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So whenever we have the data in the tabular format ready with us, we should first look at the descriptors

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of each variable.

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The descriptive statistics, as we discussed in another section, are used to describe the data and

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summarize it.

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And since we will describe each and every single variable and not relationships between two or more

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variables, it is called univariate analysis unit stange.

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What one where it stands, what variable?

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So it is one variable analysis.

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So we can look at the buildings as part of the univariate analysis.

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We can see mean median mode.

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We can see measures of dispersion like range quartiles and standard deviations.

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And for categorical data, we can look at count of each category.

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Most software packages for statistics have a very easy way to do univariate analysis for all the variables

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of the dataset.

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And when we run it, we see something like this.

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So for a variable like age, we can have info like mean median, minimum, maximum the 25th.

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58 and 75 percentile values.

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Imagine if I arrange all the ages in ascending order, the first value will be the minimum value.

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The last value will be the maximum value.

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The 25th percentile value this twenty five point seven five.

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Value will be at one fourth position.

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That is, 25 percent of the values will be lower than this value.

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Twenty nine.

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This 50 percentile value will be at the middle.

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That is, 50 percent of values will be lower than this.

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And this 50 percentile value is same as the median also.

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If you remember the definition of median.

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Seventy five percent value prettified went to file is added Trefort position.

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So seventy five percent values are below thirty five point two for a.

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And from 75 percentile to the maximum value is thirty five point two five two fifty one.

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This range is 70 and they'll do maximum.

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So you can observe what this data.

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The first twenty five percent values are in a very small range of twenty four to twenty five point seven

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five.

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But if you only see the last quartile, the last twenty five percent of values add between the huge

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range of thirty five point two, five to fifty one.

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So this distribution is not evenly distributed.

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Such observations help us identify issues in our data.

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This whole information for all the variables of the dataset is called the Extended Data Dictionary.

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Using EDT, we can deduce a lot of things like patterns of outliers, presence of missing values and

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so on.

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What are these issues and how we handle them will be covered in the coming videos?