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goldfiish [28.3K]
2 years ago
14

Can you help me please​

Mathematics
1 answer:
Volgvan2 years ago
3 0
D
Area of a circle is: 兀r✖️r, so 4.1m ➗2 = 2.05, 2.05✖️2.05 = 4.2025
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Barrett works at an ice cream shop. The function f(x) represents the amount of money Barrett earns per gallon of ice cream, wher
Sliva [168]

The value of f(g(x)) is 6x^{3} +4

Explanation:

The function f(x) is given by

f(x)=2x^{2} +4

The function g(x) is given by

g(x)=\sqrt{3x^{3} }

To determine the value of f(g(x)), let us first substitute the value of g(x).

Thus, we get,

f(g(x))=f(\sqrt{3x^{3} } )

Now, substituting for x=\sqrt{3x^{3} } in the function f(x), we get,

f(g(x))=2(\sqrt{3x^{3} } )^2+4

Simplifying, the expression, we get,

f(g(x))=2({3x^{3} } )+4

Multiplying the term within the bracket, we have,

f(g(x))=6x^{3} +4

Thus, the value of f(g(x)) is 6x^{3} +4

Hence, the value of f(g(x)) represents the total amount of money that Barrett earns for working x hours.

4 0
3 years ago
Suppose you are interested in the effect of skipping lectures (in days missed) on college grades. You also have ACT scores and h
DIA [1.3K]

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

b) 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

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:

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

And replacing we got:

t = \frac{-0.5}{0.0001}=-5000

And for this case we see that if we find the p value for this case we will get a value very near to 0, so then we can conclude that this coefficient would be significant for the regression model .

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

(c) What is the predicted college GPA for someone who scored a 25 on the ACT, had a 3.2 high school GPA and missed 4 lectures. Show your work.

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

(d) Is the estimate of skipping class statistically significant? How do you know? Is the estimate of skipping class economically significant? How do you know? (Hint: Suppose there are 45 lectures in a typical semester long class).

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:

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

And replacing we got:

t = \frac{-0.5}{0.0001}=-5000

And for this case we see that if we find the p value for this case we will get a value very near to 0, so then we can conclude that this coefficient would be significant for the regression model .

7 0
2 years ago
Please help please please please
kap26 [50]
I'm pretty sure it is 1 out of nine or 1 out of 8.
5 0
3 years ago
Someone please help me
Ugo [173]

Answer:

1,-4

Step-by-step explanation:

I hope it helps because we did that in class

5 0
3 years ago
What type of correlation would you expect to see between the number of purchased and the amount of money saved?
goblinko [34]
A negative correlation
5 0
3 years ago
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