Best Answer

A pedantic answer would be nothing!

The number of heads from repeated tosses of a fair coin will give an

approximately normal distribution. But if the distribution is truly normal then

there is an infinitesimally small, but non-zero, probability of getting a negative

number of heads.

Similarly, the heights of adult males, for example, is usually assumed to be

normally distributed. Again the distribution should have a very, very tiny - but

non-zero - probability of an adult male having a negative height! Somehow I

don't think so!

Hence the mathematically accurate answer is nothing.

The coin-tossing experiment will generate a very good approximation of a Normal

distribution when the coin is fair, as will men's heights. An even better

approximation will be obtained if you take lots of random samples of 10 men and

observe the sums or means of their heights. Women's heights, their masses,

IQs (whatever that is supposed to measure), will all be normal.

The question that has not been adequately researched is whether it is the IQ

of all people that is normal, or of only normal people.

Q: What can you measure that will give you a graph of normal distribution?

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A cartesian graph is a graph in which y is some function of x. This is the 'normal' type in which you can give an x and y coordinate. Other types include polar in which modulus is a function of argument, but there are loads of varieties and forms.

Tables of the cumulative probability distribution of the standard normal distribution (mean = 0, variance = 1) are readily available. Almost all textbooks on statistics will contain one and there are several sources on the net. For each value of z, the table gives Î¦(z) = prob(Z < z). The tables usually gives value of z in steps of 0.01 for z â‰¥ 0. For a particular value of z, the height of the probability density function is approximately 100*[Î¦(z+0.01) - Î¦(z)]. As mentioned above, the tables give figures for z â‰¥ 0. For z < 0 you simply use the symmetry of the normal distribution.

If you have an object that is accelerating, then a position vs. time graph will give you a parabola which is pretty but is very hard to measure anything on - especially hard to measure the acceleration (or the curve of the line). If however, you graph position vs. time squared, you get a nice straight line (if you have constant acceleration) and therefore, you can measure the slope and get the acceleration. Remember: x = 1/2at2 so if you graph x vs. t2 then the slope = 1/2 a or a = 2*slope No matter what you are measuring, you always want to graph a straight line. hope that helps

If I can't see the graph then how will I know the answer?

Slope of the graph will give you speed.

Related questions

example from your business or industry that seems to reflect the normal distribution

Yes, and the justification comes from the Central Limit Theorem.

Normal distribution is not "better." It is, perhaps, simpler to work with. All introductory text books and courses on statistics cover it in great detail, its properties are well-known, and there are lots of tables you can refer to. But if the real-world situation you are trying to model does not resemble a normal distribution, then it is very bad to try to use the properties of a normal distribution or to try to force a normal distribution on your data. Doing so will give you inaccurate answers.

In math, skewness is a measure of the asymmetry of a probability distribution. A distribution is considered right-skewed if the tail on the right side is longer or fatter than the tail on the left side, and vice versa for left-skewed distributions. Skewness can give insight into the shape of a dataset and how it deviates from a symmetrical distribution like the normal distribution.

A cartesian graph is a graph in which y is some function of x. This is the 'normal' type in which you can give an x and y coordinate. Other types include polar in which modulus is a function of argument, but there are loads of varieties and forms.

Tables of the cumulative probability distribution of the standard normal distribution (mean = 0, variance = 1) are readily available. Almost all textbooks on statistics will contain one and there are several sources on the net. For each value of z, the table gives Î¦(z) = prob(Z < z). The tables usually gives value of z in steps of 0.01 for z â‰¥ 0. For a particular value of z, the height of the probability density function is approximately 100*[Î¦(z+0.01) - Î¦(z)]. As mentioned above, the tables give figures for z â‰¥ 0. For z < 0 you simply use the symmetry of the normal distribution.

Yes.

If you have an object that is accelerating, then a position vs. time graph will give you a parabola which is pretty but is very hard to measure anything on - especially hard to measure the acceleration (or the curve of the line). If however, you graph position vs. time squared, you get a nice straight line (if you have constant acceleration) and therefore, you can measure the slope and get the acceleration. Remember: x = 1/2at2 so if you graph x vs. t2 then the slope = 1/2 a or a = 2*slope No matter what you are measuring, you always want to graph a straight line. hope that helps

1.it is bell shaped.2.m.d=0.7979 of s.d 3.total area under the normal curve is equal to 1.

If I can't see the graph then how will I know the answer?

The cumulative probability up to the mean plus 1 standard deviation for a Normal distribution - not any distribution - is 84%. The reference is any table (or on-line version) of z-scores for the standard normal distribution.

I will give first the non-mathematical definition as given by Triola in Elementary Statistics: A random variable is a variable typicaly represented by x that has a a single numerical value, determined by chance for each outcome of a procedure. A probability distribution is a graph, table or formula that gives the probabability for each value of the random variable. A mathematical definition given by DeGroot in "Probability and Statistics" A real valued function that is defined in space S is called a random variable. For each random variable X and each set A of real numbers, we could calculate the probabilities. The collection of all of these probabilities is the distribution of X. Triola gets accross the idea of a collection as a table, graph or formula. Further to the definition is the types of distributions- discrete or continuous. Some well know distribution are the normal distribution, exponential, binomial, uniform, triangular and Poisson.