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2: Building Data Analytics Foundations
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Ch 9: Applying Data Analysis to Risk... Quiz/Exam 3: Strengthening the Insurance Valu... CPCU 5
19 terms 13 terms 27 terms 91 terms
maggiemh08 Preview dfaust22 Preview dfaust22 Preview Jon
Katie is a data analyst who is analyzing a dataset of B. 0.05
100,000 workers compensation claims for her employer.
The data shows that on average, 10% of first aid claims
convert to indemnity within the first year. Through the use
of a predictive model, he learns that for insureds in
specific geographic regions, 15% of first aid claims
convert to indemnity. Katie calculates the leverage to be
Select one:
A. 0.015
B. 0.05
C. 0.25
D. 1.5
Sebastian is a corporate risk manager who is developing D. The model had overfit the data.
a model to predict insureds who are more likely to make
fraudulent claims. He has identified attributes that
contribute to fraud and had been testing the model using
holdout data. Historical data indicates that 5% of insureds
file fraudulent claims, but the model predicts that 90% of
insureds are likely to commit fraud. Sebastian concludes
that
Select one:
A. The data had underfit the model.
B. The data had overfit the model.
C. The model had underfit the data.
D. The model had overfit the data.
, Rodrigo is a data analysist who is using cluster analysis to A. Identifying the variables to analyze.
solve a problem. Rodrigo is most likely
Select one:
A. Identifying the variables to analyze.
B. Determining a numerical value for a target variable.
C. Determining a categorical value for a target variable.
D. Performing complex data classification.
If the data used in a predictive model has too much A. Overfitting.
complexity, it will not be accurate when data beyond the
training data is applied to it. This process is known as
Select one:
A. Overfitting.
B. Matrix confusion.
C. Cross-validation.
D. Generalization.
Fatima is a data scientist for an insurer. While analyzing C. High gain, low entropy.
claims data, she finds that insureds with a specific make of
automobile have a higher incidence of theft than other
insureds. Fatima would describe the relationship between
automobile make and theft losses as
Select one:
A. Low gain, high entropy.
B. High gain, high entropy.
C. High gain, low entropy.
D. Low gain, low entropy.
Goshen Mutual is a personal lines insurer. It has decided D. Kathy has a high degree of betweenness.
to use link prediction models to target market new
customers through social media connections with current
customers. The model indicates that information that
current customer Kathy posts on a platform spread
widely through social media connections. Which one of
the following statements is true about Kathy?
Select one:
A. Kathy has a minimal degree of betweenness.
B. Kathy has a minimal degree of closeness.
C. Kathy has a high degree of closeness.
D. Kathy has a high degree of betweenness.
Jack is analyzing the incidence of auto theft claims in a C. Nearest neighbors.
dataset. He has identified claimant attributes related to
theft, plotted the claims as data points, and measured the
distance between them. Jack refers to the data points
closest to each other as
Select one:
A. Class labels.
B. Predictive models.
C. Nearest neighbors.
D. Link predictions.
2: Building Data Analytics Foundations
Save
Students also studied
Ch 9: Applying Data Analysis to Risk... Quiz/Exam 3: Strengthening the Insurance Valu... CPCU 5
19 terms 13 terms 27 terms 91 terms
maggiemh08 Preview dfaust22 Preview dfaust22 Preview Jon
Katie is a data analyst who is analyzing a dataset of B. 0.05
100,000 workers compensation claims for her employer.
The data shows that on average, 10% of first aid claims
convert to indemnity within the first year. Through the use
of a predictive model, he learns that for insureds in
specific geographic regions, 15% of first aid claims
convert to indemnity. Katie calculates the leverage to be
Select one:
A. 0.015
B. 0.05
C. 0.25
D. 1.5
Sebastian is a corporate risk manager who is developing D. The model had overfit the data.
a model to predict insureds who are more likely to make
fraudulent claims. He has identified attributes that
contribute to fraud and had been testing the model using
holdout data. Historical data indicates that 5% of insureds
file fraudulent claims, but the model predicts that 90% of
insureds are likely to commit fraud. Sebastian concludes
that
Select one:
A. The data had underfit the model.
B. The data had overfit the model.
C. The model had underfit the data.
D. The model had overfit the data.
, Rodrigo is a data analysist who is using cluster analysis to A. Identifying the variables to analyze.
solve a problem. Rodrigo is most likely
Select one:
A. Identifying the variables to analyze.
B. Determining a numerical value for a target variable.
C. Determining a categorical value for a target variable.
D. Performing complex data classification.
If the data used in a predictive model has too much A. Overfitting.
complexity, it will not be accurate when data beyond the
training data is applied to it. This process is known as
Select one:
A. Overfitting.
B. Matrix confusion.
C. Cross-validation.
D. Generalization.
Fatima is a data scientist for an insurer. While analyzing C. High gain, low entropy.
claims data, she finds that insureds with a specific make of
automobile have a higher incidence of theft than other
insureds. Fatima would describe the relationship between
automobile make and theft losses as
Select one:
A. Low gain, high entropy.
B. High gain, high entropy.
C. High gain, low entropy.
D. Low gain, low entropy.
Goshen Mutual is a personal lines insurer. It has decided D. Kathy has a high degree of betweenness.
to use link prediction models to target market new
customers through social media connections with current
customers. The model indicates that information that
current customer Kathy posts on a platform spread
widely through social media connections. Which one of
the following statements is true about Kathy?
Select one:
A. Kathy has a minimal degree of betweenness.
B. Kathy has a minimal degree of closeness.
C. Kathy has a high degree of closeness.
D. Kathy has a high degree of betweenness.
Jack is analyzing the incidence of auto theft claims in a C. Nearest neighbors.
dataset. He has identified claimant attributes related to
theft, plotted the claims as data points, and measured the
distance between them. Jack refers to the data points
closest to each other as
Select one:
A. Class labels.
B. Predictive models.
C. Nearest neighbors.
D. Link predictions.
