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QNT 275 Final Exam Answer Guide


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About the QNT/275 Final Exam

Lost in a sea of complex statistical jargon? Discrete random variables, regression analyses, interferential studies, serial correlation, sample bias, and so on... The QNT 275 final exam answer guide covers all of these problems and provides detailed explanations to help clarify these highly complicated topics. We even show our calculations for each problem, so you know its not just some random guess by someone on the internet. With this study guide, you don't just get correct answers, you get a full understanding of each question.  

What's Included? 

  • 30 Questions and Answers
  • Full explanations with external links
  • Updated March 16th 2017
  • Additional Support from ACCNerd as Needed

UPDATE: New Answers for 2017-18

A quantitative variable is the only type of variable that can:

assume numeric values for which arithmetic operations make sense

Explanation: Quantitative values are numeric, while qualitative are labels, therefore, they are the only type that can perform arithmetic operations.

A qualitative variable is the only type of variable that:

can assume numerical values

Explanation: Qualitative variables are labeled responses, such as opinions on a certain topic taken via a survey.

The following table gives the cumulative frequency distribution of the commuting time (in minutes) from home to work for a sample of 400 persons selected from a city.

Time (minutes) f

  • 0 to less than 10 64
  • 0 to less than 20 153
  • 0 to less than 30 217
  • 0 to less than 40 283
  • 0 to less than 50 352
  • 0 to less than 60 400

The sample size is:


The percentage of persons who commute for less than 30 minutes, rounded to two decimal places, is:

Take the cumulative total values less than 30 minutes 217, then divide by 400. Multiply by 100 for the percentage.


+++ More on the Full Study Guide

Preview of Answers

What is the name of the variable that’s used to predict another variable?


​Explanation: An explanatory variable, sometimes called a predictor, is a special type of independent variable that is used to measure another variable. An example of this an action is a study about the effectiveness of a body building supplement. The explanatory variable would be the dosage of the supplement, while the response variable would be the change in muscle growth.

Reference: http://statistics.about.com/od/Glossary/a/What-Are-The-Difference-Between-Explanatory-And-Response-Variables.htm

The salaries of teachers in a particular school district are normally distributed with a mean of $50,000 and a standard deviation of $2,500. Due to budget limitations, it has been decided that the teachers who are in the top 2.5% of the salaries would not get a raise. What is the salary level that divides the teachers into one group that gets a raise and one that doesn’t?


Explanation: In this example, we can find the top 2.5% of salaries by multiplying the standard deviation of 2,500 by the 1.96, then adding it to the mean. It results in the calculation of 50,000 + (2,500 * 1.96) = 54,900.

A hedge fund returns on average 26% per year with a standard deviation of 12%. Using the empirical rule, approximate the probability the fund returns over 50% next year.


Explanation: Solving for the empirical rule results in the following values: 68% of values are between 14 to 38, 95% of Values are between 2 to 50, 97.7% of values are between -10 to 62. That leaves a 2.5% probability that the fund will return over 50% next year.

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