2014年5月28日 · A P-P plot compares the empirical cumulative distribution function of a data set with a specified theoretical cumulative distribution function F(·). A Q-Q plot compares the quantiles of a data distribution with the quantiles of a standardized theoretical distribution from a specified family of distributions. In the text, they also mention:

2012年3月19日 · Traditionally P-P plot is done as follows. On Y axis , expected cumulative probabilities of the theoretical distribution of your choice are plotted, corresponding to your observed values. That is, for example of normal distribution, CDF.NORMAL(var,m,sd) are plotted, where var is your data, m and sd are the parameters estimated from the data or ...

2016年11月26日 · I'm mainly confused on exactly what the x and y values are supposed to be within a P-P plot. The problem I'm given is as follows: If the following data are hypothesized to be drawn from a Poisson distribution:

2022年8月15日 · Aside: In the second QQ plot (with better scaling) we see that the sample has a heavier right tail than the Normal and is somewhat skewed. There are a lot of points in this QQ plot, so this indicates a degree of non-normality. You should look at the residual plot as well, ie, plot the residuals against the fitted values. In both QQ plots:

2019年11月14日 · In your plot errors seem to have different variability at the beginning of the plot then in the end so I would say there is heteroskedasticity there. Probability-probability (p-p) plot measures how closely two distributions match together. If you get perfect straight lines the distributions are perfect match.

2017年6月30日 · I'd summarize usage as (a) probability plot is a broad term which covers all these cases and some more; (b) Q-Q plots is a strict term and implies quantiles on both axes with probability implicit (c) P-P plots is another strict term and the opposite of (b). P-P plots have probabilities on both axes and quantiles are implicit.

2015年1月25日 · An S-shape P-P plot indicates that the distribution has the correct median. The "flattening" of the S means that the distribution has tails that are about as long as those of the normal distribution. So your tails aren't "short". Rather, the density decays faster to meet a tail of the same length as the Gaussian distribution.

2017年5月9日 · I also have another question. To prove that the residues follow a normal distribution, I have to do the normal Q-Q test and the normal P-P test. In the case of the normal Q-Q test gave me this chart: If a linear regression occurs, we conclude that the residuals have a normal distribution, which is what I want to prove.

If a p-value deviates from the expected distribution one "may" call that p-value for statistic significant. As you can see in the QQ-plot, at the top tail end, the last 4 points are somewhat hard to interpret. Two of the last points in the grey suggests that those p-values are in the expected distribution of p-values, whilst the other two are not.

2017年4月19日 · I'm doing a stats assignment and for one of the questions I need to make a judgement of whether there is skew and kurtosis from a p-p plot in SPSS. I've been over the lecture, and we were told to look for "snaking" around the line for skew, and points "hanging" off the line for kurtosis.