Week 4: Linear Regression Model

Hello everyone, welcome to my week 4 blog. We’re almost halfway through! This week, my goal is to continue visualizing my dataset using scatter plots and linear regression summaries.

Last week, I focused on analyzing the coefficient value of each graph. This week, I move on to focus on the R-square value. In simple terms, R-square values determine how well the graph predicts its relationships. If you have an R-square value close to 1, this means the graph is very accurate in terms of predicting the next variables. Similarly, if you have an R-square value close to 0, it means the graph is very inaccurate in terms of predictions.

In the graph of Temperature vs. pH, the analysis showed an R-squared value of 0.597. This means that approximately 59.7% of pH data can be predicted by temperature data. This a strong relationship, suggesting that as temperature changes, pH levels also change in a predictable pattern. In another graph of pH vs. TDS shows an R-squared value of 0.793. This shows stronger and more accurate predictions compared to the previous graph. Both graphs depict strong predictions indicating that as pH changes, both temperature and TDS will change by a similar amount.

Next week, I will move on from Linear Regression to the Model of Goodness, checking the linear assumptions of the graphs!