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... hours, recorded for 30 machines owned by a large manufacturing company. The period of time covered was the same for all machines. ...
... hours, recorded for 30 machines owned by a large manufacturing company. The period of time covered was the same for all machines. ...
Notes 5
... As with the ratio estimator, the regression estimator will gain more over the sample mean when y and x are highly correlated. To decide whether using the ratio estimator, regression estimator or sample mean is a good idea, we should first plot the data. If a simple linear regression model appears to ...
... As with the ratio estimator, the regression estimator will gain more over the sample mean when y and x are highly correlated. To decide whether using the ratio estimator, regression estimator or sample mean is a good idea, we should first plot the data. If a simple linear regression model appears to ...
Two Sample Inference for Means
... Independent sample observations. Random sample. Unknown variance. Two distributions have different variance, as per F test. ...
... Independent sample observations. Random sample. Unknown variance. Two distributions have different variance, as per F test. ...
Chapter 18 - PH School
... assume them. But, as we saw in Chapter 8, we should check to see whether the assumptions are reasonable. To think about the Independence Assumption, we often wonder whether there is any reason to think that the data values might affect each other. Fortunately, we can often check conditions that prov ...
... assume them. But, as we saw in Chapter 8, we should check to see whether the assumptions are reasonable. To think about the Independence Assumption, we often wonder whether there is any reason to think that the data values might affect each other. Fortunately, we can often check conditions that prov ...
Measures of Central Tendency
... Better than mode because only one score can be median and the median will usually be around where most scores fall. If data are perfectly normal, the mode is the median. The median is computed when data are ordinal scale or when they are highly skewed. ...
... Better than mode because only one score can be median and the median will usually be around where most scores fall. If data are perfectly normal, the mode is the median. The median is computed when data are ordinal scale or when they are highly skewed. ...
The Normal Distribution of Errors Computational Physics
... distribution of the sum of a large number of random variables will tend towards a normal distribution. Our 500 step random walk is the sum of 500 numbers drawn from a probability distribution with two results: +1 and -1. Hence, according to CLT, we expect a normal distribution! ...
... distribution of the sum of a large number of random variables will tend towards a normal distribution. Our 500 step random walk is the sum of 500 numbers drawn from a probability distribution with two results: +1 and -1. Hence, according to CLT, we expect a normal distribution! ...
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.