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Start by converting this to a standard normal distribution. The lower quartile is where 25% of the data lies below, so you need to read off where 25% of the data lies below from a standard inverse normal table. Same for the upper quartile where 75% of the data lies below. Once you have the Z values, convert back to your given distribution.
You need to generate 200 real numbers whose mean=100 and sd=10...for example use the following data:
{86.7242,103.095,113.867,100.546,100.32,91.4765,10 2.592,104.713,100.67,113.748,87.5385,92.1847,93.68 7,105.191,109.376,117.19,107.898,97.2232,101.785,8 5.8895,104.347,108.525,89.1039,109.471,92.2035,88. 6707,90.0258,122.608,87.0634,119.748,100.419,82.91 12,94.2178,98.6578,110.744,100.762,93.4528,96.5865 ,103.162,111.058,103.891,114.719,102.908,86.7591,1 10.698,110.139,103.647,95.1995,108.781,105.425,110 .117,99.5495,112.593,95.5003,97.7986,87.7673,99.32 2,96.8874,98.545,107.215,98.2278,80.842,119.057,10 2.44,108.739,85.7724,100.906,94.714,100.761,96.884 2,96.6799,102.845,94.5768,103.694,131.178,111.151, 97.8677,81.6983,80.9117,94.5997,92.3242,102.16,99. 0763,115.843,80.4044,102.292,95.0226,100.146,113.2 42,84.5126,96.8718,91.1545,111.939,94.154,114.719, 96.9888,107.176,114.9,95.7682,110.252,110.461,103. 651,102.557,94.5806,88.8081,90.659,114.598,101.497 ,102.034,106.38,112.795,107.006,91.391,118.129,117 .313,97.4418,78.5687,82.7369,93.2231,119.27,87.115 2,74.5946,90.9401,108.254,91.4419,121.539,99.4256, 83.5403,103.917,109.746,107.337,94.9554,102.363,10 9.493,95.9816,101.473,89.2975,88.6838,121.588,107. 681,99.3644,107.338,85.3285,122.726,87.0816,119.27 1,101.825,83.9203,79.0244,93.4866,105.461,120.249, 94.3469,92.2688,107.272,121.888,105.128,98.2146,10 3.673,84.7568,86.7944,102.236,82.763,100.965,96.07 66,111.454,113.189,111.155,90.9439,99.1924,100.573 ,105.792,93.7059,104.366,100.996,103.198,87.7051,1 11.177,95.2342,107.831,93.4341,108.662,115.518,96. 3542,89.2625,86.7332,97.8433,105.829,85.4472,88.91 34,101.658,99.5822,91.6179,91.3036,108.929,105.385 ,91.1276,98.8022,112.458,90.8502}
No. You aren't looking for an INTERVAL for a lower or upper quartile, you are looking for a VALUE.
I suggest you read this table, and recall that the normal distribution is symmetric about the mean (which is 0 in the standard distribution).
0.75, it is 0.6745.
so for 0.25, it should be: 1-0.6745 = 0.3255
Then, reversing back, the values are:
for lower: is it: 93.255
for upper: is it:106.745
Is this correct???
Now, I just found Q1, Q3... how can i find minimum, median and maximum??
Not quite, your VALUE for 25% on the standard normal distribution is the same distance away from the mean as the 75% value, just in the other direction. So the value is -0.6745, but you get the right value for the lower quartile of your set of data.
Now your median should be obvious, it's where 50% of the values lie below. What value is that the same as in the normal distribution?
The maximum value should be obvious, it's where 100% of the values lie below. Can't you read that (or a very close value to it) from your table?
From there you should be able to get the minimum value.
Please be careful with how you word what you are trying to say. If you are trying to say "for the maximum value, we need to look for the value very close to 1 on the inverse standard normal tables", then yes. And then convert that value back.
As for the median, yes, its standard value is 0, which will give 100 when you convert back (there's no need to convert to the standard distribution in this case though). It is well known that for a normal distribution, the mean and median are equal.