LECTURE 14 (Week 5)
... Drinking red wine in moderation may protect against heart attacks. The polyphenols it contains act on blood cholesterol and thus are a likely cause. To test the hypothesis that moderate red wine consumption increases the average blood level of polyphenols, a group of nine randomly selected healthy m ...
... Drinking red wine in moderation may protect against heart attacks. The polyphenols it contains act on blood cholesterol and thus are a likely cause. To test the hypothesis that moderate red wine consumption increases the average blood level of polyphenols, a group of nine randomly selected healthy m ...
chapter guide2
... - Find x-value corresponding to a given probability - sampling distribution of sample mean - Central Limit Theorem - Finding probability for x and mean of x - point estimate vs confidence interval for population mean - margin of error - use of t Table - how to calculate (formula) confidence interval ...
... - Find x-value corresponding to a given probability - sampling distribution of sample mean - Central Limit Theorem - Finding probability for x and mean of x - point estimate vs confidence interval for population mean - margin of error - use of t Table - how to calculate (formula) confidence interval ...
Introduction to the t statistic OVERVIEW 1. A sample mean (X)
... population mean (μ) or variance (σ2)to be known Can be used to test hypotheses about a completely unknown population (when the only information about the population comes from the sample) Required: a sample and a reasonable hypothesis about the population mean (μ) Can be used with one sample o ...
... population mean (μ) or variance (σ2)to be known Can be used to test hypotheses about a completely unknown population (when the only information about the population comes from the sample) Required: a sample and a reasonable hypothesis about the population mean (μ) Can be used with one sample o ...
Group Work STAT 366 1/27/10 1. Which data set has a larger
... B) 31,32,33,…88,89,90 again, that’s 60 data points in all with mean = 60.5 (Hint: Don’t try to calculate the actual standard deviations. What does standard deviation measure?) The answer is (A). In (A), the “distance” between the mean and every data point is 30, so the average of these “distances”, ...
... B) 31,32,33,…88,89,90 again, that’s 60 data points in all with mean = 60.5 (Hint: Don’t try to calculate the actual standard deviations. What does standard deviation measure?) The answer is (A). In (A), the “distance” between the mean and every data point is 30, so the average of these “distances”, ...
Lecture 4 - Cynthia Bell
... closely resembles the population. We know it will never be exactly like the population, the only way to do this is to take a census which collects data on every single person in the population (most of the time this is impossible). This means that all of the summaries (graphical: histogram, pie char ...
... closely resembles the population. We know it will never be exactly like the population, the only way to do this is to take a census which collects data on every single person in the population (most of the time this is impossible). This means that all of the summaries (graphical: histogram, pie char ...
Properties of Majority Transformations under Random Processes Parameters Measurement
... In practice it is often required to determine the quantitative attribute (variables) against noise background. Lets assume that its initially known what kind of distribution a sign exactly possesses. There comes the task of assessing parameters that determine this distribution. If its known that the ...
... In practice it is often required to determine the quantitative attribute (variables) against noise background. Lets assume that its initially known what kind of distribution a sign exactly possesses. There comes the task of assessing parameters that determine this distribution. If its known that the ...
Chapter 5: Statistical Reasoning Assignment #1: Unit 5A
... simple random sampling – we choose a sample of items in such a way that every sample of a given size has an equal chance of being selected. systematic sampling – we use a simple system to choose the sample, such as selecting every 10th member of the population. convenience sampling – we use a sample ...
... simple random sampling – we choose a sample of items in such a way that every sample of a given size has an equal chance of being selected. systematic sampling – we use a simple system to choose the sample, such as selecting every 10th member of the population. convenience sampling – we use a sample ...
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.