Stats/Inferential
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Power and Effective sizeStats/Inferential 2020. 2. 5. 18:29
1. Overview 2. Description 2.1 Power ($1-\beta$) The probability of correctly rejecting a false null hypothesis $$Power=P(reject\: H_{0}|H_{1}\: is\: true)=1-\underbrace{P(not\: rejecting\: H_{0}|H_{0}\: false)}_{Type\: 2\: error}\\=1-\beta=P(not\: making\: Type\: 2\: error)$$ Particular Interpretation Sample Size Larger sample more power Effect Size Larger Effect sieze more power Alpha Level Hi..
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Lack-of-fit sum of squares and Pure-error sum of squaresStats/Inferential 2020. 2. 4. 12:17
1. Overview In statistics, a sum of squares due to lack of fit, or more tersely a lack-of-fit sum of squares, is one of the components of a partition of the sum of squares of residuals in an analysis of variance, used in the numerator in an F-test of the null hypothesis that says that a proposed model fits well. The other component is the pure-error sum of squares. 2. Description 2.1 Intuition $..
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Type 1 Error and Type 2 ErrorStats/Inferential 2020. 1. 30. 14:22
1. Overview In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion), while a type II error is the non-rejection of a false null hypothesis (also known as a "false negative" finding or conclusion). 2. Description 2.1 Type 1 Error $\alpha$ It is often assimilated with false positives or Level of significa..
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Confidence IntervalStats/Inferential 2020. 1. 16. 00:00
1. Overview 2. Description 2.1 Confidence Interval A confidence interval is a range within which you expect the population parameter to be and its estimation is based on the data we have in our sample. when our confidence is lower the confidence interval itself is smaller. Similarly, for a 99 percent confidence interval, we would have higher confidence but a much larger confidence interval that'..