Confidence Intervals
... Typically, before data is collected, we need to decide how large a sample to collect. This is usually done by deciding how much `above and below’ the estimator seems reasonable. For example, [55-3,55+3]% = [52,58]% is more information. The 3% is known as a margin of error. Given a certain margin of ...
... Typically, before data is collected, we need to decide how large a sample to collect. This is usually done by deciding how much `above and below’ the estimator seems reasonable. For example, [55-3,55+3]% = [52,58]% is more information. The 3% is known as a margin of error. Given a certain margin of ...
central tendency & variability
... • Both the mean and the median can be computed for the values in the distribution of any quantitative variable. • However, the degree to which one or the other is a “good” measure or indicator of the central tendency of a distribution differs with the shape of the distribution, specifically the sym ...
... • Both the mean and the median can be computed for the values in the distribution of any quantitative variable. • However, the degree to which one or the other is a “good” measure or indicator of the central tendency of a distribution differs with the shape of the distribution, specifically the sym ...
between groups variance
... 1. The test variable (DV) is normally distributed 2. The variances of the normally distributed test variable is equal – Homogeniety of Variance 3. Random assignment to groups ...
... 1. The test variable (DV) is normally distributed 2. The variances of the normally distributed test variable is equal – Homogeniety of Variance 3. Random assignment to groups ...
Wikipedia's entry on Pearson's correlation
... For centered data (i.e., data which have been shifted by the sample mean so as to have an average of zero), the correlation coefficient can also be viewed as the cosine of the angle between the two vectors of samples drawn from the two random variables (see below). Both the uncentered (non-Pearson-c ...
... For centered data (i.e., data which have been shifted by the sample mean so as to have an average of zero), the correlation coefficient can also be viewed as the cosine of the angle between the two vectors of samples drawn from the two random variables (see below). Both the uncentered (non-Pearson-c ...
Interval Estimation - University of Arizona Math
... The confidence interval for a parameter ✓ is based on two statistics - ✓ˆ` (x), the lower end of the confidence interval and ✓ˆu (x), the upper end of the confidence interval. As with all statistics, these two statistics cannot be based on the value of the parameter. In addition, these two statistic ...
... The confidence interval for a parameter ✓ is based on two statistics - ✓ˆ` (x), the lower end of the confidence interval and ✓ˆu (x), the upper end of the confidence interval. As with all statistics, these two statistics cannot be based on the value of the parameter. In addition, these two statistic ...
Lecture 8 Quantitative Procedures (2)
... When we have more than two groups and want to test for mutual mean difference between all the groups we usually use a form of the ANOVA method. The term is actually confusing as the test compares the means of the various groups but it does so by computing and comparing the different population varia ...
... When we have more than two groups and want to test for mutual mean difference between all the groups we usually use a form of the ANOVA method. The term is actually confusing as the test compares the means of the various groups but it does so by computing and comparing the different population varia ...
Lecture 8
... When we have more than two groups and want to test for mutual mean difference between all the groups we usually use a form of the ANOVA method. The term is actually confusing as the test compares the means of the various groups but it does so by computing and comparing the different population varia ...
... When we have more than two groups and want to test for mutual mean difference between all the groups we usually use a form of the ANOVA method. The term is actually confusing as the test compares the means of the various groups but it does so by computing and comparing the different population varia ...
Count Population Profiles in Engineering Anomalies Experiments
... indicated amounts. It should first be noted, however, that considerable electronic processing is applied to the output of the primary noise diode prior to the bit-counting stage, including the imposition of an alternating template that compares the raw random bit string with a regularly alternating ...
... indicated amounts. It should first be noted, however, that considerable electronic processing is applied to the output of the primary noise diode prior to the bit-counting stage, including the imposition of an alternating template that compares the raw random bit string with a regularly alternating ...
(17) Find the approximate percentile ranks of the
... standard deviation of .7. A new dispatching system is being tested to see if the response time can be lowered. If a sample of 18 calls with the new system were answered in a mean time of 7.8 minutes, test at the .05 level. State the hypotheses and identify the claim, find the critical value(s), comp ...
... standard deviation of .7. A new dispatching system is being tested to see if the response time can be lowered. If a sample of 18 calls with the new system were answered in a mean time of 7.8 minutes, test at the .05 level. State the hypotheses and identify the claim, find the critical value(s), comp ...
Tutorial 1: Power and Sample Size for the One-sample t
... In carrying out a one-sample t-test we make the assumption that the observations are randomly sampled from a well-characterized population and are independent of each other (i.e., that there is no clustering). In most cases, we can easily verify this assumption. In addition, we assume an approximate ...
... In carrying out a one-sample t-test we make the assumption that the observations are randomly sampled from a well-characterized population and are independent of each other (i.e., that there is no clustering). In most cases, we can easily verify this assumption. In addition, we assume an approximate ...
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.