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lora16 [44]
2 years ago
12

A punch recipe requires 2/5 of a cup of pineapple juice for every 2 1/2 cups of soda. What is the unit rate of soda to pineapple

juice in the punch?
Mathematics
2 answers:
Sergio [31]2 years ago
8 0
2 1/2 = 5/2 
<span>ratio : 5/2 : 3/5 (soda to pineapple) </span>
<span>multiply by 2/5 </span>

<span>ratio : 1 : 6/25 (1 unit of soda to 6/25 units of pineapple)</span>
ArbitrLikvidat [17]2 years ago
5 0

Answer:

unit rate of soda to pineapple juice=\frac{25}{4}

Step-by-step explanation:

In this question we have given

amount of pineapple juice in recipe=\frac{2}{5}

amount of pineapple soda in recipe=\frac{5}{2}We have to find the unit rate of soda to pineapple juice in the punch which can be determined by deviding the quantity of soda by quantity of pineapple in the punch

Therefore,

unit rate of soda to pineapple juice=\frac{\frac{5}{2} }{\frac{2}{5} }

unit rate of soda to pineapple juice=\frac{25}{4}

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A member of a student team playing an interactive marketing game received the fol- lowing computer output when studying the rela
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Answer:

p_v = 2*P(t_{n-2} > |t_{calc}|)= 0.91

So on this case for the significance level assumed \alpha=0.05 we see that p_v >\alpha so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.

So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.

Step-by-step explanation:

Let's suppose that we have the following linear model:

y= \beta_o +\beta_1 X

Where Y is the dependent variable and X the independent variable. \beta_0 represent the intercept and \beta_1 the slope.  

In order to estimate the coefficients \beta_0 ,\beta_1 we can use least squares procedure.  

If we are interested in analyze if we have a significant relationship between the dependent and the independent variable we can use the following system of hypothesis:

Null Hypothesis: \beta_1 = 0

Alternative hypothesis: \beta_1 \neq 0

Or in other words we want to check is our slope is significant (X have an effect in the Y variable )

In order to conduct this test we are assuming the following conditions:

a) We have linear relationship between Y and X

b) We have the same probability distribution for the variable Y with the same deviation for each value of the independent variable

c) We assume that the Y values are independent and the distribution of Y is normal  

The significance level assumed on this case is \alpha=0.05

The standard error for the slope is given by this formula:

SE_{\beta_1}=\frac{\sqrt{\frac{\sum (y_i -\hat y_i)^2}{n-2}}}{\sqrt{\sum (X_i -\bar X)^2}}

Th degrees of freedom for a linear regression is given by df=n-2 since we need to estimate the value for the slope and the intercept.  

In order to test the hypothesis the statistic is given by:

t=\frac{\hat \beta_1}{SE_{\beta_1}}

The p value on this case would be given by:

p_v = 2*P(t_{n-2} > |t_{calc}|)= 0.91

So on this case for the significance level assumed \alpha=0.05 we see that p_v >\alpha so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.

So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.

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Answer:

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Step-by-step explanation:

We are given;

Mass of bucket = 30 kg

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height of building= 30 meter

Now,

work done lifting the bucket (sand and rope) to the building = work done in lifting the rope + work done in lifting the sand

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Work done in lifting the rope is given as,

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F = 0.5x + 15

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W = 765 J

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