Questions pertain to the following table (Table 12), which presents results for a fictitious logistic regression analysis predicting acute confusion in long-term care residents.
$\begin{array}{c}
\text { TABLE } 12\
\text { Predictors of Acute Confusion in Long-Term Care Residents }(N=2,100)\
\begin{array}{r}
\text { 95\% CI, Odds Ratio }\
\begin{array}{|l|c|c|c|c|c|}
\hline \text { Predictor }& \boldsymbol{b} & \text { SE }(b) & \text { Odds Ratio } & \text { Lower } & \text { Upper } \
\hline \text { Inadequate fluid intake } & 1.22 & .21 & 3.20 & 2.87 & 4.01 \
\text { Fell, past 30 days } & .45 & .20 & 1.68 & 1.37 & 2.05 \
\text { 5+ Medications } & .20 & .12 & 1.07 & .92 & 1.23 \
\text { Vision impairment } & .34 & .11 & 1.30 & 1.08 & 1.77 \
\hline
\end{array}\end{array}\end{array}$
Hosmer-Lemeshow chi-square $ =1.57, d f=5, p=.68 $
Nagelkerke $ R^{2}=.12 $
Percent correctly classified $ =68.9 \% $
-Refer to Table 12. If the Wald statistic had been reported, which predictor would be most likely to be significant at p

Question

Questions pertain to the following table (Table 12), which presents results for a fictitious logistic regression analysis predicting acute confusion in long-term care residents.
 $\begin{array}{c}
\text { TABLE } 12\
\text { Predictors of Acute Confusion in Long-Term Care Residents }(N=2,100)\
\begin{array}{r}
\text { 95\% CI, Odds Ratio }\
\begin{array}{|l|c|c|c|c|c|}
\hline \text { Predictor }& \boldsymbol{b} & \text { SE }(b) & \text { Odds Ratio } & \text { Lower } & \text { Upper } \
\hline \text { Inadequate fluid intake } & 1.22 & .21 & 3.20 & 2.87 & 4.01 \
\text { Fell, past 30 days } & .45 & .20 & 1.68 & 1.37 & 2.05 \
\text { 5+ Medications } & .20 & .12 & 1.07 & .92 & 1.23 \
\text { Vision impairment } & .34 & .11 & 1.30 & 1.08 & 1.77 \
\hline
\end{array}\end{array}\end{array}$
Hosmer-Lemeshow chi-square $ =1.57, d f=5, p=.68 $
Nagelkerke $ R^{2}=.12 $
Percent correctly classified $ =68.9 \% $
-Refer to Table 12. If the Wald statistic had been reported, which predictor would be most likely to be significant at p < .001?

Quizplus · Accepted Answer

To determine which predictor would be most likely to be significant at p < .001, we would look for the predictor with the largest Wald statistic. The Wald statistic is calculated by dividing the squared value of the coefficient (b) by its variance (SE(b)^2). Since we do not have the Wald statistic for each predictor, we cannot determine with certainty which predictor would be most likely to be significant at p < .001. However, we can make an educated guess based on the odds ratio and confidence intervals. Inadequate fluid intake has the largest odds ratio and the tightest confidence interval, indicating a strong effect with little variability. Therefore, it is the most likely predictor to have a significant Wald statistic at p < .001.

Questions Pertain to the Following Table (Table 12), Which Presents