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## PSY 1100 Notes for October 14th, 2016

by: Kelsey Jackson

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# PSY 1100 Notes for October 14th, 2016 PSY 1110

Marketplace > Ohio University > Psychology > PSY 1110 > PSY 1100 Notes for October 14th 2016
Kelsey Jackson
Ohio

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Sorry these are late, but here are the notes from Friday! Hope you all enjoy and happy studying!
COURSE
Elementary Statistical Reasoning
PROF.
Susan Tice-Alicke
TYPE
Class Notes
PAGES
4
WORDS
CONCEPTS
Statistics, scatterplot, pearsons, coefficent, Linear, Relationship
KARMA
25 ?

## Popular in Psychology

This 4 page Class Notes was uploaded by Kelsey Jackson on Sunday October 16, 2016. The Class Notes belongs to PSY 1110 at Ohio University taught by Susan Tice-Alicke in Fall 2016. Since its upload, it has received 4 views. For similar materials see Elementary Statistical Reasoning in Psychology at Ohio University.

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Date Created: 10/16/16
Kelsey A. Jackson PSY 1110 Notes Notes from Friday, October 14 . th 1. Types of Relationships in Statistical Data a. Positive Relationship i. The variables are changing in the same direction. 1. The fewer greeting cards you give; the less greeting cards you tend to receive. 2. The more time you spend studying, the higher your grades tend to  be. b. Negative Relationship i. The variables are changing in opposite directions. 1. The more time you spend partying, the lower your grades tend to  be.  c. No Relationship i. Sometimes data simply has no relation to each other. 2. Correlations a. Correlations establish the degree of the relationship. b. Pearson’s Correlation Coefficient i. A number between ­1 and 1 that describes the linear relationship between  pairs of measurement variables. ii. Two Things to keep in mind while calculating Pearson’s coefficient (r) 1. The sign (positive/negative) of r indicates the type of relationship. 2. The value of r without regard to the sign indicates the strength of  the relationship a. If r1= ­5 and r2= 3, 5 is stronger because it is the number  that is highest. (don’t pay attention to the signs) 3. If r = 1 or ­1, that means the relationship is a perfect linear  relationship 4. If r = 0, there is no relationship iii. How to calculate Pearson’s 1. statisticshowto.com 2. (This looks scary, but it’s very simple if you take your time during  the calculation) 3. Here is our sample data set: X Y 13 11 9 14 7 9 5 5 1 3 Ex:35 Ex: 42 4. Next, calculate XY, EXY, X^2, EX^2, Y^2, and EY^2 (we already know how to do all of these from the first chapter) XY X^2 Y^2 143 169 121 126 81 196 63 49 81 25 25 25 3 1 9 EXY: 360 EX^2: 325 EY^2: 432 R = 360 – (35)(42)                     [325 – (35)^2][432 – (42)^2]      5      5    R =  360 – 1470                              5 [325 – 1225/5][432 – 1764/5] R = 360 – 294 [325­245][432­352.8] R= 360 – 294 [80][79.2] R = 66 [6336] R = 66      79.6 R = .83 c. Scatterplot i. A graph of the individual data points from a set of x, y pairs. ii. A scatterplot uses two measurement variables. iii. Wikipedia.com

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