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PSYC 2300 week 2

by: Mary Kay

PSYC 2300 week 2 PSYC2300

Mary Kay
GPA 4.0

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Introduction to Statistics PSYC 2300 week 2 notes
Introduction to Statistics
Hipp, Daniel
Class Notes
25 ?




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Popular in Psychology (PSYC)

This 3 page Class Notes was uploaded by Mary Kay on Thursday September 22, 2016. The Class Notes belongs to PSYC2300 at University of Denver taught by Hipp, Daniel in Fall 2016. Since its upload, it has received 10 views. For similar materials see Introduction to Statistics in Psychology (PSYC) at University of Denver.

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Date Created: 09/22/16
Central Tendency  Average value: mean value • Most Frequent score • Mathematical average of all scores • Middle position • Sample mean= sum of X / N • Sample mean  = xx • Population mean =  μ The mode • The score that occurs most frequently in a sample.  • Find it by construct a frequency distribution. • use it when you have nominal data • isn’t so great ◦ Ignores all data except the most frequently occurring score. ◦ Can have bimodal or multimodal data The Median • The score at the 50th percentile: • exactly have of the scores are lower than the median and exactly half of the scores are  higher.  Percentile: the point in the distribution below with __th percent of the scores fall, e.g. the  25th Which measure of Central Tendency should you use? Use the mean unless • you have nominal data, use the mode. e.g., to find the “Average “ major, average eye  color... ◦ The modal response was to do better in school; 20% of all students reported that this was  their resolution.  • If you have a skewed distribution, use the median.  • use the median for ordinal data.  Normal distribution. 3 equal Median larger in Negatively skewed, on the right Different ways to measure variability in your data set. • Range... • Find Sample mean • Deviation from the mean. ◦ The size of the deviation indicates how far a particular score is from the mean. ◦ The higher the score, the more deviation from the mean.  ◦ In a normal distribution, the greater the deviation, the less frequent the score.  variance: (S^2) the average of the squared deviations around the mean. • ((x­xx)^2)/N   Standard Deviation: (S)    The square root of the average squared deviation around the   mean .  • Stander deviation sd is the average amount that the scores in your sample deviate from he mean.  • It will always be a positive number.  • Larger number, larger variable in the data.  Unbiased Estimator (a hat above S) • variance: ((x­xx)^2)/(N­1) • Because samples tend to have less variability than the parent population Z scores and Z distributions • You can transform a score into a z­score to conveys information about relative position  and relative frequency of that raw score.  • “Standardized” the score by taking into account a mean and a standard deviation ◦ Makes scores from different distributions comparable • Most useful with a normal distribution.  why standardize your scores? • to have information about position in a distribution. • To compare • z=(x­xx)/Standard Deviation  • z higher, did better on the exam.  • It is Measured in terms of the number of standard deviations from the mean. • Generally range from ­3.0 to 3.0 ◦ Why? 99.7% of scores fall with in this range. • A z­score of 0 means that the score is at the mean. • A negative Z score means that a score is below the mean • e.g. Z=1.0: Raw score is 1 standard deviation above the mean.  • e.g. Z=­0.5: Raw score is 1/2 standard deviation below the mean. Convert Z back to raw score. • Sample X=z*stander deviation +  xx Inferential stats: • Significance testing or hypothesis testing • Z­test • t­tests (Different types) • ANOVA (different types) • Chi­square How most experiments works Simple Probability • Number between 0 and 1 (a proportion) • Probability of event A is written as “P(A)" • If P(A)= 0.1 it indicates with certainty that event A will happened.  Two types of probability Distributions • Empirical probability distribution ◦ Based on an actual frequency distribution/frequency data • Theoretical probability distribution: • Based on a theoretical normal distribution • Eg. your z­table Standard error of the mean


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