PSY 211 Psych 2110
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This 3 page Study Guide was uploaded by KhloNotes on Monday September 5, 2016. The Study Guide belongs to Psych 2110 at University of Alabama - Tuscaloosa taught by Andre Souza in Fall 2016. Since its upload, it has received 19 views. For similar materials see Elem Statistics Business in Psychology (PSYC) at University of Alabama - Tuscaloosa.
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Date Created: 09/05/16
From 211 Elementary Statistical Methods (Psychology) ; Professor Souza ; Aron: Statistics for Psychology, 6e Vocab to Know: ○ Statistics = Science of learning from data and of measuring, controlling, and communicating u ncertainty ○ Values = possible number or category that a score can have; each variable has a score ○ Variables = characteristic that can have different values; what is being measured ○ Scores = the actual value chosen ○ Outliers = extreme observation that falls far from the other data, thus causes exaggerated estimates, multiple explanations, and instability ○ Population = entire group of people to which a researcher intends the results of a study to apply ○ Sample = scores of the particular group of people studied, usually considered to be representative of the scores in some larger population ○ Central Tendency = the typical observation for a given variable 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 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 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 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%. Formulas to Know: ○ Arithmetic Mean = Formula: x̄ = (Σx)/n; Sensitive to outliers ○ Properties of Mean = Formula: Σ(xi x̄ ) = 0; Best estimate for the value of a group of numbers n ○ Geometric Mean = F ormula: x = Πx ; Good for numbers that are not independent of each other ○ Median = Middle value in the dataset; Formula: Arrange values low to high and cross outside numbers off until you reach the middle term OR take number of terms and add by 1 then divide by 2, Not sensitive to outliers ○ Mode = frequency; Can be used for categorical and numerical variables ○ Range = F ormula: Z (xi x) / n ̄ ○ Residuals = Σ | xi − x ̄| or Σ ( xi x) to get rid of negatives Variance 2 Formula: Σ (xi − x)̄ n Computational Formula: Σ x − (Σx) ÷ n Measured in squared units Degrees of Freedom “n1” Allows us to make inference w/ room for error 2 est requires Σ (xi − x)̄ for variance n − 1 Squared Deviation Σ (xi − x)̄ Formula: √ n − 1 Σ x − (Σx) ÷ n Computational Formula: √
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