A ________ is a type of interval statistic for a population parameter.

		confidence interval
		mixed interval
		null Hypothesis
		85% confidence

QUESTION 2

What is a linear regression on each segment of data called?

		Piece-wise linear regression
		multiple-linear regression
		multiple-dimensional
		regression tree

QUESTION 3

Model-based reflex agents ________.

		store the internal state
		are derived from an ideal model
		report back to the original model as they perceive the environment
		are a smaller scale model of larger complex reflex agents

QUESTION 4

What are the limitations of user-based collaborative filtering?

		One problem is that people are fickle; their tastes are always changing.
		The goal state is hard to define.
		Item based filers have fewer limitations and should be used.
		People are fickle, and their tastes change continually.

QUESTION 5

This can be defined as the expected mean of the sum of multiplied difference of random variables with respect to the mean divided by the standard deviation of X and Y.

		Null hypothesis
		Pearson correlation
		False positive
		Triple negative

QUESTION 6

Collaborative filtering systems can be based on customers or products when the item-based system categorizes items. This is based on item-item correlation, whereas customer-item based system performs prediction on items based on the similarity between customers.

		Collaborative filtering systems
		Continuous filtering systems
		Combine systems
		Recommender system

QUESTION 7

This is a simple way to describe data.

		Conclusion statistics
		Data points
		Hypothesis
		Descriptive statistics

QUESTION 8

________ is a test that can tell you which group is significantly different from each other.

		chi-square distribution
		chi-square test
		F-test
		ANOVA

QUESTION 9

A system called a ________ uses two techniques called user-based and item-based collaborative filtering.

		recommender system
		Naive Bayes
		unsupervised learning
		reorder performance

QUESTION 10

Collaborative filtering methods have been applied to many kinds of data, including ________ and ________ data, for mineral exploration, environmental sensing, and financial data.

		logistics and regression
		sensing and monitoring
		stats and model
		search and strategy