Statistic approximation

The table below is given the measures of cats (of weight):

mass (grams) #no. of cats \text{\#no. of cats}
1500 m < 2000 1500 ≤ m < 2000 9 9
2000 m < 2500 2000 ≤ m < 2500 22 22
2500 m < 3000 2500 ≤ m < 3000 37 37
3000 m < 3500 3000 ≤ m < 3500 20 20
3500 m < 4000 3500 ≤ m < 4000 12 12

You have the information that the largest weighting cat is 3745 grams 3745 \text{ grams}

Find me:

  • No. of cats < 2200 grams < 2200 \text{ grams} (below 2200 grams)
  • No. of cats > 3600 grams > 3600 \text{ grams} (upper 3600 grams)

Note: The result for each must be rounded down and not approximated.

Assume that the distribution of cats is unskewed between intervals.

The input is the results added.


The answer is 24.

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1 solution

Nikolas Кraj
Nov 26, 2020

SOLUTION: The number of cats below 2200 grams is:

#Cats < 2200 = #Cats 1500 m < 2000 + #Cats P a r t i a l 2000 m < 2500 \text{\#Cats}_{<2200} = \text{\#Cats}_{1500 ≤ m < 2000} + \text{\#Cats}_{Partial 2000 ≤ m < 2500}

In the assumption that "the distribution is unskewed between intervals", we can find the no of cats (inside this partial interval) that each space occupies and we will multiply this by the space of: 2000 2200 s p a c e = 200 s p a c e 2000 - 2200 space = 200 space :

#Cats P a r t i a l 2000 m < 2500 = 22 cats 500 space × 200 space \text{\#Cats}_{Partial 2000 ≤ m < 2500} = \frac{22 \text{ cats}}{500 \text{ space}} \times 200 \text{ space}

#Cats P a r t i a l 2000 m < 2500 = 8.8 cats \text{\#Cats}_{Partial 2000 ≤ m < 2500} = 8.8 \text{ cats}

We now can solve for #Cats < 2200 = 8.8 cats + 9 cats = 17 cats \text{\#Cats}_{<2200} = 8.8 \text{ cats} + 9 \text{ cats} = \boxed{17 \text{ cats}} (we rounded down)


The same goes with the other but since we have that the largest weighting cat is 3745 grams 3745 \text{ grams} , we must change the interval from 3500 m < 4000 3500 ≤ m < 4000 to 3500 m < 3745 3500 ≤ m < 3745 (and these 2 intervals have the same cats in this case of ours).

#Cats > 3600 g r = #Cats P a r t i a l 3500 m < 3745 \text{\#Cats}_{>3600 gr} = \text{\#Cats}_{Partial 3500 ≤ m < 3745}

In the assumption that "the distribution is unskewed between intervals", we can find the no of cats (inside the partial interval) that each space occupies and we will multiply this by the space of 3745 3600 s p a c e = 145 s p a c e 3745 - 3600 space = 145 space :

#Cats P a r t i a l 3500 m < 3745 = 12 cats ( 3745 3500 ) space × 145 space \text{\#Cats}_{Partial 3500 ≤ m < 3745} = \frac{12 \text{ cats}}{(3745-3500) \text{ space}} \times 145 \text{ space}

#Cats P a r t i a l 3500 m < 3745 = 7 cats \text{\#Cats}_{Partial 3500 ≤ m < 3745} = \boxed{7 \text{ cats}} (we rounded down)

17 + 7 = 24 17+7 = \boxed{24}

@Brilliant Mathematics please fix my answer typo.

Nikolas Кraj - 6 months, 2 weeks ago

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Thanks. I've updated the answer to 24 .

Brilliant Mathematics Staff - 6 months, 2 weeks ago

I did this the same way. It's possible it would make more sense to use a "best fit" distribution using the information about the other cats, but I don't know if that would make a difference.

Chris Lewis - 6 months, 1 week ago

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That would be redundant; not needed since the enumerator (y) of the (x)cats/(y)space will cancel out with the space amount which we will multiply with (this one is (y) too).

Nikolas Кraj - 6 months, 1 week ago

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That's not quite what I meant. The data in the table show cats are more likely to be closer to the mean weight overall. I wouldn't say it looks like a normal distribution, but it's not uniform; so it may not be justified to assume that the distribution within the weight categories is uniform.

Chris Lewis - 6 months, 1 week ago

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@Chris Lewis I get it now, you mean the data about all cats is skewed? Well, yea that would make more sense.

Nikolas Кraj - 6 months, 1 week ago

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