We started the course lecture discussing the course structure and ventured into the topic for our first project which is Linear regression. I for one like going back to the basics before I can dig deep into a topic so I went back and referred the text. On reading some text, I recognized and recollected that for any kind of analysis using statistical methods, we need to understand the data and connect with it to better familiarize with its nature. Since the data provided to us is from a real source hence the predictions also need to be done in a more realistic manner instead of just trying to fit it into a simple ideal model.
What needs to be considered at this point is that the data will have errors and these errors need to be given enough significance to preserve the realistic nature of the data. I learnt about the Linear least squares model given by Karl Gauss which helps calculate the absolute value of the error in values and minimizes it to idealize the data points and plot it into a linear model. However, this model is unstable and hence unreliable. I plan on referring to available data points and plotting individual models at first so I can then move over to finding a correlation between obesity and inactivity to predict the percentage diabetes ahead.