DMC DMC Statistical Methods & Data Mining Concepts 1 — Questions and Answers
Question 1: What does the correlation coefficient measure in data mining?
- The strength and direction of the linear relationship between two variables (Correct answer)
- The average value of a dataset
- The number of outliers in a dataset
- The variance of a single variable
Correct answer: The strength and direction of the linear relationship between two variables
The correlation coefficient measures the strength and direction of the linear relationship between two variables, ranging from -1 to +1.
Question 2: Which measure of central tendency is LEAST affected by extreme outliers?
- Mean
- Mode
- Median (Correct answer)
- Range
Correct answer: Median
The median is least affected by outliers because it represents the positional middle value, not influenced by extreme values on either end.
Question 3: In hypothesis testing, a p-value less than 0.05 typically indicates:
- The null hypothesis should be accepted
- The result is statistically significant (Correct answer)
- The sample size is too small
- There is no correlation between variables
Correct answer: The result is statistically significant
A p-value below 0.05 indicates statistical significance, meaning we reject the null hypothesis because the observed result is unlikely by chance.
Question 4: What is 'overfitting' in the context of data mining models?
- A model that performs well on training data but poorly on new data (Correct answer)
- A model with too few parameters to learn patterns
- A model that consumes excessive memory during training
- A model that consistently underpredicts output values
Correct answer: A model that performs well on training data but poorly on new data
Overfitting occurs when a model learns training data too well — including its noise — causing poor generalization to unseen data.
Question 5: Which statistical technique reduces the number of variables while retaining most of the dataset's variance?
- Regression analysis
- Principal Component Analysis (PCA) (Correct answer)
- Naive Bayes classification
- Association rule mining
Correct answer: Principal Component Analysis (PCA)
PCA transforms correlated variables into a smaller set of uncorrelated principal components that capture the maximum variance in the data.
Question 6: What is the purpose of cross-validation in data mining?
- To validate database schema integrity
- To assess how well a model generalizes to independent datasets (Correct answer)
- To cross-reference records across two databases
- To check source data for duplicate entries
Correct answer: To assess how well a model generalizes to independent datasets
Cross-validation partitions data into training and validation sets multiple times to produce a reliable, unbiased estimate of model generalization performance.
DMC DMC Statistical Methods & Data Mining Concepts 1
What does the correlation coefficient measure in data mining?