 Students, now we are going to discuss histogram. Histogram, actually we make for the ordinal variables or interval variable. And in some cases, we make this graph for the continuous variables as well. Histogram, we can say that is the advanced form of the bar graph. A histogram is used to summarize discrete or continuous data. It provides a visual interpretation of numerical data by showing the number of data points that fall within a specific range of values. For example, I have told you that histogram we use for the interval variable. So we get to know the range of values in the intervals. And in continuous, we use it with normal curve. So we will go ahead and see it in detail. This is the way of the vertical bar graph. But there are no gaps between the bars. There are no gaps because we do not assume the distance of the data points. Because it is ordinal, we assume the distance of the data points. If I show this in front of you, this bar graph is a simple bar graph which is computed on continuous variables for the GPA. In this, you can see that the data is not on the ordinal level, it is on the interval level, but on the continuous level. So in histogram, we made this histogram for the frequency. From 1.5 to 4.50, these are the values of the histogram. These are the values of the GPA. In this, we are giving 3.24. The standard deviation is 0.407. The total number is 1245. And the frequency is given at each value. So the most frequency we have is between 3 and 3.50. So here, we can also fit in the normal curve on the histogram. So in this, we are telling that the data is a little skewed on the negative side. Because here, there is a little data. And according to them, it is a negative least skewed. On the histogram, if you make it on the bivariate, it becomes something like this. In this, we want to compare the gender category on the basis of age. So for male and female, it will be different. It will compare you in one graph. Here, it is the same data and the normal curve is also fit in it. So you can see that both the curves are a little violated from the normal shape curve. We call it the bell shape distribution. Here, the data is more skewed on the positive side in the female.