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Statistical Analysis Regression & Correlation Psyc 250 Winter, 2013

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Review: Types of Variables & Steps in Analysis

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Variables & Statistical Tests Variable TypeExampleCommon Stat Method Nominal by nominal Blood type by gender Chi-square Scale by nominalGPA by gender GPA by major T-test Analysis of Variance Scale by scaleWeight by height GPA by SAT Regression Correlation

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Evaluating an hypothesis Step 1: What is the relationship in the sample? Step 2: How confidently can one generalize from the sample to the universe from which it comes? p <.05

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Evaluating an hypothesis Relationship in Sample Statistical Significance 2 nom. vars.Cross-tab / contingency table “p value” from Chi Square Scale dep. & 2-cat indep. Means for each category “p value” from t- test Scale dep. & 3+ cat indep. Means for each category “p value” from ANOVA f ratio 2 scale vars.Regression line Correlation r & r 2 “p value” from reg or correlation

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Evaluating an hypothesis Relationship in Sample Statistical Significance 2 nom. vars.Cross-tab / contingency table “p value” from Chi Square Scale dep. & 2-cat indep. Means for each category “p value” from t- test Scale dep. & 3+ cat indep. Means for each category “p value” from ANOVA 2 scale vars.Regression line Correlation r & r 2 “p value” from reg or correlation

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Relationships between Scale Variables Regression Correlation

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Regression Amount that a dependent variable increases (or decreases) for each unit increase in an independent variable. Expressed as equation for a line – y = m(x) + b – the “regression line” Interpret by slope of the line: m (Or: interpret by “odds ratio” in “logistic regression”)

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Correlation Strength of association of scale measures r = -1 to 0 to perfect positive correlation -1 perfect negative correlation 0 no correlation Interpret r in terms of variance

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Mean & Variance

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Example: Weight & Height Survey of Class n = 42 Height Mother’s height Mother’s education SAT Estimate IQ Well-being (7 pt. Likert) Weight Father’s education Family income G.P.A. Health (7 pt. Likert)

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Frequency Table for:HEIGHT Valid Cum Value Label Value Frequency Percent Percent Percent Total Valid cases 42 Missing cases 0

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Frequency Table for:HEIGHT Valid Cum Value Label Value Frequency Percent Percent Percent Total Valid cases 42 Missing cases 0 Descriptive Statistics for:HEIGHT Valid Variable Mean Std Dev Variance Range Minimum Maximum N HEIGHT mean

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Variance x i - Mean ) 2 Variance = s 2 = N - 1 Standard Deviation = s = variance

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Frequency Table for:WEIGHT Valid Cum Value Label Value Frequency Percent Percent Percent Total Valid cases 42 Missing cases 0 Descriptive Statistics for:WEIGHT Valid Variable Mean Std Dev Variance Range Minimum Maximum N WEIGHT mean

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Relationship of weight & height: Regression Analysis

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“Least Squares” Regression Line Dependent = ( B ) (Independent) + constant weight = ( B ) ( height ) + constant

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Regression line

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Regression:WEIGHTonHEIGHT Multiple R R Square Adjusted R Square Standard Error Analysis of Variance DF Sum of Squares Mean Square Regression Residual F = Signif F = Variables in the Equation Variable B SE B Beta T Sig T HEIGHT (Constant) [ Equation:Weight = 3.3 ( height ) - 73 ]

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Regression line W = 3.3 H - 73

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Strength of Relationship “Goodness of Fit”: Correlation How well does the regression line “fit” the data?

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Correlation Strength of association of scale measures r = -1 to 0 to perfect positive correlation -1 perfect negative correlation 0 no correlation Interpret r in terms of variance

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Frequency Table for:WEIGHT Valid Cum Value Label Value Frequency Percent Percent Percent Total Valid cases 42 Missing cases 0 Descriptive Statistics for:WEIGHT Valid Variable Mean Std Dev Variance Range Minimum Maximum N WEIGHT mean

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Variance = 454

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Regression line mean

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Correlation: “Goodness of Fit” Variance (average sum of squared distances from mean) = 454 “Least squares” (average sum of squared distances from regression line) = 295

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l.s. = 295 Regression line mean S 2 = 454

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Correlation: “Goodness of Fit” How much is variance reduced by calculating from regression line? 454 – 295 = / 454 =.35 Variance is reduced 35% by calculating “least squares” from regression line r 2 =.35

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r 2 = % of variance in WEIGHT “explained” by HEIGHT Correlation coefficient = r

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Correlation:HEIGHTwith WEIGHT HEIGHT WEIGHT HEIGHT ( 42) ( 42) P=. P=.000 WEIGHT ( 42) ( 42) P=.000 P=.

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r =.59 r 2 =.35 HEIGHT “explains” 35% of variance in WEIGHT

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Sentence & G.P.A. Regression: form of relationship Correlation: strength of relationship p value: statistical significance

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Legal Attitudes Study: 1.Relationship of sentence length to G.P.A.? 2.Relationship of sentence length to Liberal-Conservative views

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G. P. A.

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Length of Sentence (simulated data)

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Scatterplot: Sentence on G.P.A.

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Regression Coefficients Sentence = -3.5 G.P.A. + 18

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Sent = -3.5 GPA + 18 “Least Squares” Regression Line

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Correlation: Sentence & G.P.A.

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Statistical Significance p =.31 Regression: Correlation

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Interpreting Correlations r = -.22 r 2 =.05p =.31 G.P.A. “explains” 5% of the variance in length of sentence

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Write Results “A regression analysis finds that each higher unit of GPA is associated with a 3.5 month decrease in sentence length, but this correlation was low (r = -.22) and not statistically significant (p =.31).”

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Multiple Regression Problem: relationship of weight and calorie consumption Both weight and calorie consumption related to height Need to “control for” height or assess relative effects of height and calorie consumption

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Regression line mean Multiple Regression

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Regression line mean Multiple Regression Residuals

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Multiple Regression Regress weight residuals (dependent variable) on caloric intake (independent variable) Statistically “controls” for height: removes effect or “confound” of height. How much variance in weight does caloric intake account for over and above height?

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Multiple Regression How much variance in dependent measure (weight, length of sentence) do all independent variables combined account for? multiple R 2 What is the best “model” for predicting the dependent variable?

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Malamuth: Sexual Aggression Dependent Var: self-report aggression Indep / Predictor Vars: –Dominance –Hostility toward women –Acceptance of violence toward women –Psychoticism –Sexual Experience + interaction effects

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Malamuth: multiple regressions Without “tumescence” index: multiple R =.55w/ interactions R =.67 multiple R 2 =.30 R 2 =.45 With “tumescence” index: multiple R =.62w/ interactions R =.87 multiple R 2 =.38 R 2 =.75

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