![Topic 2. Distributions, hypothesis testing, and sample size determination](http://s1.studyres.com/store/data/007996545_1-d07070d1aa3482ba69facc2763ec4ea6-300x300.png)
Class Session #5 - Descriptive Statistics
... • An established probability level which serves as the criterion to determine whether to accept or reject the null hypothesis • It represents the confidence that your results reflect true relationships • Common levels in education • p < .01 (I will correctly reject the null hypothesis 99 of 100 time ...
... • An established probability level which serves as the criterion to determine whether to accept or reject the null hypothesis • It represents the confidence that your results reflect true relationships • Common levels in education • p < .01 (I will correctly reject the null hypothesis 99 of 100 time ...
One-way ANOVA - USU Math/Stat
... Analysis of variance (ANOVA) is the technique used to determine whether more than two population means are equal. One-way ANOVA is used for completely randomized, one-way designs. ...
... Analysis of variance (ANOVA) is the technique used to determine whether more than two population means are equal. One-way ANOVA is used for completely randomized, one-way designs. ...
statistical testing
... The p-value is the probability we would see a test-statistic as extreme or more extreme than the one we observed, if the null hypothesis was true. ...
... The p-value is the probability we would see a test-statistic as extreme or more extreme than the one we observed, if the null hypothesis was true. ...
Two-sample hypothesis testing, II
... • Sometimes, either theoretically, or from the data, it may be clear that this is not a good assumption. • Note: the equal-variance t-test is actually pretty robust to reasonable differences in the variances, if the sample sizes, n1 and n2 are (nearly) equal. – When in doubt about the variances of y ...
... • Sometimes, either theoretically, or from the data, it may be clear that this is not a good assumption. • Note: the equal-variance t-test is actually pretty robust to reasonable differences in the variances, if the sample sizes, n1 and n2 are (nearly) equal. – When in doubt about the variances of y ...
Induction on Regression (Ch 15)
... – Recall: Error is the deviation from the regression line – Is dispersion of error consistent across values of X? – Definition: “homoskedasticity” = error dispersion is consistent across values of X – Opposite: “heteroskedasticity”, errors vary with X ...
... – Recall: Error is the deviation from the regression line – Is dispersion of error consistent across values of X? – Definition: “homoskedasticity” = error dispersion is consistent across values of X – Opposite: “heteroskedasticity”, errors vary with X ...
Types of Error Systematic (determinate) errors Random
... • No identifiable cause; Always present, cannot be eliminated; the ultimate limitation on the determination of a quantity. • Ex. reading a scale on an instrument caused by the finite thickness of the lines on the scale; electrical noise • The accumulated effect causes replicate measurements to fluct ...
... • No identifiable cause; Always present, cannot be eliminated; the ultimate limitation on the determination of a quantity. • Ex. reading a scale on an instrument caused by the finite thickness of the lines on the scale; electrical noise • The accumulated effect causes replicate measurements to fluct ...
x - Analytical Chemistry
... the procedure must be evaluated for known quantities of analyte (called standards) so that the response to an unknown quantity can be interpreted. We prepare a calibration curve, which ideally is linear in the region of interest. The method of least squares is used to predict the “best” straight lin ...
... the procedure must be evaluated for known quantities of analyte (called standards) so that the response to an unknown quantity can be interpreted. We prepare a calibration curve, which ideally is linear in the region of interest. The method of least squares is used to predict the “best” straight lin ...
Practice Exam 01
... 23 IQ scores have a distribution that is approximately normal in shape, with a mean of 100 and a standard deviation of 15. What percentage of scores is at or above an IQ of 116? (a) 12. 464 (b) 14. 306 (c) 15. 737 (d) 16. 355 (e) None of the above answers are correct 24 Group A has a mean of 0 and ...
... 23 IQ scores have a distribution that is approximately normal in shape, with a mean of 100 and a standard deviation of 15. What percentage of scores is at or above an IQ of 116? (a) 12. 464 (b) 14. 306 (c) 15. 737 (d) 16. 355 (e) None of the above answers are correct 24 Group A has a mean of 0 and ...