LO 9-4 - McGraw Hill Higher Education
... Using the Normal Distribution to Approximate the Binomial Distribution To develop a confidence interval for a proportion, we need to meet the following assumptions. 1. The binomial conditions, discussed in Chapter 6, have been met. Briefly, these conditions are: a. The sample data is the result of c ...
... Using the Normal Distribution to Approximate the Binomial Distribution To develop a confidence interval for a proportion, we need to meet the following assumptions. 1. The binomial conditions, discussed in Chapter 6, have been met. Briefly, these conditions are: a. The sample data is the result of c ...
Chapter 12: Inference for Proportions
... 22. Referring to the information above, which of the following assumptions for inference about a proportion using a confidence interval are violated in this example? A) n is so large that both npˆ and n(1 - p̂ ) are at least 10. B) The population is at least 10 times as large as the sample. C) We a ...
... 22. Referring to the information above, which of the following assumptions for inference about a proportion using a confidence interval are violated in this example? A) n is so large that both npˆ and n(1 - p̂ ) are at least 10. B) The population is at least 10 times as large as the sample. C) We a ...
What Can Be Inferred From A Kiss
... Rectangularity: Part III. Hypothesis Test on the Population Mean Purpose: This activity is intended to illustrate properties of hypothesis testing and describe how to perform hypothesis tests on a mean. Statistical Guide: We want to test a hypothesis about a population mean, . The null hypothesis ...
... Rectangularity: Part III. Hypothesis Test on the Population Mean Purpose: This activity is intended to illustrate properties of hypothesis testing and describe how to perform hypothesis tests on a mean. Statistical Guide: We want to test a hypothesis about a population mean, . The null hypothesis ...
Chapter 3
... = sum of the data values divided by n (statistic) “x-bar” n x μ = population mean = (parameter) “mew” N x = sample mean = ...
... = sum of the data values divided by n (statistic) “x-bar” n x μ = population mean = (parameter) “mew” N x = sample mean = ...
Evaluating Hypotheses
... Because the estimator is a random variable it can be characterised by the probability distribution that governs its value. ...
... Because the estimator is a random variable it can be characterised by the probability distribution that governs its value. ...
Confidence Intervals(new2)
... • Confidence intervals can be constructed for any parameter of interest (we have just looked at some common ones). • The general formulas shown here rely on the central limit theorem • You can choose level of confidence (does not have to be ...
... • Confidence intervals can be constructed for any parameter of interest (we have just looked at some common ones). • The general formulas shown here rely on the central limit theorem • You can choose level of confidence (does not have to be ...
Confidence Intervals(new2).
... • Confidence intervals can be constructed for any parameter of interest (we have just looked at some common ones). • The general formulas shown here rely on the central limit theorem • You can choose level of confidence (does not have to be ...
... • Confidence intervals can be constructed for any parameter of interest (we have just looked at some common ones). • The general formulas shown here rely on the central limit theorem • You can choose level of confidence (does not have to be ...
EDF 6472
... Step 3, we know that, for this sample z = -1.75. This is less than -1.65, so we conclude that the chances of the null hypothesis being true is less than 5% and decide to reject the null hypothesis. We will decide that the population mean is less than 175. e. Is there an inconsistency between the res ...
... Step 3, we know that, for this sample z = -1.75. This is less than -1.65, so we conclude that the chances of the null hypothesis being true is less than 5% and decide to reject the null hypothesis. We will decide that the population mean is less than 175. e. Is there an inconsistency between the res ...
sampling distribution
... of sample means for a relatively simple, specific situation. In most cases, however, it will not be possible to list all the samples and compute all the possible sample means. Therefore, it is necessary to develop the general characteristics of the distribution of sample means that can be applied in ...
... of sample means for a relatively simple, specific situation. In most cases, however, it will not be possible to list all the samples and compute all the possible sample means. Therefore, it is necessary to develop the general characteristics of the distribution of sample means that can be applied in ...
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.