Statistic course Cheat Sheet, Cheat Sheet of Statistics

Download Statistic course Cheat Sheet and more Cheat Sheet Statistics in PDF only on Docsity! STATS CHEAT SHEET How do I decide which test to use? What type of data do I have? Continuous Data Only Categorical/Nominal data and Continuous data Do I know the population  and ? Yes Run a Z - Test No No Do I know the population ? Yes Run a Single Sample T-test - Use Sample St. Dev. to predict  Run Correlation and/ or Regression analysis Do I have Independent Samples/Conditions? Yes No Run a Paired Samples T-Test - aka Matched, Dependent, Test-Retest Do I have 2 conditions or More conditions? 2 Conditons Run an Independent Samples T-Test - Look for experimental groups - Clues: Unequal N’s or Random Assignment to one or other group 3 or more Conditions Run an ANOVA - Look for experimental groups - Clues: Unequal N’s or Random Assignment to one or other group Nominal (Frequency) Data Run a Chi Square 1 Z-TESTS In order to run a Z-Test you must be provided with - Population  - Population  Equation: N XZ /  Critical Z-Test values: 1-Tailed 2-Tailed α = .05 1.64 1.96/-1.96 α = .01 2.33 2.58/-2.58 EXAMPLE: 2 INDEPENDENT SAMPLES T-TEST (N’s Equal) Independent Samples T-Tests: - Are used to compare 2 Independent groups - Have experimental groups / conditions - May have unequal N’s - Look for key words such as “Experiment”; “Conditions”; “Random Assignment to one condition or another” Equations:           2 2 2 1 2 1 21 )( n S n S XXt    1 /222   N NxxS N = n1 + n2 df = N - 2 Confidence Intervals:                    2 2 2 1 2 1 21 n S n S tXX crit EXAMPLE: 5 INDEPENDENT SAMPLES T-TEST (N’s Unequal) Equations:           21 2 21 11 )( nn S XXt P 2 )1()1( 21 2 22 2 11 2   nn SnSnS P df = N – 2 Confidence Intervals:                    21 2 21 11 nn StXX Pcrit EXAMPLE: 6 ANOVA ANalysis Of VArience: - Are virtually the same thing as an Independent T-Test except that there are more than 2 conditions - Accounts for possible inflation of the  level by dividing the  level between all possible comparisons (i.e. 3 conditions = /3 .:  of 0.017 per comparison) Equations: Source Sums of Squares (SS) df Mean Square Error (MS) F Between =                          N X n X totk i i 2 1 1 2 k-1 = Btwn Btwn df SS = Within Btwn MS MS Within SSTot - SSBtwn N-k = Within Within df SS OR         N Sn ii 2 Total =                       N XX tottot 22 N-1 Estimating the Magnitude of Experimental Effect: (eta) = TOT WITHINTOT SS SSSS  2 (omega) =    WITHINTOT WITHINBTWN MSSS MSkSS    12 EXAMPLE: 7 EXAMPLE: 10 POWER Power Calculations:  What is the probability of correctly rejecting a false H0?  Power is a function of: o  level o H1 o Sample size o Test statistic used  Where n is unknown, used the power table to estimate  on a given  level. Power for 1 sample Effect Size Noncentrality parameter Estimating Required Sample Size   01 d nd  2        d n  Power for 2 samples (N’s Equal) Effect Size Noncentrality parameter Estimating Required Sample Size   01 d 2 n d  2 2        d n  Power for 2 samples (N’s Unequal) Effect Size *Where  is pooled Harmonic N Noncentrality parameter Estimating Required Sample Size   01 d 21 212 nn nn nh   2 hn d  2 2        d n  Power when  is known Effect Size Noncentrality parameter Estimating Required Sample Size 1d 11  N 1 2 1            n EXAMPLE: 11