Test of statistical significance formula
[DOC File]Advanced Excel - Statistical functions & formulae
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The significance level of a statistical hypothesis test is a fixed probability of wrongly rejecting the null hypothesis H0, if it is in fact true. S. The variance – a measure of the dispersion or spread of scores around its average. σ2. Standard deviation – another measure of the dispersion of scores. ∑ Sum of a series of values. t-test
[DOC File]Formula Sheet and List of Symbols, Basic Statistical Inference
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Formula Sheet and List of Symbols, Basic Statistical Inference. Symbol What it Represents. X variable. sample mean. μ population mean. s sample standard deviation. s2 sample variance. σ population standard deviation. σ2 population variance. sample proportion. p population proportion. q 1-p. n sample size. α significance level
[DOC File]MULTIPLE REGRESSION AND CORRELATION
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A shrunken R squared equal to zero corresponds exactly to F = 1.0 in the test for statistical significance. If the formula for shrunken R squared produces a negative value, this indicates that your observed R2 is smaller than you would expect if R2 = 0 in the population, and your best estimate of the population value of R is zero.
[DOCX File]Statistical Tests - Loudoun County Public Schools
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If you are using a significance level of alpha = 0.05, a two-tailed test allots half of your alpha to testing the statistical significance in one direction and half of your alpha to testing statistical significance in the other direction. This means that .025 is in each tail of the distribution of your test statistic.
[DOC File]Testing of Hypothesis and Significance:
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H1 :µ ≠µo (two side test)l . Level of significance α = 0.05. While Test Statistics to be used is. Z = (X-µ)/ δ/√n . The known values are µo = 20.4 mm, δ = 2.0 mm, n= 16, X = 22.0 mm. Hence by putting the values, Z = (22-20.4)/2/√16 = 3.2
[DOC File]Estimating the Sample Size Necessary to Have Enough Power
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Suppose that the effect size we wish to use is one where the three populations means are 480, 500, and 520, with the within-group standard deviation being 100. Using the first formula above, . Using the second formula, the population standard deviation of the means (with k, not k-1, in the denominator) is 16.33, so f = 16.33 ( 100 = .163.
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