Extra Credit: 5 points Sara loves Christmas. She has recent…
Questions
Extrа Credit: 5 pоints Sаrа lоves Christmas. She has recently embraced the "Christmas in July". She dresses up fоr work in flip flops, red and white striped pants and shirt. She decides that a candy cane will really make her outfit pop! On the way to work she is enjoying the candy cane and gets it good and sharp on the tip. She gets out, trips on her flip flop, the candy cane goes flying into the air, shoe looks up and BAM it embeds itself in her forehead. Screaming bloody murder she runs into work and her friend Denise pulls it out and tell's her to "get to work" Her face is now packed with staphlococcus bacteria. Please describe in order her immune system response from the beginning to the end of the infection. Describe the timeline on which immune cells reach the site of infection and what they are doing to aid in resolution of the infection. Use the following WBC: Mast Cells, Nuetrophils, Monocytes, Macrophages, T-Helper, B-Cells, Cytotoxic T-Cells, Memory Cells, and Regulatory T-Cells.
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. Note: if no