![spss ibm normal distribution graph create spss ibm normal distribution graph create](https://spss-tutorials.com/img/standard-normal-distribution-with-probabilities.png)
It is seldom "correct" - but it generally doesn't have to be exactly correct to be useful.
![spss ibm normal distribution graph create spss ibm normal distribution graph create](https://mathcracker.com/images/legacy/graph.png)
See the question is normality testing essentially useless? The normal distribution is a convenient approximation for many purposes. The important question isn't really whether the data are exactly normal - we know a priori that can't be the case, in most situations, even without running a hypothesis test - but whether the approximation is sufficiently close for your needs. so why bother testing? Even if not exact, normality can still be a useful model But although this makes both negative values physically plausible and interpretable (negative centered values correspond to actual values lying below the mean), it doesn't get around the issue that the normal model will produce physically impossible predictions with non-zero probability, once you decode the modelled "centered age" back to an "actual age". That way both positive and negative "centered ages" are possible. I've occasionally seen it suggested that we can evade this problem by centering the data to have mean zero. (Though if you look at a population pyramid, it's not clear why you would expect age to be even approximately normally distributed in the first place.) Similarly if you had heights data, which intuitively might follow a more "normal-like" distribution, it could only be truly normal if there were some chance of heights below 0 cm or above 300 cm. For ages, a normally distributed model will predict there is a non-zero probability of data lying 5 standard deviations above or below the mean - which would correspond to physically impossible ages, such as below 0 or above 150. The normal distribution has infinitely long tails extending out in either direction - it is unlikely for data to lie far out in these extremes, but for a true normal distribution it has to be physically possible.
Spss ibm normal distribution graph create how to#
If you prefer the spoken word over the written word, check out our YouTube channel, and this tutorial showing how to create a histogram in SPSS.We usually know it's impossible for a variable to be exactly normally distributed. You should now be able to create a histogram within SPSS using one of its legacy tools. If you want to save your histogram, you can right-click on it within the output viewer, and choose to copy it to an image file (which you can then use within other programs). You’ll notice that SPSS also provides values for mean and standard deviation. The y-axis (on the left) represents a frequency count, and the x-axis (across the bottom), the value of the variable (in this case Height). The SPSS output viewer will pop up with the histogram that you’ve created. You’re now ready to create the histogram. We suggest you also tick the Display normal curve option, though this is optional. You can do this by selecting the variable, and then clicking the arrow (as above). You need to select the variable on the left hand side that you want to plot as a histogram, in this case Height, and then shift it into the Variable box on the right. The simplest and quickest way to generate a histogram in SPSS is to choose Graphs -> Legacy Dialogs -> Histogram, as below. For example, are there more heights at the top end than at the bottom end – in other words, is the distribution skewed? A histogram will go some way to answering this question. We want to know how the frequency of heights is distributed. The variable we’re interested in out of the three you can see here is height.