 Students, now we are going to see the Interrupted Time Series Design in Kozai Experimental Research. The first design which we are going to study in Interrupted Time Series Designs is Simple Interrupted Time Series Design. The speciality of this design is that there will be only one group, and there will be multiple observations before and after the treatment. That is, we will take an equal number of observations before and after the treatment. The speciality of this design is that we take multiple observations. So, their amount should be similar. That is, if you observe a group five times, and give them treatment, then we will observe them five times. Let me give you an example of this. For example, recently in the pandemic, COVID-19 cases were observed in a particular town in five months. How many cases were there in COVID-19? Based on that, it was decided that booster dose was required for COVID vaccination. After giving the booster dose, it was observed in five months that how many cases of COVID-19 have appeared there. So, over the long period of the time, we can see that the intervention was actually impacted or not. So, what we do in the second design is that we add one with our treatment group in the control group. So, what happens in this? We do multiple observations for both groups, for the treatment group and for the control group. And we deliver the treatment in the treatment group. And then we do the similar multiple observations in both groups. So, let me give you an example of this. For example, there are traffic accidents of motorcycles and cars. So, you want to see that in the last five years, in Lahore city, there have been so many traffic accidents of motorcycles and cars. So, based on this data, what do you do? We start a campaign in which you insure the compulsion that the bike riders insure the helmet. So, after this campaign, we see the trend of traffic accidents in the next five years. So, the treatment in this already exists, but it has been re-insured. So, what do you know about this? When you insure one thing, then after the treatment, the ratio of accidents decreases. In our third design, we take two different groups. We consider one as a control group and the other as a treatment group. We take these two as dependent variables. In this, we do the intervention in the treatment group and we do not do the intervention in the other group. For instance, if I give you an example of traffic accidents, then what do you do? We only consider the traffic accidents of the bike riders and we consider the heavy bikers and the small bikers, like CD70, Honda 125, and the heavy bikers, what are their accidents? So, from helmet wearing, how much has the trend changed? So, this can be seen in this way.