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PA Exam With Questions And Accurate Answers
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PA Exam With Questions And Accurate AnswersCode to remove variables - Answer rm( )
Clustering and PCA are what type of algorithms - Answer Unsupervised that find and
simplify variation in the data without you, as the user, having to do it
Code for Histogram - Answer ggplot(data=data) + geom_histogram(aes(x=variable))
Code for Histogram using density - Answer ggplot(data=data) +
geom_density(aes(x=variable), fill="color")
Code for Scatterplot - Answer ggplot(data=data, aes(x=variable, y=variable)) +
geom_point(aes(color="color
Code for Boxplot - Answer ggplot(data=data, aes(x=variable, y=variable)) +
geom_boxplot()
Code for Violion - Answer ggplot(data=data, aes(x=variable, y=variable)) + geom_violin()
Code for Line Graph - Answer ggplot(data=data, aes(x=variable, y=variable)) +
geom_lime()
Code for RMSE - Answer predict.variable <- predict(regression, data, type="response")
rmse.variable <- sqrt(sum(predict.variable - data$variable)^2)/nrow(data)
rmse.variable
Code to add labels - Answer labs(x=" ", y=" ", title=" ")
,Code for weighted Histogram - Answer ggplot(data, mapping=aes(x, fill=variable) +
geom_hist(aes(weight=expo),alpha=#
or
ggplot stays the same + geom_hist(aes(weight,fill=variable),alpha=#)
Note for the Exam 1 - Answer For missing data you can remove the record and comment
on a more sophisticated approach
Technique 1 to handle missing value - Answer Remove the column if there are so many
missing values if it is no longer useful in the model
Technique 2 to Handle Missing Value - Solution Remove a row in cases of missing
values, sparse enough and random enough not to create any bias and not impact the
performance of the model.
Technique 3 for dealing with missing values - Answer Replace the missing value with
mean, median or mode. You can either use this based off the other data or randomly
select ad replace. Either way this approach assumes the missing values are completely
random.
Technique 4 for dealing with missing values - Answer Replace missing values with an
assigned category/value. This should be used if data is missing systematically.
Technique 5 for handling missing values - solution Project the missing value given the
other variables Imputation relies on a model or models based on the combinations of
variables in order to impute the missing values. We can only do this if we can assume
there is a "true" value associated with every missing value and that value was omitted.
Note for Exam 2 - Solution During the exam you will not be requested to take samples
, from the data other than to create training and validation sets.
Note for Exam 3 - Solution During the exam you have to choose an appropriate time
frame and sample such that you have a good representation of the intended population
in your dataset.
Purpose of ASOP 23 - Answer The purpose of this actuarial standard of practice (ASOP)
is to provide guidance to the actuary when performing the following: a. selecting the
data that underlie the actuarial work product; b. relying on data supplied by others; c.
reviewing data; d. using data; e. preparing data to be used by other actuaries in an
actuarial work product; and f. making appropriate disclosures with regard to data
quality.
Formula for a linear model - Answer lm(dependant~independant + independant + .,
data=data)
Predict a point using a linear model - Answer predict(lm,
newdata=data.frame(independant=#), type = response)
4 common situations where linear models are not appropiate - Answer 1. The range of
target variable values is positive. The normal distribution allows for negative values and
hence the model may predict negative outcomes.
2. The target variable's variance is dependent on its mean. This violates the assumption
of constant variance.
3. The target variable is binary in nature. A restriction to response values of 0 and 1
does not fit the normal distribution, as values can go out of range.
4. LM are sensitive to outliers
3 General assumptions of GLMs - Answer 1. Given the predictor variables, the target
variables are independent.
2. Given predictor variables, the distribution of target variables is a member of the
exponential family.
3. Given predictor variables the expected value of the target variable is u=g^(-1)(n),
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