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yan [13]
3 years ago
7

Edgar is scuba diving 50 feet below the water. He is swimming to the surface at a rate of 3 feet per second. Write an equation t

o represent his depth, d, after s seconds.
Fill in the blank
d = __ s + __
(also to understand better we are doing the equation y = __ x + __ )
Mathematics
1 answer:
____ [38]3 years ago
5 0

Answer:

I know this is late but the actual answer is d=3s+-50

Step-by-step explanation:

The reason is because he is swimming down, so yeah.

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Lisa recorded a 2-hour television show. When she watched it, she skipped the commercials. It took her
Alexxx [7]

Answer:

36 minutes

Step-by-step explanation:

2 hours in minutes is 120

120-84

36 minutes

8 0
3 years ago
Find vertex for y=x^2 - 10x + 2
NARA [144]
The vertex would be (5,-23)
8 0
2 years ago
It costs a family $216 to refinish a wood floor. They want to refinish floor in a larger room. The ratio of corresponding sides
Brut [27]
D 288 because 216/3 equals 72 times that by 4 and you get 288.
5 0
3 years ago
Write an explicit formula for the recursive formula <br><br> A(n) = A(n - 1) + 3; A(1) = 6
arsen [322]

Answer:

a_{n} = 3n + 3

Step-by-step explanation:

The sequence is arithmetic with explicit formula

a_{n} = a₁ + (n - 1)d

where a₁ is the first term and d the common difference

From the recursive formula

a₁ = 6 and d = 3 [ the constant being added to A(n - 1) ] , then

a_{n} = 6 + 3(n - 1) = 6 + 3n - 3 = 3n + 3

7 0
2 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
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