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Eddi Din [679]
2 years ago
8

Ordered benedryl syrup 50mg po qid. available 4 g in a 8 oz. bottle. how many mls will you give?

Mathematics
1 answer:
daser333 [38]2 years ago
8 0

The person who administers the drug will deliver:

  • 2.956 ml or approximately, 3mls of the drug.

<h3>How is dosage administration calculated?</h3>

Dosage administration is calculated by the formula:

D/H  × Q = X

Where;

D = Amount of the drug ordered by the physician

H =  Dosage strength or the supply currently available

Q = Unit being measured

X = The unknown dosage

Now, or the figures are converted to ml

50 mg = 0.05 ml

4 g = 4ml

8 oz = 236.56 ml

Plugging these into the formula, the unknown quantity, X will be

= 0.05 ml/ 4m × 236.56 ml

= 2.956 ml.

Learn more about dosage calculations here:

brainly.com/question/15820970

#SPJ4

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I need help with the equation.
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Answer:

(x - 3)² + (y + 9)² = 25

Step-by-step explanation:

The equation of a circle in standard form is

(x - h)² + (y - k)² = r²

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Read 2 more answers
An article reports the following data on yield (y), mean temperature over the period between date of coming into hops and date o
skelet666 [1.2K]

Answer:

x1=c(16.7,17.4,18.4,16.8,18.9,17.1,17.3,18.2,21.3,21.2,20.7,18.5)

x2=c(30,42,47,47,43,41,48,44,43,50,56,60)

y=c(210,110,103,103,91,76,73,70,68,53,45,31)

mod=lm(y~x1+x2)

summary(mod)

R output: Call:

lm(formula = y ~ x1 + x2)

Residuals:  

   Min      1Q Median      3Q     Max

-41.730 -12.174   0.791 12.374 40.093

Coefficients:

        Estimate Std. Error t value Pr(>|t|)    

(Intercept) 415.113     82.517   5.031 0.000709 ***  

x1            -6.593      4.859 -1.357 0.207913    

x2            -4.504      1.071 -4.204 0.002292 **  

---  

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Residual standard error: 24.45 on 9 degrees of freedom  

Multiple R-squared: 0.768,     Adjusted R-squared: 0.7164  

F-statistic: 14.9 on 2 and 9 DF, p-value: 0.001395

a).  y=415.113 +(-6.593)x1 +(-4.504)x2

b). s=24.45

c).  y =415.113 +(-6.593)*21.3 +(-4.504)*43 =81.0101

residual =68-81.0101 = -13.0101

d). F=14.9

P=0.0014

There is convincing evidence at least one of the explanatory variables is significant predictor of the response.

e).  newdata=data.frame(x1=21.3, x2=43)

# confidence interval

predict(mod, newdata, interval="confidence")

#prediction interval

predict(mod, newdata, interval="predict")

confidence interval

> predict(mod, newdata, interval="confidence",level=.95)

      fit      lwr      upr

1 81.03364 43.52379 118.5435

95% CI = (43.52, 118.54)

f).  #prediction interval

> predict(mod, newdata, interval="predict",level=.95)

      fit      lwr      upr

1 81.03364 14.19586 147.8714

95% PI=(14.20, 147.87)

g).  No, there is not evidence this factor is significant. It should be dropped from the model.

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Answer:

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Step-by-step explanation:

The problem statement tells you the independent variable w represents weeks that have passed. "Domain" refers to values the independent variable may have, so choices A or B make no sense here.

Time is measured continuously, and fractions of a week are possible. So, the domain could be <em>non-negative real numbers</em>. However, the answer choice D is "<em>all</em> real numbers", which includes negative numbers for which the function makes no sense.

The domain "all whole numbers" includes non-negative integers. It is reasonable to restrict the domain to non-negative integer numbers of weeks, so answer choice C is the best option.

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