Module 10 Assignment

 Question 1: Conduct ANOVA (analysis of variance) and Regression coefficient to the data from cystfibr: data ("cystfibr") database. Note that the dataset is part of the ISwR package in R.

I created a linear regression model that predict age using multiple predictors from the cystfiber dataset. The summary showed an intercept of 17.367 and coefficients of other variables. The age is positively correlated with "weight" and "fev1" and negatively correlated with "bmp" and "pemax". The low p-value (7.817e-11) indicates the model is statistically significant. I displayed the coefficients side-by-side for easier comparison.

The ANOVA table for the regression model shows that age variance is analyzed against the predictors and residuals. Weight and bmp are highly significant predictors, while pemax and fev1 are less significant.

Question 2: You can choose any variable you like in your report, you need to state the result of Coefficients (intercept) to any variables you like both under ANOVA and multivariate analysis. I am specifically looking at your interpretation or R results.

I used the data() function to load the secher dataset and view its summary. I log-transformed bwt, ad, and bpd and created two linear models predicting log.bwt from log.ad or log.bpd. The coefficient summed to 5.568. I then fitted a model with both predictors, where log.ad = 1.467 and log.bpd = 1.552 (sum = 3.019). The summary shows both predictors are highly significant (p < 202e-16).

Using diameters in the prediction model, the sum of coefficients is about 3, as expected. This shows that both predictors significantly contribute to predicting and have nearly equal influence. Overall, the results provide clear insight into how these variables interact and affect.