SOA PA Exam Final Exam Questions and Answers 2024

SOA PA Exam Final Exam Questions and Answers 2024

1. What to examine when assessing the bivariate relationship between a Fac- tor predictor variable and a binary target variable?: A table to asses (with rows as factor levels) the mean probabilities, counts of observations of each factor, and counts of each observation of each binary target. 2. What to examine when assessing the bivariate relationship between a Con- tinuous predictor variable and a binary target variable?: - A graph with separate histograms for a continuous variable, one for those with target binary = 0 and one for those with binary = 1; - Box plots summarized based on binary target; - Tables summarizing the mean, median, and count of the predictor based on each binary target 3. What to examine when assessing the bivariate relationship between a Fac- tor predictor variable and a Continuous target variable?: Box Plots and tables summarizing the mean, median, and count of the target based on each factor 4. What to examine when assessing the bivariate relationship between a Continuous predictor variable and a Continuous target variable?: Scatter plots. Correlation between each variable [cor() in R]. 5. What to examine when assessing (univariate analysis) a Continuous predic- tor variable?: Assess the histogram of the distribution. Check the skewness (does it need to have a log transformation). - Check for extreme (unreasonable) outliers - Check for obvious errors in data - Check for obvious duplicates 6. What to examine when assessing (univariate analysis) a Factor predictor variable?: Assess Bar chart. (Count of observations per factor level) 7. What data questions should be considered while reading the project state- ment?: Is the project statement more interested in interpretable models or more accurate complicated models? What type of variable is the target variable? What type of variable are the predictor variables? Are there any outliers that need to be removed? Are there any Factor variables that could be combined? 8. R-Code; Histogram Continuous Variable: ggplot(df, aes(x = variable)) + geom_histogram(bins = 30) + labs(x = "variable") 9. R-Code; Bar chart for a factor variable: ggplot(df, aes(x = variable)) + geom_bar() + labs(x = "variable")