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AlladinOne [14]
3 years ago
12

A grocery store clerk put only packages of flour tortillas and packages of corn tortillas on a shelf. The ratio of the number of

packages of corn tortillas to the total number of packages on the shelf was 7 to 16. Which number could be the number of packages of flour tortillas the clerk put on the shelf?
Mathematics
1 answer:
Mazyrski [523]3 years ago
8 0

Answer:

9

Step-by-step explanation:

Let corn tortillas be represented by c, flour tortillas be represented by f and the total number of packages be t

such that

t = c + f     .... eq 1

ratio of c to t = 7 : 16 = 7/16

Therefore, t = 16,    c = 7

substitute the value of t in eq 1

16 =  7 + f

f = 16 - 7

f = 9

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Angle B has a measure between 0° and 360° and is coterminal with a –865° angle. What is the measure of angle B?
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Read 2 more answers
Suppose you are interested in the effect of skipping lectures (in days missed) on college grades. You also have ACT scores and h
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Answer:

a) For this case the intercept of 2.52 represent a common effect of measure for any student without taking in count the other variables analyzed, and we know that if HSGPA=0, ACT= 0 and skip =0 we got colGPA=2.52

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\hat \beta_{ACT} = 0.015

So then for every unit increase in the ACT score we expect and increase of 0.015 in the GPA or the predicted variable

c) 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_i = 0

Alternative hypothesis: \beta_i \neq 0

Or in other wouds we want to check if an specific slope is significant.

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

Th degrees of freedom for a linear regression is given by df=n-p-1 = 45-3-1 = 41, where p =3 the number of variables used to estimate the dependent variable.

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

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And replacing we got:

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Step-by-step explanation:

For this case we have the following multiple regression model calculated:

colGPA =2.52+0.38*HSGPA+0.015*ACT-0.5*skip

Part a

(a) Interpret the intercept in this model.

For this case the intercept of 2.52 represent a common effect of measure for any student without taking in count the other variables analyzed, and we know that if HSGPA=0, ACT= 0 and skip =0 we got colGPA=2.52

(b) Interpret \hat \beta_{ACT} from this model.

This value represent the effect into the ACT scores in the GPA, we know that:

\hat \beta_{ACT} = 0.015

So then for every unit increase in the ACT score we expect and increase of 0.015 in the GPA or the predicted variable

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For this case we can use the regression model and we got:

colGPA =2.52 +0.38*3.2 +0.015*25 - 0.5*4 = 26.751

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