First of all water (H2O) is made from 2 atoms of hydrogen and 1 atom of oxygen. A mole of water molecules would be 2 moles of hydrogen atoms plus 1 mole of oxygen atoms. So lets say x = 3m, m=1, and x=3, because 3 x 1 = 3 = x, and r = radius of drinking fountain's water dispense, and AOW = amount of water produced in a minute, and y = (x*(r*(AOW/r))... So Y = a, and a = answer... Meaning your answer is (y = (x*(r*(AOW/r)) )!
Suspension is a heterogeneous mixture (solute particles do not dissolve), that contains solid particles (for example nickel sulfide or NiS) sufficiently large for sedimentation.
The internal phase (solid nickel sulfide) is dispersed throughout the external phase (water).
<h3>Explanation;</h3><h2>Trying to address this part of your question -"This question stems from the fact that all data scientists state that data generated by people will grow exponentially and whe need as many data scientists as possible to tackle this phenomenon. And so I wondered what will be the difference between what we can discover now in our data and what can we discover in the future when we will have exponentially more data."</h2><h2 /><h2>Trying to address this part of your question -"This question stems from the fact that all data scientists state that data generated by people will grow exponentially and whe need as many data scientists as possible to tackle this phenomenon. And so I wondered what will be the difference between what we can discover now in our data and what can we discover in the future when we will have exponentially more data."It depends on what field the data is related to - is it related to areas we already know a lot about - it probably wont make a whole lot of difference if the distribution of the data is similar to your current data set.</h2><h2>Trying to address this part of your question -"This question stems from the fact that all data scientists state that data generated by people will grow exponentially and whe need as many data scientists as possible to tackle this phenomenon. And so I wondered what will be the difference between what we can discover now in our data and what can we discover in the future when we will have exponentially more data."It depends on what field the data is related to - is it related to areas we already know a lot about - it probably wont make a whole lot of difference if the distribution of the data is similar to your current data set.If the data is related to areas where we did not have a whole lot of existing data - space, weather or even all the new fitness trackers we all wear these days.</h2><h2>Trying to address this part of your question -"This question stems from the fact that all data scientists state that data generated by people will grow exponentially and whe need as many data scientists as possible to tackle this phenomenon. And so I wondered what will be the difference between what we can discover now in our data and what can we discover in the future when we will have exponentially more data."It depends on what field the data is related to - is it related to areas we already know a lot about - it probably wont make a whole lot of difference if the distribution of the data is similar to your current data set.If the data is related to areas where we did not have a whole lot of existing data - space, weather or even all the new fitness trackers we all wear these days.The fitness tracker data wasnt available a few years back, so more data is probably going to change the data, especially once the adoption moves from the early adopters (tech and fitness geeks) to other people - old people. kids etc - bet there is not much data related to them.</h2><h2 /><h2>Trying to address this part of your question -"This question stems from the fact that all data scientists state that data generated by people will grow exponentially and whe need as many data scientists as possible to tackle this phenomenon. And so I wondered what will be the difference between what we can discover now in our data and what can we discover in the future when we will have exponentially more data."It depends on what field the data is related to - is it related to areas we already know a lot about - it probably wont make a whole lot of difference if the distribution of the data is similar to your current data set.If the data is related to areas where we did not have a whole lot of existing data - space, weather or even all the new fitness trackers we all wear these days.The fitness tracker data wasnt available a few years back, so more data is probably going to change the data, especially once the adoption moves from the early adopters (tech and fitness geeks) to other people - old people. kids etc - bet there is not much data related to them.Without knowing what type of data, the amount and quality of existing data, it is hard to generalize.</h2><h2><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u> </u></em><em><u>Confirm</u></em><em><u>.</u></em><em><u> </u></em></h2><h2><em><u>#Brainliest</u></em><em><u> </u></em><em><u>Answer</u></em></h2>
