When documenting on a client with a diagnosis of colon cance…

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When dоcumenting оn а client with а diаgnоsis of colon cancer, which of the following would be appropriate documentation of subjective findings? Select all that apply. The client  

Exаm 2 Summаry *Nоte: The fоllоwing is not а complete list of everything assessed on the exam, but rather a high-level overview. Chapter 3 Summary: Confidence Intervals We estimate a population parameter using a sample statistic. Since such statistics vary from to sample, we need to get some sense of the accuracy of the statistic, for example, with a margin of error. This leads to the concept of an interval estimate as a range of plausible values for the population parameter. An interval estimate is a range of plausible values for the population parameter. when we construct this interval using a method that has some predetermined chance of capturing the true parameter, we get a confidence interval. General form of an interval estimate: Sample statistic (pm) margin of error 95% CI using SE: Sample statistic (pm) 2*SE Bootstrap Distribution: How bootstrap distributions are constructed... Generate bootstrap samples with replacement from the original sample, using the same sample size. Compute the statistic of interest for each of the bootstrap samples Collect the statistics from many (usually at least 5000) bootstrap samples into a bootstrap distribution. From a bell-shaped bootstrap distribution, we have two methods to construct an interval estimate: Method 1: Standard Error - The standard error, SE, of the statistic is the standard deviation of the bootstrap distribution. Roughly, the 95% confidence interval for the parameter is then sample statistic (pm) 2*SE. Method 2: Percentiles - Use percentiles of the bootstrap distribution to chop off the tails of the bootstrap distribution and keep a specified percentage (determined by the confidence level) of the values of the middle. Chapter 4 Summary: Hypothesis Testing Hypothesis tests are used to investigate claims about population parameters. We use the question of interest to determine the two competing hypotheses: The null hypothesis is generally that there is no effect or no difference while the alternative hypothesis is the claim for which we seek evidence. The null hypothesis is the default assumption; we only conclude in favor of the alternative hypothesis if the evidence in the sample supports the alternative hypothesis and provides strong evidence against the null hypothesis. If the evidence is inconclusive, we stick with the null hypothesis.We measure the strength of evidence against the null hypothesis using a p-value. A p-value is the probability of obtaining a sample statistic as extreme as (or more extreme than) the observed sample statistic, when the null hypothesis is true. A small p-value means that the observed sample results would be unlikely to happen just by random chance, if the null hypothesis were true, and thus provides evidence against the null hypothesis. The smaller the p-value, the stronger the evidence against the null hypothesis and in support of the alternative hypothesis. When making specific decisions based on the p-value, we use a pre-specified significance level, (alpha). If p-value (lt alpha), we reject (H_0) and have statistically significant evidence for (H_a). If p-value (ge alpha), we do not reject (H_0), the test is inconclusive, and the results are not statistically significant at that level. Randomization Distribution: We calculate a p-value by constructing a randomization distribution of possible sample statistics that we might see by random chance, if the null hypothesis were true. A randomization distribution is constructed by simulating many samples in a way that: Assumes the null hypothesis is true Uses the original sample data The p-value is the proportion of the randomization distribution that is as extreme as, or more extreme than, the observed sample statistic. If the observed sample statistic falls out in the tails of the randomization distribution, then a result this extreme is unlikely to occur if the null hypothesis is true, and we have evidence against the null hypothesis in favor of the alternative.   Possible Errors in a Formal Statistical Decision  Possible errors in a formal statistical decision   Decision   Reject H0 Do not reject H0 Reality: H0 is true Type I error No error H0 is false No error Type II error