Which of the following correctly concludes the hypothesis test conducted in the r output above?

Question

BlackTom AI · Accepted Answer

First, we need to consider what the question is asking: it wants a conclusion about the hypothesis test conducted in the R output referenced. Each option makes a claim about significance and about the strength of the relationship between income and age.
Option 1: 'There is a significant relationship between income and age, but we do not know the strength of the relationship using this r output.' This statement distinguishes between statistical significance and practical/strength interpretation. If the p-value indicates significance but the output lacks a clear measure of effect size (such as an r value, beta, or correlation coefficient) or if the reported statistic is not enough to gauge strength, this option can be accurate.
Option 2: 'There is a significant but weak linear relationship between income and age.' This asserts both significance and a weak strength. Without seeing the actual test statistic or effect size from the R output (e.g., r value or standardized coefficient), you cannot conclude strength confidently, so this assertion may be unfounded unless the output explicitly shows a small effect size.
Option 3: 'There is a significant and strong linear relationship between income and age.' This claims strong strength. Similar to option 2, a determination of strength requires a reported effect size or correlation coefficient. Without that information, this statement asserts a level of strength that may be incorrect.
Option 4: 'There is neither a significant nor a strong linear relationship between income and age.' This denies significance and strength. If the test showed a p-value below the chosen alpha level, this option would be contradicted by the data, unless the result is non-significant. Since we do not have that detail here, this option could be incorrect if the test was significant.
In discussing each option, it’s important to note that R outputs typically provide a p-value to assess significance and an effect size measure (like r, r-squared, or coefficients) to gauge strength. Without a reported effect size, concluding strength is speculative. Conversely, reporting significant p-values without effect size leaves strength indeterminate. The core idea is that significance and strength are related but distinct concepts in regression or correlation analyses, and both should be reported if possible. If the R output includes a significant p-value but no clear measure of strength, option 1 appropriately separates significance from strength. If the output includes an explicit strength assessment, options 2 or 3 could be supported or refuted accordingly. If the p-value is not significant, option 4 could be consistent, but that would depend on the actual test result. The safest interpretation, given typical outputs and the phrasing of option 1, is that significance might be present while strength remains undetermined from the provided output. The reasoning here hinges on distinguishing significance from effect size and recognizing what the R output actually reports.": "

FA25_QTM_100_3 Final Exam- Requires Respondus LockDown Browser

Which of the following correctly concludes the hypothesis test conducted in the r output above?

查看解析

登录即可查看完整答案

类似问题

TB MC Qu. 10-18 Which of the following potential... Which of the following potential observations are scatter plots used to exhibit?

If 2 items are close to negatively correlated, then the value of the correlation is approximately ..

Which of the following methods should be used to determine if there is an association between weekly time spent doing homework (in hours) and exam score (in points)?

See the previous regression analysis information. The correlation between number of adverse life events and depression is:

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单