How much 1 : 25 solution and 1 : 500 solution should you mix to make 1 L of a 1 : 250 soaking solution? Answers are rounded to the nearest whole number.
947 mL of the 1 : 25 solution and 53 mL of the 1 : 500 solution
750 mL of the 1 : 25 solution and 250 mL of the 1 : 500 solution
250 mL of the 1 : 25 solution and 750 of the 1 : 500 solution
53 mL of the 1 : 25 solution and 947 mL of the 1 : 500 solution
The Correct Answer is D
Step 1. Convert ratios to percentages.
1 : 25 = (1/25) x 100 = 4%
1 : 500 = (1/500) x 100 = 0.2%
1 : 250 = (1/250) x 100 = 0.4%
Step 2. Set up alligation.
Higher: 4%
Lower: 0.2%
Target: 0.4%
Step 3. Calculate parts.
Parts of 4% = 0.4 - 0.2 = 0.2 parts
Parts of 0.2% = 4 - 0.4 = 3.6 parts
Total parts = 0.2 + 3.6 = 3.8 parts
Step 4. Calculate volumes for 1000 mL (1 L).
Vol of 1 : 25 (4%) = (0.2 / 3.8) x 1000 = 52.63 mL (rounds to 53 mL)
Vol of 1 : 500 (0.2%) = (3.6 / 3.8) x 1000 = 947.37 mL
(rounds to 947 mL)
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Related Questions
Correct Answer is E
Explanation
Step 1 is to calculate the molecular weight (MW) of KCl
MW = K (39) + Cl (35.5)
39 + 35.5 = 74.5
Result at this step = 74.5 g/mol
Step 2 is to convert grams (g) to milligrams (mg)
mg = 0.5 g × 1,000
0.5 × 1,000 = 500
Result at this step = 500 mg
Step 3 is to calculate the milliequivalents (mEq)
Formula: mEq = (mg × valence) ÷ MW
Valence of KCl = 1
mEq = (500 × 1) ÷ 74.5
500 × 1 = 500
(500 ÷ 74.5) = 6.7114
Result at this step = 6.7114 mEq
Step 4 is to round to the nearest whole number
6.7114 ≈ 7
Answer: 7
Correct Answer is C
Explanation
In biostatistics and clinical research, variables are analyzed to determine the complexity of causal relationships. While independent variables influence outcomes, other factors can significantly impact the nature of that influence. Identifying these factors helps researchers understand for whom or under what conditions an intervention is effective. This distinction is critical for stratified analysis and the development of personalized medicine based on specific patient characteristics.
Rationale:
A. A variable not included in the analysis that leads to residual confounding is known as a confounder or a lurking variable. Confounders distort the true relationship because they are associated with both the exposure and the outcome. This is different from a moderator, which is typically measured and analyzed to understand interaction effects. Neglecting such variables leads to systematic bias.
B. An intermediate variable that links a primary independent variable to a dependent variable is defined as a mediator. Mediators explain "why" or "how" an effect occurs by providing a biological or logical pathway. For example, exercise decreases heart disease through the mediator of weight loss. A moderator, however, does not link the two; it changes the interaction intensity.
C. A moderator variable is a factor that influences the strength or direction of the effect between the independent and dependent variables. It answers the question of "when" or "for whom" a relationship exists. For example, a drug might work better in men than in women; here, gender is the moderator. This represents a statistical interaction effect that refines clinical findings.
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