which of the following statements is NOT true about an outlier ? a . an outlier
ID: 3295888 • Letter: W
Question
which of the following statements is NOT true about an outlier? a. an outlier is a data point or case that is substantially from the trend of the data b. outliers cannot cause bias in the data c. outliers can cjhange the slope and intercepts in multiple linear regression d. deleting an outlier from the the dataset is not an acceptable options
Assumed that a researcher wants to understand the impact of diabetes on people living with HIV. So he asks the following research questions: is knowledge about diabetes associated with A1C result among people living with HIV? He wants to explore whether this relationship is affected by sex, social support, length of time of diabetes diagnosis, and anti-retovial therapy regiment.Knowledge about diabetes will be measured usinga test that is scoredfrom 0-100points, and assumed that the variableA1C result is normally distributed. What is the depaendent variable, a. knowledge about diabetes b. HIV status c. A1c result d. social support
a 95% confidence interval for one of the partial slopes ( regression coefficient) in a multiple linear regression analysis (-0.25, 0.66) Given the information you can conclude? a. the coefficient needs to be standardized before drawing any conclusions b. the coeeficient is not significantly diffeent from zero at an alpha of.05 after controlling for the other variables in the model c. the coefficient is significantly different from zero at the p-value <.05 after controlling the other variables in the model d. there is no sufficient information to comment on significance of the coefficient
Explanation / Answer
The incorrect statement is (b) Outlier can't cause biasness in the data set. (Ans).
Here the dependent variable is (c) A1c result. (Ans).
Since the interval contains 0, it can't be significantly different from 0. So the correct choice is: Option (b): the coefficient is not significantly different from zero at an alpha of.05 after controlling for the other variables in the model. (Ans).
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