Powerpoint
... • The mean is sensitive to outliers, median and mode are not • In general, the median is more appropriate for skewed data than the mean • In some situations, the median may be too insensitive to changes in the data • The mode may not be unique ...
... • The mean is sensitive to outliers, median and mode are not • In general, the median is more appropriate for skewed data than the mean • In some situations, the median may be too insensitive to changes in the data • The mode may not be unique ...
Measures of Variation
... (d) Determine the percentage of patients that have serum HDL between 34 and 69.1 according to the Empirical Rule. (e) Determine the actual percentage of patients that have serum HDL between 34 and 69.1 (use the raw data directly, not the empirical rule for this question. See how close the empirical ...
... (d) Determine the percentage of patients that have serum HDL between 34 and 69.1 according to the Empirical Rule. (e) Determine the actual percentage of patients that have serum HDL between 34 and 69.1 (use the raw data directly, not the empirical rule for this question. See how close the empirical ...
lecture7
... Round-off rule for sample size n: when necessary, round up to obtain the next whole number. Example 3: a sociologist wishes to estimate the percentage of the U.S population living in poverty. What size sample should be obtained if she whishes the estimate to be within 2 percentage points with 99% co ...
... Round-off rule for sample size n: when necessary, round up to obtain the next whole number. Example 3: a sociologist wishes to estimate the percentage of the U.S population living in poverty. What size sample should be obtained if she whishes the estimate to be within 2 percentage points with 99% co ...
One Sample t-Test for a Mean
... Comparative studies are more convincing than single-sample investigations. For that reason, one-sample inference is less common than comparative inference. Study designs that involve making two observations on the same individual, or one observation on each of two similar individuals, result in pair ...
... Comparative studies are more convincing than single-sample investigations. For that reason, one-sample inference is less common than comparative inference. Study designs that involve making two observations on the same individual, or one observation on each of two similar individuals, result in pair ...
Lecture 9 - Statistics
... Last day, introduced a point estimator…a statistic that estimates a population parameter Often more desirable to present a plausible range for the parameter, based on the data We will call this a confidence interval ...
... Last day, introduced a point estimator…a statistic that estimates a population parameter Often more desirable to present a plausible range for the parameter, based on the data We will call this a confidence interval ...
Bootstrap Finite-Sample Distribution
... In addition to the standard bootstrap, the examples offered herein of using the analytical approach include the wild bootstrap of Liu (1988) and Davidson and Flachaire (2001), the wild bootstrap of Mammen (1993), the non-overlapping, fixed-block bootstrap of Hall (1985) and Carlstein (1986), and th ...
... In addition to the standard bootstrap, the examples offered herein of using the analytical approach include the wild bootstrap of Liu (1988) and Davidson and Flachaire (2001), the wild bootstrap of Mammen (1993), the non-overlapping, fixed-block bootstrap of Hall (1985) and Carlstein (1986), and th ...
Module 3: Estimation and Properties of Estimators
... population standard deviation or population variance was known. In fact, even if the population standard deviation or variance is unknown, but the sample size is sufficiently large, then σ 2 ≈ s2 . We can use the methods of the previous section to create a confidence interval in such a situation. Th ...
... population standard deviation or population variance was known. In fact, even if the population standard deviation or variance is unknown, but the sample size is sufficiently large, then σ 2 ≈ s2 . We can use the methods of the previous section to create a confidence interval in such a situation. Th ...
Mth 240
... weight of garbage discarded by households in one week. Find the sample size necessary to estimate that mean if you want to be 96% confident that the sample mean is within 2 lbs of the true population mean. For the population standard deviation , use the value 12.46 lbs, which is the standard deviat ...
... weight of garbage discarded by households in one week. Find the sample size necessary to estimate that mean if you want to be 96% confident that the sample mean is within 2 lbs of the true population mean. For the population standard deviation , use the value 12.46 lbs, which is the standard deviat ...
Measures of Central Location
... – The range of a set of observations is the difference between the largest and smallest observations. – Its major advantage is the ease with which it can be computed. – Its major shortcoming is its failure to provide information on the dispersion of the observations between the two end points. But, ...
... – The range of a set of observations is the difference between the largest and smallest observations. – Its major advantage is the ease with which it can be computed. – Its major shortcoming is its failure to provide information on the dispersion of the observations between the two end points. But, ...
Lecture #7 Chapter 7: Estimates and sample sizes In this chapter
... Round-off rule for confidence intervals used to estimate : a) If the original set of data in known, round the confidence interval limits to one more decimal place than is used for the original set of data. b) When the original set of data is unknown, round the confidence interval limits to the same ...
... Round-off rule for confidence intervals used to estimate : a) If the original set of data in known, round the confidence interval limits to one more decimal place than is used for the original set of data. b) When the original set of data is unknown, round the confidence interval limits to the same ...
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.