Using SAS® to Create an Effect Size Resampling Distribution for a Statistical Test
... The resampling distributions for effect size and p-values differ from analytic studies such as power studies in as far as no theoretical probability distribution is assumed. The conclusions are of the form: these are distributions of outcomes when sampling from a population such as the one originall ...
... The resampling distributions for effect size and p-values differ from analytic studies such as power studies in as far as no theoretical probability distribution is assumed. The conclusions are of the form: these are distributions of outcomes when sampling from a population such as the one originall ...
![parametric statistics version 2[1].](http://s1.studyres.com/store/data/002643361_1-f1ac5a1364ee6a2d4038d7b36f236808-300x300.png)
Hypothesis Testing
... • If we hypothesize that the data come from a N(0,1) distribution, how unusual an observation must we see to reject our hypothesis? It depends on the alternative hypothesis… ...
... • If we hypothesize that the data come from a N(0,1) distribution, how unusual an observation must we see to reject our hypothesis? It depends on the alternative hypothesis… ...
Power and Sample Size + Principles of Simulation
... Mean different from 0 hypotheses: ◦ ho (null hypothesis) is μ=0 ◦ ha (alternative hypothesis) is μ ≠ 0 ...
... Mean different from 0 hypotheses: ◦ ho (null hypothesis) is μ=0 ◦ ha (alternative hypothesis) is μ ≠ 0 ...
Estimating a Population Mean - Unknown SD
... Key Concept This section presents methods for estimating a population mean when the population standard deviation is not known. With σ unknown, we use the Student t distribution assuming that the relevant requirements are satisfied. ...
... Key Concept This section presents methods for estimating a population mean when the population standard deviation is not known. With σ unknown, we use the Student t distribution assuming that the relevant requirements are satisfied. ...
inference_steps - the Department of Statistics Online Learning!
... Hypothesis Testing. If the question were “We want to show that there is a difference between mean GPA of males and females” this would infer that a hypothesis test should be conducted where the hypothesized value is zero (i.e. no difference). Step 2: After determining the inference method we then ha ...
... Hypothesis Testing. If the question were “We want to show that there is a difference between mean GPA of males and females” this would infer that a hypothesis test should be conducted where the hypothesized value is zero (i.e. no difference). Step 2: After determining the inference method we then ha ...
Ch6-Sec6.1
... Interpreting the Results The horizontal segments represent 90% confidence intervals for different samples of the same size. In the long run, 9 of every 10 such intervals will contain μ. ...
... Interpreting the Results The horizontal segments represent 90% confidence intervals for different samples of the same size. In the long run, 9 of every 10 such intervals will contain μ. ...
Statistical Analysis
... inferences he or she makes about the larger population (the entire Shorecrest student body) is accurate? We use inferential statistics! Instead of making absolute conclusions about the population, researchers make statements about the population using the laws of probability and statistical ...
... inferences he or she makes about the larger population (the entire Shorecrest student body) is accurate? We use inferential statistics! Instead of making absolute conclusions about the population, researchers make statements about the population using the laws of probability and statistical ...
iclicker_chapter_19
... the shape of the sampling distribution of p̂ close enough to normal to use the normal distribution to compute probabilities on p̂ ? ...
... the shape of the sampling distribution of p̂ close enough to normal to use the normal distribution to compute probabilities on p̂ ? ...
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.