What I did was add up the credit points, 6+6+4+4 = 20, then multiply each grade by its share of the credit, so 65 x0.2 = 13, 81x0.2 = 16.2, 78x0.3 = 23.4, 76x0.3 = 22.8, then add up those products = 75.4. I'm not sure if this video helped me do that calculation, but it may have.
I got the same answer (75.4) but through a different approach (your's was more mathematically efficient by using 'share of credit' with a total value of 1 or 100%). First I took the weights 4. 4. 6. 6 and combined them into two categories of weight giving me weights of 8 and 12 (4+4 and 6+6). I then took the values in each category and found the mean of those and multiplied them by their respective weight as in: 12(78 & 76) + 8(65 & 81) ---> 12(78 + 76/2) + 8(65+81/2) = 12(154/2) + 8(146/2) = 12(17) + 8(73) = 924 + 584 = 1508. So then I have this crazy value of 1508 but then just divide that by the total weighted points (12 + 8 = 20) to get: 1508 / 20 = 75.4. Cool.
watch his example of weighted average from earlier in the video. you dont have to convert each of the credit points to each of them divided by the total credit points. if you do (65*4+81*4+78*6+76.6*6) / 20, it works out to be the same with less steps
So 1) you add all the credits 4+4+6+6=20 and gave each credit weighted contribution in percentage standpoint so 20%, 20%, 30%, 30%, after I multiplied them to scores, (65*0.2)+(81*0.2)+(78*0.3)+(76*0.3)=75.4, Weighted mean is 75.4
Год назад+7
75,4. Having Credit Points , Price or whatever weight can be reinterpreted as frequency repetitions (remember that a weighted mean is a regular mean where all its weights are 1) . For example , a Mark of 65 of credit 4 is the same as saying getting a Mark 65 of 1 credit 4 times. Another example , 9 Apples priced on 3 USD can be interpreted as having 3 Apples priced on 1 USD repeated 3 times. So in this case we have: (65*4+81*4+78*6+76*6)/20 Or (65+65+65+65 +81+81+81+81+78+78+78+78+78+78+76+76+76+76+76+76)/(1+1+1+1 …. +1)
According to your course, the answer is : (4x65 + 4x81 + 6x78 + 6x76) /(4 + 4 + 6 + 6) ===> 1 508/20 But for me (4x65 + 4x81 + 6x78 + 6x76) / 4 makes more sense We have four subjects weighted differently
For the last question. No need to complicate the problem. Just Multiply Mark*Credit Points = Total and divide the total by 20. Which is total credit points. In this case Mark*Credit points sum of all values and Credit Points total number of observations.
I have gone through all the comments and finally understood why everybody is writing 75.4 as an answer and its calculation but I still don't know the reason behind assumption of considering credit points as frequency for marks. Can somebody explain this to me ? What is the meaning of credit points here actually ?
@@cryptotv3013 becase is a "weight" mean this you must to multiply the "weight" for the number in this case the score for the number, so you have (65*4)+(81*4)+7(78*6)+(76*6) all divide by the sum of the wight (4+4+6+6)
I got 75, 5. I see 4 is the more popular answer, so I am sure I did something wrong, but for the weighted mean I did 2(4) + 2(6) over 4, then 8 +12 over 4, then 20/4 or 5.
You could explain by better example,like the time your grade is 75%by your finall(80)exam&25%by your quiz(90) {(80*75)+(90*25)}/{(75+25)}= And your final grade is=82.5
yoop, from what I see in comments thinking about weights as frequency messed up many peoples understanding, IMO it's better to think about it as 'how important for outcome is particular value?' which Your example shows perfectly.
I have a question plz. This really confused me I have data of plant water uptake by day and daily temperature within one month. I want to get an idea about the « average » temperature per month. Should I calculate the arithmetic mean though I have no idea if my data follows a normal distribution. I found an article using Arithmetic mean in non-normalized data and I got super confused as I thought we only calculate the mean if we have Gaussian distribution
@@tassoskat8623 the numerator is the way it is because of the way that numpy does vector/matrix multiplication. For the denominator, not sure why they used a dot product. Just needed to call sum on the credit vector and call it good.
Even when you consider summation of marks / n the answer will be 300/4 =75 , frequency will only be considered in weighted mean that you performed in your later answer which brings to 75.4
Challenge question solution in numpy :
import numpy as np
marks = np.array([65,81,78,76])
credits = np.array([4,4,6,6])
average = np.sum(marks*credits) / np.sum(credits)
print(average) # 75.4
What I did was add up the credit points, 6+6+4+4 = 20, then multiply each grade by its share of the credit, so 65 x0.2 = 13, 81x0.2 = 16.2, 78x0.3 = 23.4, 76x0.3 = 22.8, then add up those products = 75.4. I'm not sure if this video helped me do that calculation, but it may have.
I got the same answer (75.4) but through a different approach (your's was more mathematically efficient by using 'share of credit' with a total value of 1 or 100%). First I took the weights 4. 4. 6. 6 and combined them into two categories of weight giving me weights of 8 and 12 (4+4 and 6+6). I then took the values in each category and found the mean of those and multiplied them by their respective weight as in: 12(78 & 76) + 8(65 & 81) ---> 12(78 + 76/2) + 8(65+81/2) = 12(154/2) + 8(146/2) = 12(17) + 8(73) = 924 + 584 = 1508. So then I have this crazy value of 1508 but then just divide that by the total weighted points (12 + 8 = 20) to get: 1508 / 20 = 75.4. Cool.
Sorry for the silly question.. but how did you come up with "share of the credit?"
You can use simple formula using credit points as frequency, you will get same answer.
@@ankurkulshrestha1308 genius! 😊
watch his example of weighted average from earlier in the video. you dont have to convert each of the credit points to each of them divided by the total credit points. if you do (65*4+81*4+78*6+76.6*6) / 20, it works out to be the same with less steps
So 1) you add all the credits 4+4+6+6=20 and gave each credit weighted contribution in percentage standpoint so 20%, 20%, 30%, 30%, after I multiplied them to scores, (65*0.2)+(81*0.2)+(78*0.3)+(76*0.3)=75.4, Weighted mean is 75.4
75,4. Having Credit Points , Price or whatever weight can be reinterpreted as frequency repetitions (remember that a weighted mean is a regular mean where all its weights are 1) . For example , a Mark of 65 of credit 4 is the same as saying getting a Mark 65 of 1 credit 4 times. Another example , 9 Apples priced on 3 USD can be interpreted as having 3 Apples priced on 1 USD repeated 3 times.
So in this case we have:
(65*4+81*4+78*6+76*6)/20
Or
(65+65+65+65 +81+81+81+81+78+78+78+78+78+78+76+76+76+76+76+76)/(1+1+1+1 …. +1)
(65*4+81*4+78*6+76*6)/(4+4+6+6) = 75.4
Mark''s weighted average score is 75.4 with one more decimal place as convention.
feels like 3blue1brown for stat
Man it feels awesome to find these videos
According to your course, the answer is : (4x65 + 4x81 + 6x78 + 6x76) /(4 + 4 + 6 + 6) ===> 1 508/20
But for me (4x65 + 4x81 + 6x78 + 6x76) / 4 makes more sense
We have four subjects weighted differently
He is not asking for the mean, he asked for the weighted average marks
@@gehadhisham2539 What is the difference?
@Aldrina Lawrence I think it is better to think of credit as the importance of the given marks to the final result than as a frequency.
looking forward to your video series on descriptive statistics. you should do a series on probability.
Thinking graphical measures will come after descriptive stats but lets see how I go! Good idea, Sweet Chyld o Mine.
@@zedstatistics lol thanks
For the last question. No need to complicate the problem. Just Multiply Mark*Credit Points = Total and divide the total by 20. Which is total credit points. In this case Mark*Credit points sum of all values and Credit Points total number of observations.
This video was very helpful. I thought I knew what mean was, but your explanation intuitive and helped me better understand weighted mean. Thank you.
I have gone through all the comments and finally understood why everybody is writing 75.4 as an answer and its calculation but I still don't know the reason behind assumption of considering credit points as frequency for marks. Can somebody explain this to me ? What is the meaning of credit points here actually ?
I would love to know too
Just stumbled upon your videos, I love your simplified explanations! Easy to understand! Is the challenge answer 75.4?
Yeah
75.4
can you explain
@@shakhriyorbegmatjonov6711 Yes, you are probably dividing by the wrong number. Divide by the sum of all the weights (6+6+4+4=20)
YEP!
@@zedstatistics which means 15 is the right answer or 75.4, i still have confusion, please explain zed
@@cryptotv3013 becase is a "weight" mean this you must to multiply the "weight" for the number in this case the score for the number, so you have (65*4)+(81*4)+7(78*6)+(76*6) all divide by the sum of the wight (4+4+6+6)
the weighted mean makes so much more sense now, i was totally lost and just plugging stuff in but i think i understand now
Gonna binge watch this playlist loll
I got 75, 5. I see 4 is the more popular answer, so I am sure I did something wrong, but for the weighted mean I did 2(4) + 2(6) over 4, then 8 +12 over 4, then 20/4 or 5.
Can you please explain how the mean formula came about.........love the content
I think each subject has a frequency of 1. They haven't been repeated. Therefore the mean becomes (65+81+78+76)/4=75
75.4 is the weighted average
Georgia's weighted average is 75.4.
You could explain by better example,like the time your grade is 75%by your finall(80)exam&25%by your quiz(90)
{(80*75)+(90*25)}/{(75+25)}=
And your final grade is=82.5
yoop, from what I see in comments thinking about weights as frequency messed up many peoples understanding, IMO it's better to think about it as 'how important for outcome is particular value?' which Your example shows perfectly.
I have a question plz. This really confused me
I have data of plant water uptake by day and daily temperature within one month. I want to get an idea about the « average » temperature per month. Should I calculate the arithmetic mean though I have no idea if my data follows a normal distribution. I found an article using Arithmetic mean in non-normalized data and I got super confused as I thought we only calculate the mean if we have Gaussian distribution
1508/20=75.4
hey bro can i know please how you add them
Georgia's weighted average mark: 75.40
Thank you for the great video! I am wondering if there is a mean between how often two or more numbers have been selected when the dataset is skewed.
great video. challenge question got 75.4 weighted mark
Great video. I have a terminology question: What is the difference between a (arithmetic) mean and an average?
hi Zed，would you please explain the meaning of boutiques measures for me?
what is the possible explanation if the mean value is below 2?
life saver!
thanks a lot, very precious lesson
great work!
thank you so much
Good presentation.
excellent
This video is good, I mean it!
U dont know how much you saved meee
mark = np.array([[65,81,78,76]])
credit = np.array([[4,4,6,6]])
np.dot(mark,credit.T)/np.dot(credit,np.ones([4,1]))
array([[75.4]])
That seems like an overkill 😆
@@tassoskat8623 the numerator is the way it is because of the way that numpy does vector/matrix multiplication. For the denominator, not sure why they used a dot product. Just needed to call sum on the credit vector and call it good.
Ans. 75.4
Weighted average= 65+81+78+76/4=75
Because frequency is 1 of all data
Weighted mean 75.4
Where I can find the correct answer
@TheNachosvastav got it below :)
Answer : 75.4
The answer is 75.4.
75
Love the music! Where is it from?
Glad you do! Me too! He's a great Australian musician called Purdy. Check the video description for links.
@@zedstatistics definitely will, thank you!
My answer was 75.4
I got 75.4
I'm getting 15, and I don't know why. I'm adding all the numbers at the top, all the numbers at the bottom, and dividing them by each other. 300/20=15
Nevermind, gotta multiply the scores by their frequency and sum the results; then, sum the frequencies; and then, divide the results. Looks like 75.4
Even when you consider summation of marks / n the answer will be 300/4 =75 , frequency will only be considered in weighted mean that you performed in your later answer which brings to 75.4
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