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10 Cards in this Set
- Front
- Back
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Measuring the strength of linear correlation: r, r^2, range, 0 |
r = Correlation Coefficient r^2 = Coefficient of determination - it expresses the strength of the relationship between x and y variables Range is from -1 to 1 If R is 0, there is not correlation |
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Use Scatterplots to show relationships between |
Numerical Variables |
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Three Types of Correlation Coefficients |
1. Pearson Product Moment 2. Spearman's Rank 3. Kendall's Tau |
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3 Goals of linear regression and correlation |
- See if there is a relationship between the two variables - Find line of best fit - Estimate strength of relationship between variables
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Homoscedasticity vs. Heteroscedasticity |
Homo: constant variance around the regression line Hetero: uneven variance |
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Correlation vs. Regression |
C: 2 random measurement variables R: Only dependent variable is random C: opening a case study R: further investigating a case study C: Don't have to think about cause and effect |
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Paired T-test (Seen more in Bio than the T-test) |
Use the paired t-test when you have one measurement variable and two nominal variables -Works great with sets of data that multiple pairs of observation on the same subject over time - Compares the mean difference between two samples |
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Autocorrelation |
Refers to a correlation between a series of numbers arranged over time. Ex: If today is rainy, it is more likely for tomorrow to be rainy. |
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T-test |
Method of testing the mean of a normally distributed population when the population's standard deviation is unknown
- Small sample - Use when you have one measurement variable and a theoretical expectation of what the mean should be like |
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If t-stat is greater than t-crit... If t-stat is less than t-crit... |
1. Null hypothesis is rejected 2. Null hypothesis not rejected |
