Pelham Ch 2 Answers to Questions
... higher kurtosis score is presumably a reflection of the fact that only two (rather than four) of the 21 scores deviate at all from the median score of 10 fingers. Supplemental Answer: To see whether kurtosis is more sensitive to the number of scores that deviate from the central tendency or the amo ...
... higher kurtosis score is presumably a reflection of the fact that only two (rather than four) of the 21 scores deviate at all from the median score of 10 fingers. Supplemental Answer: To see whether kurtosis is more sensitive to the number of scores that deviate from the central tendency or the amo ...
Lesson 6 Z-Scores Outline Linear Transformation
... instead of zero), what would I have to do to find the true heights of everyone? If you think about it you will see that I must subtract two inches from each measurement to get the true heights (because the start position was too high). This example of a linear transformation is one in which we simpl ...
... instead of zero), what would I have to do to find the true heights of everyone? If you think about it you will see that I must subtract two inches from each measurement to get the true heights (because the start position was too high). This example of a linear transformation is one in which we simpl ...
Statistical Hypotheses
... In the example above, we calculated the P-value of the test by finding the area to the left of the standardized test statistic z on the standard normal curve. Notice that the example above was also a left-tailed test, and that any hypothesis test which is left-tailed will have the P-value calculated ...
... In the example above, we calculated the P-value of the test by finding the area to the left of the standardized test statistic z on the standard normal curve. Notice that the example above was also a left-tailed test, and that any hypothesis test which is left-tailed will have the P-value calculated ...
Graph A
... though the hypothesis can be falsified, it can never be proved to be true. The evidence from an investigation can only provide support for the hypothesis. In our example, if cacti without spines were eaten, the hypothesis has not been proved, but has been supported by evidence. Other explanations st ...
... though the hypothesis can be falsified, it can never be proved to be true. The evidence from an investigation can only provide support for the hypothesis. In our example, if cacti without spines were eaten, the hypothesis has not been proved, but has been supported by evidence. Other explanations st ...
Statistical Foundations: Descriptive Statistics
... We know an “A” is higher than a “C”, but we don’t know by exactly by how much. “Strongly Disagree” is an opinion which is very different from “Strongly Agree,” but exactly how different is unknown. Interval data is similar to ordinal data; but, distances between points on a specified measurement sca ...
... We know an “A” is higher than a “C”, but we don’t know by exactly by how much. “Strongly Disagree” is an opinion which is very different from “Strongly Agree,” but exactly how different is unknown. Interval data is similar to ordinal data; but, distances between points on a specified measurement sca ...
Describing Data: Summary Measures
... median. It is important to understand that service times are bounded on the lower end by zero (or it is impossible for the service time to be negative). However, there is no bound on the maximum service time. Therefore, the larger service times are causing the mean to be somewhat higher than the med ...
... median. It is important to understand that service times are bounded on the lower end by zero (or it is impossible for the service time to be negative). However, there is no bound on the maximum service time. Therefore, the larger service times are causing the mean to be somewhat higher than the med ...
Statistical Inference
... • Suppose that I want to conduct a survey and want to estimate p = proportion of voters who favour a downtown location for a casino: I know that the approximate value of p is • p* = 0.50. This is also a good choice for p if one has no preliminary estimate of its value. • I want the survey to estimat ...
... • Suppose that I want to conduct a survey and want to estimate p = proportion of voters who favour a downtown location for a casino: I know that the approximate value of p is • p* = 0.50. This is also a good choice for p if one has no preliminary estimate of its value. • I want the survey to estimat ...
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.