# What can we say about QQPlot of this data ?

• paawansharmas
In summary, The QQPlot of the first dataset shows that it is not normally distributed, with more data on the tails than a normal distribution would have. The QQPlot of the second dataset appears to be approximately normal, but is discrete instead of continuous. Jaque-Bera tests were performed, with the first dataset failing and only three datasets similar to the second one passing the test for normality. The p-values for the JB test for 14 datasets similar to the second graph range from 0 to 0.782434, with three datasets having a p-value below 0.05. Despite the similar QQPlots, there is a difference in normality between the datasets, with only three passing the test. The reason for this difference

#### paawansharmas

This is the QQPlot of a data. What can be inferred from this plot ?
(Tested against Normal Distribution)
QQPLOT 1 :

QQPLOT 2:

#### Attachments

• qq1.png
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• qq2.png
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The first one is not normal - there is more data on the tails than there would be in a normal distribution.

The second one looks approximately normal, except for the fact that it is discrete instead of continuous.

thnks mxscnt

Jaque-Bera tests failed for first dataset.
while for data sets similar to second one, few were passing JB test for normality.

these are the p-values for JB test for 14 data sets similar to second graph.

P-VALUE
0.005
0.039
0.003
0
0.00287214
0.595792
0.190489
0.0947931
0.782434
0
0.12
0.257
0.656
0.246

you can see almost 3 sets qualify for normality.

Though there QQPlot are almost similar.

What can be the reason for difference ?

## 1. What is a QQ plot and how is it used to analyze data?

A QQ plot, or quantile-quantile plot, is a graphical technique used to compare the distribution of a dataset to a theoretical distribution. It plots the quantiles of the dataset against the quantiles of the theoretical distribution, allowing us to visually assess how well the dataset fits the theoretical distribution.

## 2. How do you interpret a QQ plot?

The closer the points on the QQ plot are to the diagonal line, the closer the dataset is to the theoretical distribution. If the points deviate significantly from the diagonal line, it suggests that the dataset does not fit the theoretical distribution well.

## 3. What can we say about a QQ plot if the points fall on a straight line?

If the points on the QQ plot fall on a straight line, it suggests that the dataset follows a normal distribution. However, it is important to also consider the sample size and any potential outliers in the dataset.

## 4. Can QQ plots be used to compare two datasets?

Yes, QQ plots can be used to compare two datasets by plotting the quantiles of one dataset against the quantiles of the other. This can help to determine if the two datasets have a similar distribution.

## 5. Are QQ plots affected by sample size?

Yes, the accuracy of a QQ plot is affected by sample size. A larger sample size will result in a more accurate representation of the theoretical distribution on the QQ plot, making it easier to detect any deviations from the diagonal line.