SOA PA EXAM 2025/2026 QUESTIONS AND ANSWERS

SOA PA EXAM 2025/2026 QUESTIONS
AND ANSWERS




What to examine when assessing the bivariate relationship between a Factor predictor variable
and a binary target variable? - ANS 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.


What to examine when assessing the bivariate relationship between a Continuous predictor
variable and a binary target variable? - ANS - 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


What to examine when assessing the bivariate relationship between a Factor predictor variable
and a Continuous target variable? - ANS Box Plots and tables summarizing the mean,
median, and count of the target based on each factor


What to examine when assessing the bivariate relationship between a Continuous predictor
variable and a Continuous target variable? - ANS Scatter plots. Correlation between each
variable [cor() in R].


What to examine when assessing (univariate analysis) a Continuous predictor variable? -
ANS Assess the histogram of the distribution. Check the skewness (does it need to have a log
transformation).
- Check for extreme (unreasonable) outliers
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,- Check for obvious errors in data
- Check for obvious duplicates


What to examine when assessing (univariate analysis) a Factor predictor variable? -
ANS Assess Bar chart. (Count of observations per factor level)


What data questions should be considered while reading the project statement? - ANS 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?


R-Code; Histogram Continuous Variable - ANS ggplot(df, aes(x = variable)) +
geom_histogram(bins = 30) +
labs(x = "variable")


R-Code; Bar chart for a factor variable - ANS ggplot(df, aes(x = variable)) +
geom_bar() +
labs(x = "variable")


R-Code; Table for binary target and factor variable - ANS data %>%
group_by(variable) %>%
summarise(
zeros = sum(Target == 0),
ones = sum(Target == 1),
n = n(),
proportion = mean(Target)


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, )


R-Code; Separate histograms for a continuous variable and a binary target - ANS ggplot(
data,
aes(
x = variable,
group = Target,
fill = as.factor(Target),
y = ..density..
)
)+
geom_histogram(position = "dodge", bins = 30)


R-Code; Relevel Factor variables - ANS table <- as.data.frame(table(df$variable))
max <- which.max(table[, 2])
level.name <- as.character(table[max, 1])
df$variable <- relevel(df$variable, ref = level.name)


R-Code; Remove all observations in entire data set of a variable greater than or equal to 50 -
ANS data <- data[data$variable <= 50, ]


R-Code; Remove all observations of a factor variable = "value" - ANS toBeRemoved <-
which(data$factor=="value")
data <- data[-toBeRemoved, ]


R-Code; Combine factor levels into new factors. - ANS var.levels <- levels(df$variable)
df$occupation_comb <- mapvalues(df$variable, var.levels, c("Group12", ... , "GroupNA"))


R-Code; remove a variable from the dataframe. - ANS df$variable <- NULL

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