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PSY 211 Week 2

by: KhloNotes

PSY 211 Week 2 Psych 2110


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About this Document

These notes include formulas and important information for the test on chapters 1-2.
Elem Statistics Business
Andre Souza
Class Notes
Statistics, Psychology, standard deviation, variance
25 ?




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This 3 page Class Notes was uploaded by KhloNotes on Monday September 5, 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 9 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
  Key:​ ​Bold/Italic = ​important information, ​Highlighted ​= vital for test       From 211 Elementary Statistical Methods (Psychology) ; Professor Souza     Ideas Stressed in Chapter Two:   ­ Measurements often cluster around certain values  ­ Data Frames: An object w/ rows and columns used for data collection and organizing  data  ­ Rows contain different observations   ­ Columns contain the values of different variables   ­ Values can be quantitative (numbers) or text (qualitative)   ­ Sum of Squares ̄ or  Σ (xi ­ x)  (Most important measure in Statistics)     Central Tendency  ­ Shows the typical observation for a given variable   ­ Different samples will be different because everything varies, but sample statistics also  cluster around central values     Arithmetic Mean  ­ Most straightforward   ­ Represented by a letter w/ a bar on top:   x ̄  ­ Sum of all data points divided by the # of data points n   ̄ ­ Formula: x = (Σx)/n   ­ Answers: If all the data points had the same value, what would that value be?   ­ Can replace every number in the data set and still equal sum   ­ Only appropriate for quantitative variables   ­ Sensitive to outliers     Properties of Mean   ­ Usually pulled in the direction of the outlier   ­ For binary data, the mean equals the proportion of observatory equalling 1   ­ Arithmetic mean is the only single number for which the residuals (as defined as the  distance between each data point and the mean) sum to zero  ̄ ­ Formula: Σ(xi ­ x ) = 0    ­ Best estimate for the value of a group of numbers     Geometric Mean   ­ For process that change multiplicity rather than additively   ­ Uses product ​ (Πx) ​instead of sum ​(Σx)  n ​ ̄ ­ Formula : x =  Πx     Key:​ ​Bold/Italic = ​important information, ​Highlighted ​= vital for test  ­ Good for numbers that are not independent of each other     Median   ­ Middle value in the dataset (number separating the higher and lower halves of a  distribution)   ­ 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  ­ For lists where the middle term is a decimal or even numbered, add the middle two terms  and divide by 2   ­ Not sensitive to outliers    Median Properties   ­ Appropriate for quantitative and  and ordinal variables  ­ Requires ordered data    ­ Not affected by outliers = appropriate for skewed distributions     Mode   ­ Represents the most common outcome (frequency)   ­ Can be used for categorical and numerical variables    ­ Note: Be aware of which variable the question is requesting      Measures of Variability (VERY IMPORTANT)   ­ What is variation?   ­ The greater the variability in your data, the greater your uncertainty     Range   ­ Distance covered by the scores in a distribution, from smallest and largest   ­ Distance between between the minimum and maximum values  ̄ ­ Distance between each data point and the mean ( ​ xi­ x) s called residual, deviation, or  error   ­ Then, take distance ­ mean and add them all up before dividing by n   ̄ ­ Formula: Z (xi ­ x) / n   ­ The answer should always be zero   ­ Useful for finding the mean and seeing how far from the mean each data point               Key:​ ​Bold/Italic = ​important information, ​Highlighted ​= vital for test      Residuals    ­ Sum will be zero or close to zero due to rounding errors   ­ Negatives cancel out positives   ​ ​ ̄ ​ ­  Σ |  xi − x ̄|  or  ​ Σ ​(xi ­ x)  to get rid of negatives   ­ Represents the total variability in a data set   Characteristics:   ­ Always the squared of the original unit   ­ The more numbers, the bigger the Sum of Squares (SS)    ­ Avoidable if you divide the SS by the sample size, resulting in a Average Sum of  Squares or the Variance        Variance   Σ (xi − x)̄  ­ Formula:  n   ­ Computational Formula:  ­ Measured in squared units     Degrees of Freedom   ­ “n­1”   ­ Allows us to make inference w2 room for error   Σ (xi − x)̄  ​ ­  ​ est requires  n − 1 f    ​Squared Deviation   2 ​ ­ Formula:   Σ (xi − x)̄  √ n − 1 ​ ­ Computational Formula:                       


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