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First, you need to determine the mean. The mean of a list of numbers is the sum of those numbers divided by the quantity of items in the list (read: add all the numbers up and divide by how many there are). Then, subtract the mean from every number to get the list of deviations. Create a list of these numbers. It's OK to get negative numbers here. Next, square the resulting list of numbers (read: multiply them with themselves). Add up all of the resulting squares to get their total sum. Divide your result by one less than the number of items in the list. To get the standard deviation, just take the square root of the resulting number I know this sounds confusing, but just check out this example: your list of numbers: 1, 3, 4, 6, 9, 19 mean: (1+3+4+6+9+19) / 6 = 42 / 6 = 7 list of deviations: -6, -4, -3, -1, 2, 12 squares of deviations: 36, 16, 9, 1, 4, 144 sum of deviations: 36+16+9+1+4+144 = 210 divided by one less than the number of items in the list: 210 / 5 = 42 square root of this number: square root (42) = about 6.48
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When finding standard deviation you are typically looking at what kind of distribution?
Usually a normal distribution.
What is the essence of finding the standard deviation of your data?
I am not entirely sure I understand correctly what you mean by "essence". However, the idea of finding the standard deviation is to determine, as a general tendency, whether most data points are close to the average, or whether there is a large spread in the data. The standard deviation means, more or less, "How far is the typical data point from the average?"
What is a standard deviation?
Standard deviation is a statistical tool used to determine how tight or spread out your data is. In effect, this is quantitatively calculating your precision, the reproducibility of your data points. Here's how you find it: 1). Take the average of all the data points in your set. 2). Find the deviation of each point by finding the difference between each data point and the mean. 3). Add the squares of each deviation together. 4). Divide by one less than the number of data points. If there are 20 data points, divide by 19. 5). Take the square root of this value. 6). Done.
What is the T-Test formula?
t= absolute value of ( sample 1 - sample two) THEN DIVIDED by the (standard error of sample one - standard error of sample 2) standard error = the standard deviation divided by (square root of the pop. sample number) You have to work in steps to get all info 1. mean ( REPRESENTED BY 'Xbar') 2. sum of squares ('SS') 3. Sample variance ('s^2') 4. standard deviation ('s') 5. standard error ('s subscript x') 6. pooled measure ('s^2p') 7. Standard error between means (s subscript mean one-mean two) 8. t test In other word finding the mean and having ht esample info leads you to each formula with the end formular being the t-test have fun, its easy but dumb
What is a z score and what its used for?
The Normal probability distribution is defined by two parameters: its mean and standard deviation (sd) and, between them, these two can define infinitely many different Normal distributions. The Normal distribution is very common but there is no simple way to use it to calculate probabilities. However, the probabilities for the Standard Normal distribution (mean = 0, sd = 1) have been calculated numerically and are tabulated for quick reference. The z-score is a linear transformation of a Normal variable and it allows any Normal distribution to be converted to the Standard Normal. Finding the relevant probabilities is then a simple task.
