Central tendency, dispersion
... Not the arithmetic mean, because in year 1 your investment was multiplied (not added to) by 1.10, etc…… Ask "by what constant factor would your investment need to be multiplied by each year in order to achieve the same effect as multiplying by 1.10 one year, 1.50 the next, and 1.30 the third?" The a ...
... Not the arithmetic mean, because in year 1 your investment was multiplied (not added to) by 1.10, etc…… Ask "by what constant factor would your investment need to be multiplied by each year in order to achieve the same effect as multiplying by 1.10 one year, 1.50 the next, and 1.30 the third?" The a ...
Measures of Variability
... • At least .75, or 75 percent of the data values must be within 2 ( z = 2) standard deviations of the mean. • At least .89, or 89 percent, of the data values must be within 3 (z = 3) standard deviations of the mean. • At least .94, or 94percent, of the data values must be within 4 (z = 4) standard d ...
... • At least .75, or 75 percent of the data values must be within 2 ( z = 2) standard deviations of the mean. • At least .89, or 89 percent, of the data values must be within 3 (z = 3) standard deviations of the mean. • At least .94, or 94percent, of the data values must be within 4 (z = 4) standard d ...
Descriptive Statistics and
... • Estimate variability from pilot or previous, related study • Example: The authors estimate that the standard deviation of DBP is 7 mm Hg. ...
... • Estimate variability from pilot or previous, related study • Example: The authors estimate that the standard deviation of DBP is 7 mm Hg. ...
Statistics
... Typical Problem • Repeated counts are made in 1min intervals with a long-lived source. The observed mean is 813 counts with s = 28.5 counts. What is the probability of observing 800 or fewer counts? Answer • This is about -0.45s. • Look up P((x-m)/s < -0.45) – P = 0.324 ...
... Typical Problem • Repeated counts are made in 1min intervals with a long-lived source. The observed mean is 813 counts with s = 28.5 counts. What is the probability of observing 800 or fewer counts? Answer • This is about -0.45s. • Look up P((x-m)/s < -0.45) – P = 0.324 ...
chap7 sec3
... • A statistical test for a population mean. • The t-test can be used when the population is normal or nearly normal, σ is unknown, and n < 30. • The test statistic is the sample mean x • The standardized test statistic is t. ...
... • A statistical test for a population mean. • The t-test can be used when the population is normal or nearly normal, σ is unknown, and n < 30. • The test statistic is the sample mean x • The standardized test statistic is t. ...
11: Variances and Means ( )2
... Because calculation of dfWelch is tedious, we may when working by hand use the smaller of df1 = n1 − 1 or df2 = n2 − 1 as a conservative approximation for the degrees of freedom: dfconserv = the smaller of df1 and df2 The degrees of freedom are never less than the smaller of df1 and df2 (Welch, 1938 ...
... Because calculation of dfWelch is tedious, we may when working by hand use the smaller of df1 = n1 − 1 or df2 = n2 − 1 as a conservative approximation for the degrees of freedom: dfconserv = the smaller of df1 and df2 The degrees of freedom are never less than the smaller of df1 and df2 (Welch, 1938 ...
No Slide Title
... What if you have found 100 hypotheses satisfying this condition? Then the probability that all have an error below 10% is equal to (0.95)100 0.013 corresponding to 1.3 %. So, the probability of having at least one hypothesis with an error above 10% is about 98.7%! ...
... What if you have found 100 hypotheses satisfying this condition? Then the probability that all have an error below 10% is equal to (0.95)100 0.013 corresponding to 1.3 %. So, the probability of having at least one hypothesis with an error above 10% is about 98.7%! ...
Sampling Distributions
... If you generate your sample using an appropriate method, you don’t have control over the amount of variability in your sample. You do, however, have control over your sample size. So if you want to have confidence in the estimate from your one sample, generate a large sample! Notice that the samplin ...
... If you generate your sample using an appropriate method, you don’t have control over the amount of variability in your sample. You do, however, have control over your sample size. So if you want to have confidence in the estimate from your one sample, generate a large sample! Notice that the samplin ...
Bootstrapping (statistics)
In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.