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## Exam #2 Study guide

by: Kelsey Jackson

28

1

4

# Exam #2 Study guide PSY 1110

Marketplace > Ohio University > Psychology > PSY 1110 > Exam 2 Study guide
Kelsey Jackson
Ohio

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Here's all the most important concepts from chapter 2, 3, and 6. These are the topics that will probably show up most on the exam. Good luck to everyone!
COURSE
Elementary Statistical Reasoning
PROF.
Susan Tice-Alicke
TYPE
Study Guide
PAGES
4
WORDS
CONCEPTS
Math, Statistics, psy1110, elementary statistics
KARMA
50 ?

## Popular in Psychology

This 4 page Study Guide was uploaded by Kelsey Jackson on Wednesday October 5, 2016. The Study Guide belongs to PSY 1110 at Ohio University taught by Susan Tice-Alicke in Fall 2016. Since its upload, it has received 28 views. For similar materials see Elementary Statistical Reasoning in Psychology at Ohio University.

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Date Created: 10/05/16
Kelsey A. Jackson Study Guide for Exam #2 (Wednesday, October 12 ) th Exam Study Guide 1. Measures of Central Tendency a. Mean (bar x) i. Best measure of CT when the distribution is symmetrical. ii. The arithmetic average of all the scores in the distribution iii. How to compute the mean: 1. Add up all the scores (EX) 2. Divide by n (the number of scores) iv. Mu is the population mean, and is found the same way as any other mean. b. Median i. The exact midpoint of any distribution. ii. Must order the scores according to magnitude. iii. If two midpoints, the average of the two scores is the median. c. Mode i. Most frequently occurring number in a set of data. ii. If the data is nominal, this is the only measure of CT available. d. Range i. A measurement of the width of the entire distribution ii. How to compute the range 1. Highest score – lowest score = range e. Interquartile Range (IQR) i. A range of the middle 50 percent of the distribution. ii. How to compute the IQR: 1. Arrange the scores according to magnitude 2. Find the median of the scores, take out the median, and group the  scores into equal groups. 3.  1 2 3 4 5 6 7 8 9 (1 2 3 4) 5 (6 7 8 9) 4. find the median of the upper and lower halves 5. subtract these two scores  f. Variance i. The average of the squared deviations of the scores around the mean ii. Formula: 1. iii. Example 1. X X^2 14 196 10 100 10 100 9 81 8 64 8 64 7 49 6 36 6 36 4 16 2 4 2 4 EX = 86 EX= 750 750­(86) ^2/12 (750­(7396)/12)/12 (750­616.33)/12 (133.67)/12 variance = 11.14 iv. Standard Deviation 1. The square root of the variance 2. Example a. 11.14 = 3.34 b. standard deviation = 3.34 2. Normal Distribution a. Z­score i. Formula 1. (X­Mu)/standard deviation 2. if the z­score is positive, the direction is right 3. if the z­score is negative, the direction is left ii. How to compute the percentage of scores 1. Sketch the graph 2. Compute the z­score 3. Go to the normal distribution table and find the proportion 4. Multiply by 100 3. Simple Frequency Distribution a. How to graph using a histogram i. List the range and the frequency of each number X f 7 5 8 2 9 3 10 5 11 2 12 3 ii. Arrange X scores on the X axis, and frequency on the Y axis iii. Make sure the bars are touching 4. Grouped Frequency Distribution a. Real Limits i. The points falling one half a unit above and below a particular number. ii. EX:  1. For the number 10, the lower real limit would be 9.5, and the upper real limit would be 10.5 b. Midpoint i. The halfway point within the class interval ii. If the class interval is 10­20, the midpoint would be 15 c. Frequency i. How many times a score appears in a data set d. Cumulative Frequency i. The frequency of all scores at or below a particular score ii. The running total of all frequencies iii. The last CF should equal the sum of your frequencies. e. Relative Percent i. The percentage of time the raw score occurs in the sample or the  population. ii. Frequency/n * 100 = relative percent f. Cumulative Percentage i. Refers to the percentage of cases at or below a particular score ii. The same as cumulative frequency, you just add up your percentages as  you go. iii. The last percentage should equal 100. 5. Extra things to note a. Positively skewed graphs have lots of LOW scores, and few high scores. b. Negatively skewed graphs have lots of HIGH scores, and few low scores. c. The measures of variability are range, IQR, variants, and standard deviance. d. The Characteristics of a good statistical picture i. The Message of the picture should stand out clearly from the background ii. There should be a purpose and a title iii. The source of the data should be given iv. The graph should be clearly labeled v. Minimize extras on the visible display 6. Good Luck on the Exam Guys!

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