It is impossible to answer the question because "the following" did not follow.
no
z- statistics is applied under two conditions: 1. when the population standard deviation is known. 2. when the sample size is large. In the absence of the parameter sigma when we use its estimate s, the distribution of z remains no longer normal but changes to t distribution. this modification depends on the degrees of freedom available for the estimation of sigma or standard deviation. hope this will help u.... mona upreti.. :)
The mean, median, and mode of a normal distribution are equal; in this case, 22. The standard deviation has no bearing on this question.
No. The standard deviation is not exactly a value but rather how far a score deviates from the mean.
The answer will depend on the underlying distribution for the variable. You may not simply assume that the distribution is normal.
mean deviation is minimum
They are measures of the spread of the data and constitute one of the key descriptive statistics.
SE stands for ''standard error'' in statistics. Thanx Sylvia It is the same as the standard deviation of a sampling distribution, such as the sampling distribution of the mean.
Relevant statistics contain data that directly answers the question researchers analyzed. Findings include samples with standard deviation, distribution, and variance included.
Parametric and non-parametric statistics.Another division is descriptive and inferential statistics.Descriptive and Inferential statistics. Descriptive statistics describes a population (e.g. mean, median, variance, standard deviation, percentages). Inferential infers some information about a population (e.g. hypothesis testing, confidence intervals, ANOVA).
Mean and Standard Deviation
The standard deviation in a standard normal distribution is 1.
If the Z-Score corresponds to the standard deviation, then the distribution is "normal", or Gaussian.
The standard deviation in a standard normal distribution is 1.
No. Descriptive statistics are those that characterise samples without attempting to draw conclusions. The purpose of them is to help investigators to form an understanding of what the data might be capable of telling them. Descriptive statistics include graphs as well as measures of location, scale, correlation, and so on. Parametric statistics are those that are based on probabilistic models (ie, mathematical models involving probability) that involve parameters. For instance, an investigator might assume that her results have come from a population that is normally distributed with a certain mean and standard deviation; this would be a parametric model. She could estimate this pair of parameters, the mean and standard deviation, using parametric statistics, or test hypotheses about them, again using parametric statistics. In either case the parametric statistics she uses would be based on the parametric mathematical model she has chosen for her data.
The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
The total deviation from the mean for ANY distribution is always zero.