[{"columns":["Exhibition_ID","Year","Theme","Artist_ID","Ticket_Price"],"name":"exhibition","type":"grid"}]

Show maximum ticket prices for exhibitions for all years before 2009?

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I am looking for a way to calculate how similar each number is compared to every other number in a set. Here's an example:
`[61, 62, 59, 60, 63, 62, 58]` would have a high "similarity score", where as `[12, 96, 71, 66, 108, 23, 5, 59]` would have a low "similarity score".

Is there a term for this, and how would I calculate it?

{"text":"Yes, the term you may be looking for is \"variance\" or \"standard deviation.\" Variance and standard deviation are statistical measures that indicate the dispersion or spread of a set of numbers. A smaller variance or standard deviation implies that the numbers are closer together, whereas a larger one implies the numbers are more spread out.\n\nIn your case, you can use standard deviation as a \"similarity score.\" Lower standard deviation means higher similarity, and higher standard deviation means lower similarity.\n\nHere's how to calculate the standard deviation:\n\n1. Calculate the mean (average) of the numbers in the dataset.\n2. Subtract the mean from each number and square the result.\n3. Calculate the mean of the squared differences.\n4. Take the square root of the mean of the squared differences.\n\nUsing the two arrays you gave. Here's a step-by-step example of computing the standard deviation:\n\nLet's start with the first array `[61, 62, 59, 60, 63, 62, 58]`:\n\n1. Calculate the mean (average) of the numbers in the dataset.\n(61 + 62 + 59 + 60 + 63 + 62 + 58) / 7 = 425 / 7 = 60.714\n\n2. Subtract the mean from each number and square the result.\n(61 - 60.714)^2 = 0.085\n(62 - 60.714)^2 = 1.647\n(59 - 60.714)^2 = 2.947\n(60 - 60.714)^2 = 0.510\n(63 - 60.714)^2 = 5.247\n(62 - 60.714)^2 = 1.647\n(58 - 60.714)^2 = 7.347\n\n3. Calculate the mean of the squared differences.\n(0.085 + 1.647 + 2.947 + 0.510 + 5.247 + 1.647 + 7.347) / 7 = 18.48 / 7 = 2.640\n\n4. Take the square root of the mean of the squared differences.\nsqrt(2.640) = 1.625\n\nThe standard deviation for the first array is approximately 1.625.\n\nNow for the second array `[12, 96, 71, 66, 108, 23, 5, 59]`:\n\n1. Calculate the mean (average) of the numbers in the dataset.\n(12 + 96 + 71 + 66 + 108 + 23 + 5 + 59) / 8 = 440 / 8 = 55\n\n2. Subtract the mean from each number and square the result.\n(12 - 55)^2 = 1849\n(96 - 55)^2 = 1681\n(71 - 55)^2 = 256\n(66 - 55)^2 = 121\n(108 - 55)^2 = 2809\n(23 - 55)^2 = 1024\n(5 - 55)^2 = 2500\n(59 - 55)^2 = 16\n\n3. Calculate the mean of the squared differences.\n(1849 + 1681 + 256 + 121 + 2809 + 1024 + 2500 + 16) / 8 = 10256 / 8 = 1282\n\n4. Take the square root of the mean of the squared differences.\nsqrt(1282) = 35.818\n\nThe standard deviation for the second array is approximately 35.818.\n\nSo, the standard deviation (similarity score) for the first array is 1.625, and for the second array, it is 35.818. A lower score indicates higher similarity.","dsl":{}}

Is standart deviation the thing that shows up in graph as the shaded part of projections to show uncertainty?

{"text":"Yes, that's correct! The shaded part of projections is often used to represent the standard deviation, which is a measure of the amount of variation or dispersion of a set of values. The standard deviation provides a way to quantify the spread of the data around the mean, and it is often used to represent the uncertainty or error in a set of measurements or predictions.\n\nIn a graph, the standard deviation is typically shown as the shaded region around a line, such as a mean or median, that represents the expected value of a set of data. The size of the shaded region is proportional to the standard deviation, with larger shaded regions indicating a greater degree of uncertainty. By convention, the shaded region typically encompasses 68% of the data, which means that there is a 68% probability that a value drawn from the data set will fall within the shaded region.","dsl":{}}