It means that the data are distributed according to a probability distribution function known as the normal distribution. This site is useless for showing most mathematical functions but you can Google "normal distribution" to get more details.
The mean and standard deviation. If the data really are normally distributed, all other statistics are redundant.
The means of repeated samples from any population.
no
The z score is (1650-1500)/150 = 150/150 = 1
The cause of skewed data distributions are extreme values, also know as outliers. For example imagine taking the weights of people you see on the street. If you have 9 cheerleaders' weights and then the weight of a sumo wrestler mixed into the averages this skews the data. This makes the mean much higher because of the one extreme value. Instead of the data being distributed normally, it is distributed with a positive skew. If there is a really small extreme value instead of a really large one, then the data has a negative skew. This could be the heights of people on the street, but one of them would be a midget. The mean is made lower by that one extreme value. Perhaps, little person is a more politically correct term in our day.
The mean and standard deviation. If the data really are normally distributed, all other statistics are redundant.
The easiest way to tell if data is normally distributed is to plot the data.line plot apex
The form of this question incorportates a false premise. The premise is that the data are normally distributed. Actually, is the sample mean which, under certain circumstances, is normally distributed.
Why wood u say that because that .
Yes. The transform is z= (x-xbar)/s where x is the data value, xbar is the mean of the data and s is the standard deviation.
In a normal distribution the mean, median and mode are all the same value.
Also normally distributed.
The means of repeated samples from any population.
it must be normally distributed
Whether or not the data are normally distributed and the Customer expectations.
If the test result is significant (Lower than or equal to 0.05) = The data is not normally distributed... If the test result is not significant (Higher than 0.05) = The data is normally distributed... This synchronize with the Statistical Hypothesis Assumption (Ho and Ha) Ho means "Nothing Happen" and Ha means "Something Happen" then for KSL and Shapiro Wilk test of normality assumption also.... If the test result reject Ha and accept Ho means "NOTHING HAPPEN" to data or the data is normally distributed but if the result reject Ho and accept Ha means "SOMETHING HAPPEN" to data or in this case the data is NOT normally distributed. Dr.Tanarat Thiengkamol (send2nude@gmail.com)
d. All the above.