Great !❤ But understand, this is a continuous frequency distribution it means the class interval is present, there is not any kind of fix value observation or data value. Therefore you have to assume the mode, now try to think what thing is important to measure the mode If the data value is in interval and not fix or not a discrete value. So you must have to approximate and try to indicate mode with the help of previous and upcoming frequencies.. As we know that mode is basically higher frequency class but actually which value will be our Mode we can't find out in that class interval... Therefore with the help of previous and upcoming frequencies we join that two lines because to fix the Mode value and as upcoming and previous frequencies fluctuate the mode exact value also fluctuate.. If you join the line and let's suppose the upcoming frequencies more than previous frequency of the modal class.. Then join the line you can clearly see that mode slightly shift towards upper limit of class interval.. So basically here we predict, that yes previous frequency is lower and upcoming frequency is higher than the previous so we only predict that Mode maybe have to lie there.. But finally yes it's not exact it's also a kind of approximation so that we can find the mode if we have continuous frequency distribution table. For our simplicity we do this ! Hopefully you get it ! Please give me your feedback ☺❤🙏
Basically, you can't calculate the exact mode value.. 🔴THINK 🤔 :- if there is "class intervals" present, hence there is no any fix constant value of observation or constant data value... So, now you know that mode is that observation or data value which repeats maximum Times so you have to see the maximum frequency class but in that there is a range of observation or there is a range of data value at that particular maximum frequency class.. Clearly in this class range you can't find which number will be the mode so we predict it without finding the actual mode as we can't know that okay so it's a kind of probability as we predict it by the adjacent frequency. Now your question is why the frequency is affected by this adjacent frequency so listen... As mode depends on the frequency as which frequency is more than, that will be the model class and from that model class I have to select the mode value and mode only depends on the frequency.. therefore we compare mode with the adjacent frequencies of different classes as there is nothing in the whole data set to compare the mode with other thing.😊 I will again say that we can only predict it, not it's 100% accurate. Hopefully you get this😊 Please give me your feedback to this if you have any Query😊❤.
@@Yourmaths thanku for clearing my doubt😊 I want to know what we do for getting 100% sure result . And as mode is the class with highest frequency, in a grouped data, for example, there are two class intervals 20-30 and 30- 40 In 20-30 total frequency is 12 and frequency of 24 let's say is 10 in it On other hand in 30-40 total frequency let's say is 19 and frequency for its components , i.e., 32 is 7 33 is 6 35 is 6 Then the modal class on basis of class interval is 30-40 but in reality the frequency of 24 which lies in 20-30 is 10 which is maximum. How will we deal with this condition.
Great !❤😊,, But try to know when you fix the value of data value or when you fix the value of the observation as it is 24, 32, 33... Then, this will become now discrete grouped data it's not Continious grouped data. As in the interval of 20-30, you have fixed the observation which is 24 and their frequency as well which is 10. So any other observation in between 20 to 30 must have only two frequency left.. And the model class having maximum frequency you also divided it and you make it a discrete grouped data it's not Continious Grouped one.. Therefore, the model class condition not satisfy here, as its not, you know, discrete grouped data its basically continious grouped data.. 🔴Continious Grouped data means observation lie in the interval form.. 🔴discrete grouped data means observation is discrete it means there is a fix value of the observation. Now in these two, Why it's called grouped data because it's grouped in the tabular form and frequency for a particular observations is fix and it's also not repeat and this is grouped data.😊 Now, please give me your feedback if you have any Query😊❤...
Everything was fine But one thing actually Why we connected the lines in the graph that formed the triangles and why only that lines why not others? Please answer today as my exam is tomorrow and I have to remember it Thanks for all the derivations
Great !☺ But try to understand this is a continuous frequency distribution it means the class interval is present, there is not any kind of fix value observation or data value. Therefore you have to assume the mode, now try to think what thing is important to measure the mode If the data value is in interval and not fix or not a discrete value. So you must have to approximate and try to indicate mode with the help of previous and upcoming frequencies.. As we know that mode is basically higher frequency class but actually which value will be our Mode we can't find out in that class interval... Therefore with the help of previous and upcoming frequencies we join that two lines because to fix the Mode value and as upcoming and previous frequencies fluctuate the mode exact value also fluctuate.. If you join the line and let's suppose the upcoming frequencies more than previous frequency of the modal class.. Then join the line you can clearly see that mode slightly shift towards upper limit of class interval.. So basically here we predict, that yes previous frequency is lower and upcoming frequency is higher than the previous so we only predict that Mode maybe have to lie there.. But finally yes it's not exact it's also a kind of approximation so that we can find the mode if we have continuous frequency distribution table. For our simplicity we do this ! Hopefully you get it ! Please give me your feedback ☺❤🙏
Why does the mode have to be in the modal class? For example, in the data given in the video, if 21 comes two times, 22 comes two times, but 13 comes 3 times, the actual mode is 13, which does not lie in the modal class.
Great☺, I appreciate your thinking ❤, But try to understand this is not a discrete frequency distribution table, it's a continuous frequency distribution table. Means the data value observation or the term is is not a particular value its continuous therefore we will calculate the mid value of the class interval to predict the data value or to understand the data value in discrete form. But as you know that this is not a actual value of the class interval as it can be anything in between that class interval. Maybe in between that class interval there are two different data value and in that case we can't even predict the mode of a continuous grouped data. But in this we will try to make it simple by saying that in this class interval there may be a number which is maximum and that average number is mostly repeatedly come ! So basically, you basically try to calculate the mode that it must be here but no needed it's an assumed mode not real because the whole table is depending on the prediction. So, the answer to this question is you can't predict exact mode. According to your question as we have given the scenario at that time it's not a continuous frequency distribution table at that time it becomes discrete frequency distribution table at that time we can predict the exact mode as you have predicted that is 13 ! But here we are discussing about the continuous frequency distribution table where the mode is only predictable not exact ! Hopefully you get it, please give me your feedback❤☺🙏
@@Yourmaths first of all, thank you so much for this video sir. I understand that we assume the data to be continuous, and predictable. But the problem with this whole intersecting lines is that it assumes the frequency to be a perfect curve ( of sorts ) rising up and falling down, with the assumption that greater the value of 'x', the greater the frequency f0. it is like it assumes that the mode is being "attracted" by greater frequencies, which is clearly not true as only one mode can exist, and which again, may not lie in the modal class! My question is - why go through such an elaborate process to give the incorrect answer for mode?? this formula is like - conducting a competition among people, to give the prize to the highest scorer, but you don't know their names, or their scores individually, you've just grouped them in "modal classes" and you have taken the three highest "modal classes" ( that arbitrarily happened to be grouped together ) and found the mean between them, and gave the prize to an arbitrary person ( who might not even have a descent score! ) and no prize to the highest scorers! If we assume so much in a formula, then why does it even exist? Again, I thank you for this video, I have my maths pre-board tomorrow.
Great❤, You have asked such a great amazing question ! I'll try to answer ! It's a continuous frequency distribution table, so the mode is not accurate means mode is predictable mode ! And even we know something about continuous frequency distribution table's mode... Therefore we can say we know something which is far better than doing nothing ! But think that if you eat 1 Roti today then next day 3 roti, next day let's say 5 Roti So in these three days you have eaten 5 + 3 + 1 roti, which is equals to 9 roti. Per day how many Roti have you eaten ? It's 9 / 3 which is three. Means you can say that averagely you have eaten three Roti per day ! But you know that it's not correct if I will only tell you that you have eaten 3 Roti per day then do you think that it's a correct answer ? Absolutely not because you have eaten one Roti one day 3 Roti next day and then five roti. So similarly if there is continuous frequency distribution, we don't know the exact mode what we can predict the mode which is most likely to be the exact mode ! Lastly I want to say that, that you calculated the area of all uniform bodies and shapes like Volume of Cylinder, volume of Sphere, volume of cube, area of triangle, area of rectangle, you have calculated this many things in your maths studies but do you think that in our actual life these type of structure are present more ? No, Because this whole world is not uniform this is non-uniform irregular and those shapes area can never be calculated with these things, we need of function for that calculation which you will learn in the higher classes ! So like that mode is here even though continuous frequency distribution table's part.. Even though we are trying to explain the mode with the help of uniformness.. And not with the help of irregularities ! As Mode can be anything, But as you know that here you can't even find the irregulaness.. Therefore the other option you have is to calculate the mode with the help of uniformness.. As you have done in the example of Roti ! That you have averagely eaten 3 Roti per day which is the uniformness. But in actual you have eaten 1, 3 and then five roti and this is irregularness.. But your thinking is great !❤ Hopefully you get it, please give me your feedback ❤☺🙏
@@Yourmaths Thank you so much for your constructive feedback sir! But in the Roti example, we only calculate the average or the mean, for which, relying on the "uniformness" is the most sensible thing to do, like in my example, if we want the average score of the contestants, then, we do not need to know their names and details.. and proceed further. But to award someone a prize, you need to know their exact score and confirm it is the highest! but the main flaw with the mode formula is that it is far from giving the highest scorer! ( Not even close with fluctuating data! ) So, atleast when calculating the mode, it is indeed better to not do anything than giving a formula that is far from being accurate! Regards, ( Thank you again! )
@@Yourmaths but how does the data being continuous make the mode any more predictable?? So I could convert any data into continuous data and suddenly the mode will be more predictable? ( You've earned a new subscriber today :) )
Then you can take any of them as modal class and solve and find mode, like if you have three same Max frequency then one by one, Take each and every Max frequency's corresponding class interval and solve then you may get 3 same mode sometimes and sometimes not. 🔴Case 1 :- If they, three are same then it's that mode, Done ! 🔴Case 2 :- But if they, three are different then that data has three modes, for simplicity you can take average of those three different modes to show single mode values for the given frequency table🙂 Sorry for being late, dear❤
Great ! I can't take the exact example but think if class size is different or class interval is different means h is different but you can see that it also depends on the frequency of the model class its previous frequency and its upcoming class frequency so maybe the frequency subtract and divide like that it can make the different modes same ( if there is two or more same frequency class is present ) As you don't know the frequency is what it can form the same mode as well. Hopefully you get it❤ Thank you ❤☺🙏
Respected Sir, Why we are choosing the class with highest frequency why not other classes Ex 20-40 f= 5 40-60 f= 6 60-80 f= 10 80-100 f= 8 In 60-80 class 60 may come 6 times 75 once and 77once ,78once 79 once. And in 80-100 class 80 may come 7 time and 99 once Then for sure 80 is mode If we put formula Some other will come Please tell why? I beg your pardon
Great ! I appreciate your thinking ☺❤ But it is continuous frequency distribution not discrete one. The observation or the data value is not fix, it's in the interval form of 20-40 or 60-80 like that. This is like approximation like from 20 to 40 ok so it may be anything in between this, like we have 10 houses in which there is 5 to 20 types of flower present.. It means each house can have any number of flower in between 5 to 20, you don't know !!! And we don't know the exact value as you have told 80 comes 7 times. As the data value or observation is in the form of interval and not a single value. Therefore we have to check the highest frequency's class interval. What if as you are saying happens then it's a discrete frequency distribution, not continuous ok. Hopefully you get it, please give me your feedback☺❤🙏
Why does mode lies in the intersection point of the adjacent frequencies?
Great !❤
But understand, this is a continuous frequency distribution it means the class interval is present, there is not any kind of fix value observation or data value.
Therefore you have to assume the mode, now try to think what thing is important to measure the mode If the data value is in interval and not fix or not a discrete value. So you must have to approximate and try to indicate mode with the help of previous and upcoming frequencies..
As we know that mode is basically higher frequency class but actually which value will be our Mode we can't find out in that class interval...
Therefore with the help of previous and upcoming frequencies we join that two lines because to fix the Mode value and as upcoming and previous frequencies fluctuate the mode exact value also fluctuate..
If you join the line and let's suppose the upcoming frequencies more than previous frequency of the modal class..
Then join the line you can clearly see that mode slightly shift towards upper limit of class interval..
So basically here we predict, that yes previous frequency is lower and upcoming frequency is higher than the previous so we only predict that Mode maybe have to lie there..
But finally yes it's not exact it's also a kind of approximation so that we can find the mode if we have continuous frequency distribution table.
For our simplicity we do this !
Hopefully you get it !
Please give me your feedback ☺❤🙏
why and how the mode is affected by the frequency of other class intervals ?
Basically, you can't calculate the exact mode value..
🔴THINK 🤔 :- if there is "class intervals" present, hence there is no any fix constant value of observation or constant data value...
So, now you know that mode is that observation or data value which repeats maximum Times so you have to see the maximum frequency class but in that there is a range of observation or there is a range of data value at that particular maximum frequency class..
Clearly in this class range you can't find which number will be the mode so we predict it without finding the actual mode as we can't know that okay so it's a kind of probability as we predict it by the adjacent frequency.
Now your question is why the frequency is affected by this adjacent frequency so listen...
As mode depends on the frequency as which frequency is more than, that will be the model class and from that model class I have to select the mode value and mode only depends on the frequency.. therefore we compare mode with the adjacent frequencies of different classes as there is nothing in the whole data set to compare the mode with other thing.😊
I will again say that we can only predict it, not it's 100% accurate.
Hopefully you get this😊
Please give me your feedback to this if you have any Query😊❤.
@@Yourmaths thanku for clearing my doubt😊
I want to know what we do for getting 100% sure result .
And as mode is the class with highest frequency, in a grouped data, for example,
there are two class intervals 20-30 and 30- 40
In 20-30 total frequency is 12 and frequency of 24 let's say is 10 in it
On other hand in 30-40 total frequency let's say is 19 and frequency for its components , i.e.,
32 is 7
33 is 6
35 is 6
Then the modal class on basis of class interval is 30-40 but in reality the frequency of 24 which lies in 20-30 is 10 which is maximum.
How will we deal with this condition.
Great !❤😊,,
But try to know when you fix the value of data value or when you fix the value of the observation as it is 24, 32, 33...
Then, this will become now discrete grouped data it's not Continious grouped data. As in the interval of 20-30, you have fixed the observation which is 24 and their frequency as well which is 10.
So any other observation in between 20 to 30 must have only two frequency left..
And the model class having maximum frequency you also divided it and you make it a discrete grouped data it's not Continious Grouped one..
Therefore, the model class condition not satisfy here, as its not, you know, discrete grouped data its basically continious grouped data..
🔴Continious Grouped data means observation lie in the interval form..
🔴discrete grouped data means observation is discrete it means there is a fix value of the observation.
Now in these two,
Why it's called grouped data because it's grouped in the tabular form and frequency for a particular observations is fix and it's also not repeat and this is grouped data.😊
Now, please give me your feedback if you have any Query😊❤...
@@Yourmaths thank you 😊
You cleared my doubts.
@bhavyaagarwal3662 your welcome Bhavya😊.
Please subscribe us❤
Everything was fine
But one thing actually
Why we connected the lines in the graph that formed the triangles and why only that lines why not others? Please answer today as my exam is tomorrow and I have to remember it
Thanks for all the derivations
Great !☺
But try to understand this is a continuous frequency distribution it means the class interval is present, there is not any kind of fix value observation or data value.
Therefore you have to assume the mode, now try to think what thing is important to measure the mode If the data value is in interval and not fix or not a discrete value. So you must have to approximate and try to indicate mode with the help of previous and upcoming frequencies..
As we know that mode is basically higher frequency class but actually which value will be our Mode we can't find out in that class interval...
Therefore with the help of previous and upcoming frequencies we join that two lines because to fix the Mode value and as upcoming and previous frequencies fluctuate the mode exact value also fluctuate..
If you join the line and let's suppose the upcoming frequencies more than previous frequency of the modal class..
Then join the line you can clearly see that mode slightly shift towards upper limit of class interval..
So basically here we predict, that yes previous frequency is lower and upcoming frequency is higher than the previous so we only predict that Mode maybe have to lie there..
But finally yes it's not exact it's also a kind of approximation so that we can find the mode if we have continuous frequency distribution table.
For our simplicity we do this !
Hopefully you get it !
Please give me your feedback ☺❤🙏
@@Yourmaths thanks
@@saurabhsuman7417 you are welcome, brother 😊 🙏
Why does the mode have to be in the modal class? For example, in the data given in the video, if 21 comes two times, 22 comes two times, but 13 comes 3 times, the actual mode is 13, which does not lie in the modal class.
Great☺,
I appreciate your thinking ❤,
But try to understand this is not a discrete frequency distribution table, it's a continuous frequency distribution table.
Means the data value observation or the term is is not a particular value its continuous therefore we will calculate the mid value of the class interval to predict the data value or to understand the data value in discrete form. But as you know that this is not a actual value of the class interval as it can be anything in between that class interval.
Maybe in between that class interval there are two different data value and in that case we can't even predict the mode of a continuous grouped data.
But in this we will try to make it simple by saying that in this class interval there may be a number which is maximum and that average number is mostly repeatedly come !
So basically, you basically try to calculate the mode that it must be here but no needed it's an assumed mode not real because the whole table is depending on the prediction.
So, the answer to this question is you can't predict exact mode.
According to your question as we have given the scenario at that time it's not a continuous frequency distribution table at that time it becomes discrete frequency distribution table at that time we can predict the exact mode as you have predicted that is 13 !
But here we are discussing about the continuous frequency distribution table where the mode is only predictable not exact !
Hopefully you get it,
please give me your feedback❤☺🙏
@@Yourmaths first of all, thank you so much for this video sir.
I understand that we assume the data to be continuous, and predictable. But the problem with this whole intersecting lines is that it assumes the frequency to be a perfect curve ( of sorts ) rising up and falling down, with the assumption that greater the value of 'x', the greater the frequency f0. it is like it assumes that the mode is being "attracted" by greater frequencies, which is clearly not true as only one mode can exist, and which again, may not lie in the modal class!
My question is - why go through such an elaborate process to give the incorrect answer for mode??
this formula is like - conducting a competition among people, to give the prize to the highest scorer, but you don't know their names, or their scores individually, you've just grouped them in "modal classes" and you have taken the three highest "modal classes" ( that arbitrarily happened to be grouped together ) and found the mean between them, and gave the prize to an arbitrary person ( who might not even have a descent score! ) and no prize to the highest scorers!
If we assume so much in a formula, then why does it even exist?
Again, I thank you for this video, I have my maths pre-board tomorrow.
Great❤,
You have asked such a great amazing question !
I'll try to answer !
It's a continuous frequency distribution table, so the mode is not accurate means mode is predictable mode !
And even we know something about continuous frequency distribution table's mode...
Therefore we can say we know something which is far better than doing nothing !
But think that if you eat 1 Roti today then next day 3 roti,
next day let's say 5 Roti
So in these three days you have eaten 5 + 3 + 1 roti, which is equals to 9 roti.
Per day how many Roti have you eaten ?
It's 9 / 3 which is three.
Means you can say that averagely you have eaten three Roti per day !
But you know that it's not correct if I will only tell you that you have eaten 3 Roti per day then do you think that it's a correct answer ?
Absolutely not because you have eaten one Roti one day 3 Roti next day and then five roti.
So similarly if there is continuous frequency distribution, we don't know the exact mode what we can predict the mode which is most likely to be the exact mode !
Lastly I want to say that, that you calculated the area of all uniform bodies and shapes like Volume of Cylinder, volume of Sphere, volume of cube, area of triangle, area of rectangle, you have calculated this many things in your maths studies but do you think that in our actual life these type of structure are present more ?
No,
Because this whole world is not uniform this is non-uniform irregular and those shapes area can never be calculated with these things, we need of function for that calculation which you will learn in the higher classes !
So like that mode is here even though continuous frequency distribution table's part..
Even though we are trying to explain the mode with the help of uniformness..
And not with the help of irregularities !
As Mode can be anything,
But as you know that here you can't even find the irregulaness..
Therefore the other option you have is to calculate the mode with the help of uniformness..
As you have done in the example of Roti !
That you have averagely eaten 3 Roti per day which is the uniformness.
But in actual you have eaten 1, 3 and then five roti and this is irregularness..
But your thinking is great !❤
Hopefully you get it, please give me your feedback ❤☺🙏
@@Yourmaths Thank you so much for your constructive feedback sir!
But in the Roti example, we only calculate the average or the mean, for which, relying on the "uniformness" is the most sensible thing to do, like in my example, if we want the average score of the contestants, then, we do not need to know their names and details.. and proceed further. But to award someone a prize, you need to know their exact score and confirm it is the highest! but the main flaw with the mode formula is that it is far from giving the highest scorer! ( Not even close with fluctuating data! ) So, atleast when calculating the mode, it is indeed better to not do anything than giving a formula that is far from being accurate!
Regards,
( Thank you again! )
@@Yourmaths but how does the data being continuous make the mode any more predictable?? So I could convert any data into continuous data and suddenly the mode will be more predictable?
( You've earned a new subscriber today :) )
I love this but we're we get the final answer please
Thank you, brother❤ !
But please ask the question again, i can't understand❤☺🙏
What to do if two or more classes have same max frequency
Then you can take any of them as modal class and solve and find mode, like if you have three same Max frequency then one by one, Take each and every Max frequency's corresponding class interval and solve then you may get 3 same mode sometimes and sometimes not.
🔴Case 1 :-
If they, three are same then it's that mode, Done !
🔴Case 2 :-
But if they, three are different then that data has three modes,
for simplicity you can take average of those three different modes to show single mode values for the given frequency table🙂
Sorry for being late, dear❤
How can you get the same mode if the class interval/class are different.Can you explain with small example?😊
Great ! I can't take the exact example but think if class size is different or class interval is different means h is different but you can see that it also depends on the frequency of the model class its previous frequency and its upcoming class frequency so maybe the frequency subtract and divide like that it can make the different modes same ( if there is two or more same frequency class is present )
As you don't know the frequency is what it can form the same mode as well.
Hopefully you get it❤
Thank you ❤☺🙏
Thank you too sir, you're a great a tutor.
Love u bro
Love you too, brother❤☺🙏
👍
Thank you dear❤🙏🏻
thank you sir
You are welcome, brother❤☺🙏
Respected Sir,
Why we are choosing the class with highest frequency why not other classes
Ex
20-40 f= 5
40-60 f= 6
60-80 f= 10
80-100 f= 8
In 60-80 class 60 may come 6 times 75 once and 77once ,78once 79 once.
And in 80-100 class 80 may come 7 time and 99 once
Then for sure 80 is mode
If we put formula Some other will come
Please tell why? I beg your pardon
Great ! I appreciate your thinking ☺❤
But it is continuous frequency distribution not discrete one.
The observation or the data value is not fix, it's in the interval form of 20-40 or 60-80 like that. This is like approximation like from 20 to 40 ok so it may be anything in between this, like we have 10 houses in which there is 5 to 20 types of flower present..
It means each house can have any number of flower in between 5 to 20, you don't know !!!
And we don't know the exact value as you have told 80 comes 7 times.
As the data value or observation is in the form of interval and not a single value.
Therefore we have to check the highest frequency's class interval.
What if as you are saying happens then it's a discrete frequency distribution, not continuous ok.
Hopefully you get it,
please give me your feedback☺❤🙏
@@Yourmaths Very helpful thanks
@@saurabhsuman7417 you are always welcome brother 🙏 ❤️