What to examine when assessing the bivariate relationship between a Factor predictor variable and a binary target variable? - Answer 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.
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SOA PA Exam Questions And Already
Passed Answers.
What to examine when assessing the bivariate relationship between a Factor predictor variable and a
binary target variable? - Answer 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? - Answer - 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? - Answer 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? - Answer Scatter plots. Correlation between each variable [cor() in R].
What to examine when assessing (univariate analysis) a Continuous predictor variable? - Answer 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
What to examine when assessing (univariate analysis) a Factor predictor variable? - Answer Assess Bar
chart. (Count of observations per factor level)
What data questions should be considered while reading the project statement? - Answer 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; remove a variable from the dataframe. - Answer df$variable <- NULL
R-Code; Create training and testing sets. - Answer set.seed(n)
train_ind <- createDataPartition(df$Target, p = 0.7, list = FALSE)
data.train <- df[train_ind, ]
data.test <- df[-train_ind, ]
What type of data to use a log transformation? - Answer Right Skewed (common with variables of
Time, Distance, or Money which have a lower boundary of 0)
What type of data to use a Logit transformation? - Answer Binary (boolean) Target variable
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