As SD decreases the strength of evidence increases Pairing and Random

As sd decreases the strength of evidence increases

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As SD decreases, the strength of evidence increases - Pairing and Random Assignment - Makes it easier to detect statistical significance - Review* - Pairing and Observational Study - Use pre and post test - Paired T-test, confidence intervals, with degrees of freedom - Correlation coefficient(r)
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- Correlation near 0 = weak linear assoc - Signs give us direction of correlation - Sensitive to influential observations: a single extreme point can change the correlation by a lot - Correlation does not equal causation - Regression line: y= a + bx, a is the intercept and b is slope - Finding regression line: residual = observed - predicted - Slope = r(S y /S x ), standard deviations of x and y Just know the scenarios from exam 1: no formulas 1. Testing a single proportion: 2. Testing single mean: a) is the avg body temp higher than 98.6 3. Comparing two proportions: 2 categorical variables: is there an association between sex and where the person gets the flu 4. Comparing 2 means(independent): is there an association between gender and GPA 5. Testing a mean difference(paired data): Will rats go through a maze faster after given caffeine compared to the same rats with w/o caffeine Chapter 4: • Explanatory vs Response variables: - Explanatory variable: (independent, predictor, x-variable), variable we think is “explaining” the change in the response variable - Response variable: (dependent, outcome, y-variable), variable being impacted or changed by the explanatory variable • Observational studies: - Observe individuals and measure variables of interest - Ex: college students who spend more time on facebook tend to have lower GPA -
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