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## Week 1 Stat Notes

by: Nate Dickstein

193

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3

# Week 1 Stat Notes PSYCH240

Marketplace > PSYCH240 > Week 1 Stat Notes
Nate Dickstein
UMass
GPA 3.55
Statistics In Psychology
Jeffrey Starns

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Here are the class notes for the first week of class from Tuesday, January 20 until Friday, January 23
COURSE
Statistics In Psychology
PROF.
Jeffrey Starns
TYPE
Class Notes
PAGES
3
WORDS
KARMA
25 ?

## Popular in Department

This 3 page Class Notes was uploaded by Nate Dickstein on Friday January 30, 2015. The Class Notes belongs to PSYCH240 at a university taught by Jeffrey Starns in Fall. Since its upload, it has received 193 views.

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Date Created: 01/30/15
Class 1 12015 About the Class know the course objectives for each section to get a better understanding of what you should know for that section and the tests for the sections bring calculator bring paper to labs up to 12 Sona credits syllabus has details round to 2 decimal points for everything throughout the semester Scientific revolution knowing that your ideas might be wrong and testing them against observations to find out if they are wrong Statistics mathematical tools to organize analyze and interpretation of data we can never be certain about anything we try to quantify how uncertain we are about things ways to estimate probability that something is true estimating some range of values for different things Class 212215 Do you understand the world you live in to understand the world we need information we have all this information but the problem is understanding that information look at large data sets and try to find out what we can attain from them we can39t just look at the raw data because there is too much information this is when we use statistics to mathematically organize all the information 2 types of statistics descriptive statistics describe a data set inferential statistics use data to make general conclusions conclusions that go beyond the data at hand Terminology variables condition characteristic or measure that can take on different values value one of the possible levels that a variable can take score the value of a variable observed for a particular datacollection units distribution collection of scores across a number of datacollection units types of variables you have to know what kind of variable you are considering to know what kind of conclusions you can make numericquantitative variables values of the variables are numbers height nominalcategoricalqualitative variables values of the variable are verbal labels marital status sometimes people use numbers as labels for categorical variables this is still considered a categorical variable since the values don39t actually code a numerical quantity Types of numerical variables equal interval equal sized changes in the variable represent equal sized changes in what is being measured temperature ordinal values of variables only indicate the rank of the score finishing position in a race rank in a class rating scales should probably be considered ordinal but some researchers treat them as equal interval watch out for this continuous variable for which here are no gaps in the values that the variable can take there are in infinite number of possible values between any two scores height speed response time discrete variable for which there are gaps in the values that the variable can take some scores simply never occur number of siblings number of prior marriages ratio variable type of equal interval variable equal interval variable for which a value of zero truly indicates the complete absence of what is being measured pounds of beef consumed in a year bank account balance 0 degrees Fahrenheit does not mean the absence of temperature you can go lower than 0 degrees Fahrenheit or Celsius absolute 0 however is as cold as it can get so the kelvin scale is a ratio scale a variable must have a true zero point to determine what ratio is formed by two of the values is one value half of another value a third of the other value twice the other value on one September day the temperature is 50 degrees Fahrenheit in Albany NY and 100 degrees Fahrenheit in Austin TX the newscaster says it is twice as hot in Austin as it is in Albany This is technically incorrect because 0 degrees Fahrenheit is not really 0 you can go below 0 Section 2 once we have data to evaluate we need to organize the data in ways our minds can understand there are three pieces of info you need to know to have a complete picture of a variable the central tendency variability and shape of the distribution of scores Central tendency the goal is to give a number that describes the typical score for the variable this is called a measure of a central tendency the two most popular measures central tendency are mean and median meanaverage sum of the scores divided by the number of scores MEXN M is mean E is sum X is all of the scores N is the number of scores 2 4 5 O 1 6 E18 N6 M3 SCO re OU LOO39lPN mean deviation 3 1 3 1 3 2 3 3 3 2 3 3 properties of mean 1 the sum of all the deviations from the mean is zero 2 the sum of the squared deviations from the mean is lower than the sum of the squared deviations from any other number Median value at the middle of a sorted set of scores for an odd number of scores sort the scores and take the value of the middle one for an even number of scores sort the scores and take the average of the two middle numbers 2 4 5 6 O 1 order of scores 0 1 2 4 5 6 two middle scores 2 4 median3

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