. If there are 2 values in the middle you will have to add both up and divide them by 2 

# data[].attributes.subject
to find the middle value you simply have to get the value that is in the middle, after the data has been placed by order by size. If there are 2 values in the middle you will have to add both up and divide them by 2 

# data[].attributes.body
In this case the range is calculated by doing 6-1 that gives you 5

# data[].attributes.headline
the difference ( - ) between the largest and smallest value, it is useful to know how spread out the observations are.

# data[].attributes.subject
the difference ( - ) between the largest and smallest value, it is useful to know how spread out the observations are.

# data[].attributes.body
STEP 1:  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,  3, 3, 3, 3, 3 ,3, 3 , 4, 4, 4, 4, 4, 5, 5, 5, 5 ,5, 5, 6, 6, 6, 6, 6, 6, 6
STEP 2 :  median is 2
STEP 3 ; (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2 ) 2,  (3, 3, 3, 3, 3 ,3, 3 , 4, 4, 4, 4, 4, 5, 5, 5, 5 ,5, 5, 6, 6, 6, 6, 6, 6, 6)
STEP 4: Q1 = 1 / Q3 = 4
STEP 5: 4 - 1= 3
The inter-quartile  range is equal to 3

# data[].attributes.headline
is a measure of variability, based on dividing a data set into quartiles

# data[].attributes.subject
is a measure of variability, based on dividing a data set into quartiles

# data[].attributes.body
In this case the mean will be calculated by doing,
all the scores: (1 x 15) + (2 x 10) + (3 x 7) + (4 x  5) + (5 x 6) + (6 x 7) = 148
amount of die rolled: 15 + 10  + 7 + 5 + 6 + 7 = 50
148 divided 50 = 2.96
The mean is 2.96

# data[].attributes.headline
it is found by adding up the values and dividing the answer by the number of values.

# data[].attributes.subject
it is found by adding up the values and dividing the answer by the number of values.

# data[].attributes.headline
You can fiend the mode by looking at the most amount of  frequency

# data[].attributes.subject
You can fiend the mode by looking at the most amount of  frequency

# data[].attributes.body
In this case the mode of the results after rolling a normal die 50 times is to score 1 because it is the scores that occurred most often.

# data[].attributes.headline
- is the middle value once the data has been ranked: placed in order of size.

# data[].attributes.subject
- is the middle value once the data has been ranked: placed in order of size.

# data[].attributes.headline
Continuous Data                                                                                                           EXAMPLE

# data[].attributes.subject
Continuous Data                                                                                                           EXAMPLE

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-  Information that can be measured on a continuum or scale.
-  Can have almost any numeric value
-  Can be meaningfully subdivided into finer and finer increments,
depending upon the precision of the measurement system

# data[].attributes.body
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,  3, 3, 3, 3, 3 ,3, 3 , 4, 4, 4, 4, 4, 5, 5, 5, 5 ,5, 5, 6, 6, 6, 6, 6, 6, 6
as there is a total of 50 values the middle value in this case wil be the 25th, that means the middle vaule is equal to 2

# data[].attributes.headline
 is a type of quantile which divides the number of data points into four more or less equal parts, or quarters.

# data[].attributes.subject
 is a type of quantile which divides the number of data points into four more or less equal parts, or quarters.

# data[].attributes.body
Q1= Is the  lower quartile, it is the middle value of the lower half. It is s a point which has 25% of the observations below it and 75% of the observations above it.
Q2= Is the median that divides the data into a lower half and an upper ha
Q3=Is the  upper quartile is the middle value of the upper half. It is a point which has 75% of the observations below it and 25% of the observations above it.

# data[].attributes.body
Kognity : https://app.kognity.com/study/app/cambridge-igcse-mathematics-fe2020/statistics/averages-and-range/location-and-spread-for-ungrouped-data/

Dictionary:
https://www.isixsigma.com/dictionary/discrete-data
https://www.isixsigma.com/dictionary/continuous-data/
https://www.investopedia.com/terms/s/statistics.asp
: https://www.emathzone.com/tutorials/basic-statistics/quartiles.html#ixzz6MMYpCtpy

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Discrete Data                                                                                                           EXAMPLE

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Discrete Data                                                                                                           EXAMPLE

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-  Information that can be categorized into a classification
-  Based on counts
-  Only a finite number of values is possible
-  The values cannot be subdivided meaningfully
-  It is typically things counted in whole numbers