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PSY 211 Week 1 Notes

by: KhloNotes

PSY 211 Week 1 Notes Psych 2110

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About this Document

These are class notes from week one
Elem Statistics Business
Andre Souza
Class Notes
Statistics, Psychology




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This 2 page Class Notes was uploaded by KhloNotes on Thursday September 1, 2016. The Class Notes belongs to Psych 2110 at University of Alabama - Tuscaloosa taught by Andre Souza in Fall 2016. Since its upload, it has received 49 views. For similar materials see Elem Statistics Business in Psychology (PSYC) at University of Alabama - Tuscaloosa.

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Date Created: 09/01/16
From 211 Elementary Statistical Methods (Psychology) ; Professor Souza       Ideas Stressed in Chapter One:   ­ Everything varies  ­ Heterogeneity is universal   ­ Graphs are good for visualizations, but can be misleading   ­ Statistics is the only science able to quantify and understand variation    ­ Statistics is not math, but a tool for understanding the world around us      What is Statistics?   Science of learning from data and of measuring, controlling, and communicating ​uncertainty; ​      Why?   Statistics holds a fundamental role in scientific research. Notably, it separates fact from fiction  and aids in decision making with uncertainties.     Basic Concepts and Important Vocab    ­ Descriptive Statistics  ​ = procedures for summarizing a group of scores or otherwise  making them more understandable   Ex: The average age of the class is 18.5    ­ Inferential Statistics  ​ = procedures for drawing conclusion based on the scores  collected in a research study but going beyond them; creates an educated guess for  studies where it is impossible to question the entire population    Ex: The average of all college classes is 18.5     ­ Population​ = entire group of people to which a researcher intends the results of a study  to apply  Ex: In a study about which bananas chimps prefer, the researcher studies  responses from the a city zoo’s blue toed chimps. Thus, the chimps are his population.     ­ Sample​ = scores of the particular group of people studied, usually considered to be  representative of the scores in some larger population   Ex:  Five blue toed chimps were selected to partake in the study. Thus, those five  chimps are the sample applied to the larger blue toed chimp population.     ­ Variables​ = characteristic that can have different values; what is being measured   Ex: Height differences in teens    ­ Discrete Variable​ = variable that has specific values and that cannot have values  between these specific values   Explanation: Whole numbers instead of fractions or decimals       ­ Continuous Variable = ​  variable for which, in theory, there are an infinite number of  values between any two values   Ex: Travel time can be 5.5 hours or 5.567 hours.      ­ Categorical Variable (Nominal Variable)​ = variable with values that are categories  (names rather than numbers); listing of categories and count of frequency   Ex: Survey to find most popular cell phone brand     ­ Numeric Variable (Quantitative Variable)  ​ = variable whose values are numbers as  opposed to a nominal variable; can be sorted into a set of intervals divided by a  measurement scale   Ex: David’s response was “two” in a survey calculating average sodas with     dinner, thus his response is placed in the 1­5 slot.    Three Types of Numeric Variables:   1. Equal­interval = variable in which the numbers stand for approximately  equal amounts of what is being measured   2. Ratio Scale = variable measured on a ratio scale if it has an absolute zero  point, meaning that the value of zero on the variable indicates a complete  absence of the variable   3. Rank Order (Ordinal Variable) = numeric variable in which he values are  ranks      ­ Values​ = possible number or category that a score can have; each variable has a score  Ex: 0 to 6’0 feet    ­ Scores​ = the actual value chosen   Ex: 5’0     ­ Outliers​ = extreme observation that falls far from the other data, thus causes  exaggerated estimates, multiple explanations, and instability  Ex: A 36 year old in a class made up of mostly 18 year olds      ­ Frequency Distribution  ​ = pattern of frequencies over the various values  Ex: frequency table, histogram, or frequency polygon      ­ Relative Frequency  ​ = proportion or percentage of observations that fall into that  category   Equation: Frequency/Total = Proportion x 100 = %   Note: All proportions should add up to 1. All percentages should add up to 100%.      


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