Intro to R
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Three commands that were used in the video were:
1) plot(Age, LungCap, main=Scatterplot). This was used to obtain scatterplot, modeling the relationship between Age and Lung Capacity, with Lung Capacity being the Y variable (dependent variable) on the axis.
2) cor(Age, LungCap). This was used to calculate the Pearsons correlation between the two variables.
3) attributes(mod). This command tells which particular attributes are stored in the object (the linear model).
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Simple regression is a basic predictive analytical tool. It enables us to model and investigate the relationship between two variables: a dependent variable (outcome) and an independent variable (predictor). In the example from the tutorial, the variables were age (independent) and lung capacity (dependent).
One of the most crucial aspects of simple regression is the scatterplot. The command 'plot(Age, LungCap, main=Scatterplot")' was used to create a visual representation of the data points on an XY-plane, giving us a quick overview of how the two variables might relate to each other. This is the first step in identifying potential relationships and trends.
The command 'cor(Age, LungCap)' was then used to calculate the Pearson's correlation coefficient, which is a measure of the strength and direction of the association between the two variables. A correlation close to...
…relationships with various influencing factors.3
One key point I found was the ability to generate scatterplots in R to visualize data and the possible relationship between two variables, which I find to be a very useful feature. It helps in forming initial hypotheses about the nature of the relationship between variables, even before performing the actual regression analysis. Also, regression allows us to quantify the strength of the relationship between variables. The command 'cor(Age, LungCap)' calculates the correlation coefficient, which was a very good way to summarize the relationship numerically. This quantitative element, complementing visual interpretation, is helpful for establishing rigorous data…
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