Exam 2: Reasoning With Data

A business analyst uses data mining to establish a negative correlation between customer complaints and customer relations training hours for employees. She concludes increasing training will reduce customer complaints. What is a key weakness in this approach as stated?

Which of the following conclusions is an example of inductive reasoning?

Which of the following is an example of flawed inductive reasoning due to predictable-world bias?

I give you a coin, and you flip it 100 times. Let X = the number of heads observed in those 100 flips. You build the following line of reasoning: Steps 1) You assume each flip is an independent, random outcome. 2) You assume the coin is fair. 3) Using your statistics book, you conclude that X is distributed binomial with mean 50 and variance 25. 4) Based on this distribution, you conclude that you would observe X < 41 or X > 59 approximately 5% of the time. 5) You observe X = 37 and conclude that the probability that X < 41 or X > 59 is not 5%. 6) Because the probability that X < 41 or X > 59 is not 5%, you conclude the coin is not fair. Which of these steps is part of inductive reasoning?

In attempting to determine the average starting salary of your university's graduates, you conduct a survey of a recent set of graduates given to you from the gift/donation department of your university. What is wrong with this logic?

Reasoning that goes from the specific (e.g., a data sample) to the general (e.g. the population) is known as:

Suppose a salesperson claims Brand A is more effective at cleaning dishes than Brand B. If you assume this claim to be true, what is an empirically testable conclusion that would follow from this assumption?

In the deductive reasoning behind the claim that "If firms sell homogenous products, then prices should fall to marginal costs" the role of "firms sell homogenous products" is:

Which of the following are empirically testable conclusions?

Degrees of support based off opinion and ultimately lacking any statistical foundation are often termed:

Which of the following is not an empirically testable conclusion?

For the purposes of reaching sound conclusions with data analysis it is critical that:

If you are intending to refute conclusions drawn via clear deductive reasoning, what must you disagree with?

I give you a coin, and you flip it 100 times. Let X = the number of heads observed in those 100 flips. You build the following line of reasoning: Steps 1) You assume each flip is an independent, random outcome. 2) You assume the coin is fair. 3) Using your statistics book, you conclude that X is distributed binomial with mean 50 and variance 25. 4) Based on this distribution, you conclude that you would observe X < 41 or X > 59 approximately 5% of the time. 5) You observe X = 37 and conclude that the probability that X < 41 or X > 59 is not 5%. 6) Because the probability that X < 41 or X > 59 is not 5%, you conclude the coin is not fair. Which of these steps is part of deductive reasoning?

The statement, "The Golden State Warriors are the most watched basketball team in the Oakland TV market, therefore they are the most watched NBA team" is an example of what type of reasoning?

Using the following question in a survey to elicit the effectiveness of an advertising campaign would suffer from what sort of bias: "Was the primary reason for your purchase of this product the advertising campaign?"

Reasoning that goes from the general to the specific or "top-down" logic is known as:

In the deductive reasoning behind the claim that "If firms can price discriminate, then prices will be higher in markets with lower price elasticities of demand" the role of "prices will be higher in markets with lower price elasticities of demand" is:

All of your friends listen to hard rock music more than any other genre. Consequently, you conclude that hard rock music is the most popular genre in the United States. What is the flaw in your reasoning?

A description of the rules and/or steps behind the reasoning process is known as: