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# PSY 202 Chapter 3 - Day 2 Notes Psy 202

Marketplace > University of Mississippi > Psychology > Psy 202 > PSY 202 Chapter 3 Day 2 Notes
Stephanie
OleMiss
GPA 4.0

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The notes cover the last few sections of Chapter 3 on Describing Quantitative Data with Summary Statistics.
COURSE
Elementary Statistics
PROF.
Matthew Mervin
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
PSY202, Psychology, Lecture Notes, Statistics
KARMA
25 ?

## Popular in Psychology

This 3 page Class Notes was uploaded by Stephanie on Saturday September 10, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Matthew Mervin in Fall 2016. Since its upload, it has received 35 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.

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Date Created: 09/10/16
PSY 202:  Elementary Statistics Chapter 3:  Describing Quantitative Data with Summary Statistics – Day 2 I. Variability a. Deviations from the mean i. When using this, you’re trying to get the distance between the mean and  something else ii. Basically it is the average distance from the average iii. The Average Deviation 1. Add up deviation scores a. However, they all average to 0 2. You need to get find the absolute value first in order to get rid of  the negative signs so that the scores do not equal 0 3. Divide by the number of deviation scores 4. The average deviation is sensitive to the sample size iv. The Variance 1. Definitional formula a. This squares the deviation scores b. Square the deviation scores and then add them c. After that divide by the number of deviation scores d. Not really helpful because it gives squared scores e. This formula is not recommended  for calculating variance f. 2. Computational Formula a. Allows us to calculate variance without worrying about  rounding errors v. The Standard Deviation  1. This is basically the square root of the variance 2. Definitional Formula a. 3. Computational Formula a. 4. Interpreting the Standard Deviation a. Coefficient of Variation i. ii. The standard deviation is the measure of dispersion  in a distribution and the coefficient of variation is  used to tell if sets of measures are more spread out  or not II. The Variance of a Sample a. Degrees of Freedom i. Definitional Formula 1. ii. You want your sample to approximate the population iii. We use n­1 so that the sample variance equals the population variance iv. For every population parameter estimate you make you lose one degree of  freedom III. The Shape of Distribution a. Symmetry i. Skewness 1. No Skew a. Perfectly symmetrical skewness = 0 b. Mean=Median=Mode 2. Left/Negative Skew a. Mean is lower than median 3. Right/ Positive Skew a. Mean is higher than median 4. Mode is not affected, median is pulled slightly, and mean is pulled  a lot b. Curvature i. Kurtosis 1. Mesokurtic a. Kurtosis = 0 b. Average Central Tendency 2. Leptokurtic a. Kurtosis is greater than 0 b. These distributions are peaked, have narrow “shoulders”,  and have long tails 3. Platykurtic a. Kurtosis is less than 0 b. These distributions are flatter than normal and have short  tails IV. Box and Whisker Plots a. These graph gives the measure of variability and central tendency b. Variability i. These graphs use the interquartile range c. Central Tendency i. They also use the median to cut it in half d. They allow us to compare distributions

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