Raptim Research Interview Questions and Answers

Classification techniques categorize data into predefined classes based on features and assumptions.

• Logistic Regression: Assumes a linear relationship between features and log-odds of the outcome.

• Decision Trees: Assumes that data can be split into subsets based on feature values.

• Support Vector Machines: Assumes that classes can be separated by a hyperplane in a high-dimensional space.

• Naive Bayes: Assumes independence a...read more

The Central Limit Theorem (CLT) states that the distribution of sample means approaches normality as sample size increases.

• CLT applies to independent random samples from any population.

• As sample size (n) increases, the sampling distribution of the mean becomes approximately normal.

• For n ≥ 30, the sample mean is normally distributed regardless of the population's distribution.

• Example: In quality control, measuring the a...read more

Sampling methodology involves techniques to select a subset from a population for analysis, ensuring representativeness and minimizing bias.

• Random Sampling: Every member has an equal chance of selection (e.g., lottery system).

• Stratified Sampling: Population divided into subgroups (strata) and sampled from each (e.g., age groups in a survey).

• Cluster Sampling: Entire clusters are randomly selected (e.g., selecting entire...read more

ISRs must be efficient, reentrant, and quick to execute to ensure timely response to interrupts.

• Efficiency: ISRs should execute quickly to minimize interrupt latency.

• Reentrancy: ISRs must be reentrant, meaning they can be interrupted and safely called again.

• Minimal Use of Resources: Avoid using blocking calls or heavy processing within ISRs.

• Priority Handling: ISRs should manage priorities effectively to handle critical...read more