Business Analysis Statistics Test #3

Question 1

Regression models that don't include duplicated input variables can:

Accepted Answer

Enhance parameter estimates' stability and cut down on overfitting danger.

Answer

Regression models that do not include duplicated or highly correlated (redundant) input variables generally benefit from enhanced parameter estimates' stability. Redundancy can lead to multicollinearity, making it difficult to determine the individual effect of each predictor and inflating standard errors. Removing such variables simplifies the model, improves interpretability, and reduces the risk of overfitting by focusing on truly independent contributions.

Question 2

An analyst is aware that storeId, a categorical predictor, is a key indicator of the objective. To be a workable predictor in the model, store Id has too many levels. In order to treat them as members of the same class level, the analyst wants to consolidate stores. What are the two most efficient solutions to the issue? (Select two.)

Accepted Answer

Combine stores that are related by using subject-matter expertise.

Answer

When a categorical predictor like `storeId` has too many levels, it can lead to sparse data, overfitting, and computational issues. Two effective solutions involve grouping similar stores. One approach is to use subject-matter expertise to combine stores based on known characteristics like location, size, or customer demographics. Another sophisticated method is to employ clustering techniques, such as Greenacre's approach (often associated with correspondence analysis), to identify and group stores with similar response patterns, thereby reducing the number of levels while retaining predictive power.

Question 3

Which interpretation of the estimate is accurate?

Accepted Answer

For every $1,000 increase in wage, the likelihood of the incident increases by 1.142.

Answer

Assuming 1.142 represents an odds ratio for a $1,000 increase in wage, the correct interpretation is that for every $1,000 increase in wage, the *odds* of the incident occurring multiply by 1.142. This means the likelihood (or odds) of the incident increases by 14.2% for each $1,000 wage increment. Option C best reflects this interpretation of a multiplicative change in likelihood per unit increase.

Question 4

Regression models with redundant input variables may:

Accepted Answer

Make parameter estimations less stable and make overfitting more likely.

Answer

Regression models containing redundant input variables, often due to multicollinearity, can lead to several problems. These include making parameter estimates less stable, meaning their values can fluctuate significantly with small changes in the data. Furthermore, redundant variables can increase the risk of overfitting, as the model might learn noise or specific patterns from the training data that do not generalize well to new data, leading to poorer performance on unseen observations.

Question 5

Which sampling techniques are suitable for data partitioning for model evaluation?

Accepted Answer

Simple random sampling without replacement

Answer

Simple random sampling without replacement is suitable because it ensures each observation has an equal chance of being selected for a partition and prevents duplicates across subsets, creating independent datasets for training, validation, and testing. Stratified random sampling without replacement is also suitable, especially for imbalanced datasets, as it ensures that each partition maintains the same proportion of key characteristics as the original dataset, leading to more representative and robust model evaluation. Both methods help in creating unbiased and independent data splits for model evaluation.

Business Analysis Practice Test

Business Analysis Practice Test

Business Analysis Statistics Test #3