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Introduction Introduction • Last Week – Recap • Correlation • How To Draw A Line • Simple Linear Regression • Summary Last Week - Recap Last Week - Recap • Hypotheses • Probability & Significance (p=<0.05) • Chi-square test for two categorical variables • t-test for one categorical and one interval variables • What about a test for two interval variables?... Correlation I Correlation I • Calculates the strength and direction of a linear relationship between two interval variables • e.g. is there a relationship between age and income? • Measured using the Pearson correlation coefficient (r) • Data must be normally distributed (check with a histogram) If not normally distributed use Spearman’s Rank Order If not normally distributed use Spearman’s Rank Order Correlation (rho) - consult Pallant (2005:297) Correlation (rho) - consult Pallant (2005:297) Correlation II Correlation II • ‘r’ can take any value from +1 to -1 • +/- indicates whether the relationship is positive or negative • +1 or -1 is a perfect linear relationship, but usually it is not this clear cut • Rule of thumb: –+/- 0.7 = a strong linear relationship Alternatively: Alternatively: - - +/- 0.10 to 0.29 = weak –+/- 0.5 = a good linear relationship +/- 0.10 to 0.29 = weak - - +/- 0.30 to 0.49 = medium –+/- 0.3 = a linear relationship +/- 0.30 to 0.49 = medium - - +/- 0.50 to 1.00 - strong –Below +/- 0.3 = weak linear relationship +/- 0.50 to 1.00 - strong –0 = no linear relationship Correlation III Correlation III No relationship Negative Positive No Negative Positive No Negative Positive relationship Relationship Relationship Relationship relationship Relationship Relationship Relationship Formulate hypotheses and use scatter plots! Formulate hypotheses and use scatter plots!
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