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PSYC 515 Module 5: Week 5 Quiz: Non-parametric Tests Concepts 2023 worth 50 points. $10.99   Add to cart

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PSYC 515 Module 5: Week 5 Quiz: Non-parametric Tests Concepts 2023 worth 50 points.

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PSYC 515 Module 5: Week 5 Quiz: Non-parametric Tests Concepts 2023 worth 50 points. Quiz: Non-parametric Tests Concepts • Due Jul 30 at 11:59pm • Points 50 • Questions 26 • Time Limit 90 Minutes Instructions The quiz: • Covers the Learn ...

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  • September 20, 2023
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PSYC 515 Module 5: Week 5 Quiz: Non-parametric Tests Concepts
2023 worth 50 points.


Quiz: Non-parametric Tests Concepts
• Due Jul 30 at 11:59pm




• Points 50




• Questions 26




• Time Limit 90 Minutes

Instructions
The quiz:

• Covers the Learn material from Module 5: Week 5.
• Contains 25 multiple-choice questions.
• Is limited to 1 hour and 30 minutes.
• Allows 1 attempt.
• Is worth 50 points.

This quiz is designed to assess understanding of content from this week’s topics covered in the
Learn section (non-cumulative) and will not require use of SPSS. You have 90 minutes to
complete these 25 questions. This allows for judicious use of time to refer to assigned readings
and presentations (open book / open notes) but should be completed with no other forms of
collaboration.
Please note: that the first question asks you to indicate your agreement to abide by LUO's
Online Honor Code. Answering this question is required.
Submit this assignment by 11:59 p.m. (ET) on Sunday of Module 5: Week 5.

Attempt History

, Attempt Time Score

LATEST Attempt 1 21 minutes 50 out of 50
Correct answers are hidden.
Score for this quiz: 50 out of 50
Submitted Jul 18 at 7:13am
This attempt took 21 minutes.

Question 1
pts
I have read and understand the Academic Honor policy as outlined in
the Online Honor Code for Liberty University Online. I agree to abide by this
policy and that the work submitted herein is my own.


Yes



No



Question 2
pts



Wilcoxon rank-sum test is to as Wilcoxon matched-pairs signed-ranks T test is to .




ordinal data; nominal data



interval data, ordinal data



between-subject design; within-subjects design



within-subjects design; between-subjects design

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