Figure one indicates table entries for the randomly chosen data for height and weight considering the limits given. The figure also shows a scatter plot with individual regression lines and the r squared value. From the scatterplot, the regression equation is, y = 0.5259 x + 49.07 with R² = 0.1866. The correlation coefficient R=0.432. The correlation , denoted by r, measures the amount of linear association between two variables. My correlation result indicates that we have a small positive correlation between height and weight.
Figure 1: Height and Weight Values and the Scatter Plot
This concept can be applied when one wishes to study the relation between two variables. That is when one wants to study the effect of one variable on the other variable. This is studied under the head "regression analysis" in statistics. When two variables (or more) are positively correlated, i.e., if one increases, others increase too, and vice versa, the slope of the trend line or regression line is positive. On the other hand, if the variables are negatively correlated, the slope of the trend line is negative. For instance, if we are to study the relationship between the price of a product and its quality, we can use this concept. Naturally, the price inflates as the quality improves. However, situations may sometimes be challenging to understand verbally. Such complex cases can be tackled using the concept of regression analysis.
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