It is the number of times that an observation - of some characteristic - is made.
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There is no minimum number: very few observations can be indicative. As the population number increases the observations should get closer to the Normal distribution. You should have 30 or so observations to get a smooth-ish curve.
The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x. The cumulative frequency distribution for a value x of a random variable X, is a count of the number of observations in which X is less than or equal to x.
With either test, you have a number of categories and for each you have an expected number of observations. The expected number is based either on the variable being independent of some other variable, or determined by some know (or hypothesised) distribution. You will also have a number of observations of the variable for each category. The test statistic is based on the observed and expected frequencies and has a chi-squared distribution. The tests require the observations to come from independent, identically distributed variables.
It has no meaning. In statistics, if you have a set of observations, the lower quartile (Q1) is the value such that a quarter of the [number of] observations are smaller and three quarters are larger. The upper quartile, Q3, is defined similarly as the value such that a quarter of the observations are larger. The interquartile range, is the distance between these two: IQR = Q3 - Q1.
It is the observed frequency divided by the total number of observations, expressed as a percentage.