Yes, if you mean normal with a mean other than 0 and/or standard error other than 1.
If m is the mean and s the standard error, then transform the original data, y, using:
z = (y - m)/s
z will have the N(0,1) distribution!!!!!!!
A distribution table would be primarily used in the field of statistics and probability. Collecting and interpreting data is much easier when compiled in this format.
The Normal distribution is, by definition, symmetric. There is no other kind of Normal distribution, so the adjective is not used.
Normal distribution is a perfectly symmetrical bell-shaped normal distribution. The bell curve is used to find the median, mean and mode of a function.
If the underlying distribution of the product is normally distributed then (and only then) the normal distribution can be used to identify specimens that are outside the acceptable range.
The central limit theorem basically states that for any distribution, the distribution of the sample means approaches a normal distribution as the sample size gets larger and larger. This allows us to use the normal distribution as an approximation to binomial, as long as the number of trials times the probability of success is greater than or equal to 5 and if you use the normal distribution as an approximation, you apply the continuity correction factor.
If there is no appropriate Table of Organization and Equipment (TOE), the authorization document that prescribes the resources for a unit to perform a specific mission is called a Table of Distribution and Allowances (TDA). The TDA outlines the personnel and equipment required for the unit to carry out its mission. It is used when there is no specific TOE that fits the unit's needs.
A distribution table would be primarily used in the field of statistics and probability. Collecting and interpreting data is much easier when compiled in this format.
The Normal distribution is, by definition, symmetric. There is no other kind of Normal distribution, so the adjective is not used.
The z-score table is the cumulative distribution for the Standard Normal Distribution. In real life very many random variables can be modelled, at least approximately, by the Normal (or Gaussian) distribution. It will have its own mean and variance but the Z transform converts it into a standard Normal distribution (mean = 0, variance = 1). The Z-distribution is then used to make statistical inferences about the data. However, there is no simple analytical method to calculate the values of the distribution function. So, it has been done and tabulated for easy reference.
Frequency Distribution Table
You can use a normal distribution to approximate a binomial distribution if conditions are met such as n*p and n*q is > or = to 5 & n >30.
Normal distribution is a perfectly symmetrical bell-shaped normal distribution. The bell curve is used to find the median, mean and mode of a function.
If the paired differences are normal in a test of mean differences, then the distribution used for testing is the
we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.
If the underlying distribution of the product is normally distributed then (and only then) the normal distribution can be used to identify specimens that are outside the acceptable range.
It may not be better, but there is a lot of information on the normal distribution. It is one of the most widely used in statistics.
It is probably the most widely used distribution in statistics. In addition, a lot of information exists on this distribution.