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Feliz [49]
3 years ago
6

The graph below shows a line of best fit for data collected on the number of dogs visiting a dog park since it first opened.

Mathematics
1 answer:
aleksandrvk [35]3 years ago
8 0

Hello!

Usually in a slope intercept equation, the y-intercept is a term, and is not a coefficient of x. The slope is usually multiplied by x. This eliminates A and C as answers.

Usually the y-intercept is a starting point. Not the slope. This means that B is also incorrect.

In D, it has the correct y-intercept. It shows that 16 dogs visited the first day, and the slope shows a constant rate of more dogs.

Therefore, our answer is D.

I hope this helps!

You might be interested in
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
3 years ago
Marshall está recolectando leche de las granjas lecheras que se tomará y se convertirá en queso. En un mapa, la distancia de una
exis [7]
I don’t know my bad lol
4 0
1 year ago
Only do 7. iii<br>I will mark you as brainliest.. I don't have time please fasttt​
Sophie [7]

Answer:

Check my explanation

Step-by-step explanation:

(i) the area of the floor is 30m² (6m · 5m)

(ii) the perimeter of the floor is 22m (6m + 5m + 6m + 5m)

(iii) the area of the walls is:

     24m² (for the 6m by 4m walls)

     20m² (for the 5m by 4m walls)

     88m² in total total

7 0
3 years ago
A media company wants to track the results of its new marketing plan, so the video production manager recorded the number of vie
Alisiya [41]

Answer:

f(x)=5120(1.25)^{x}

Step-by-step explanation:

As per the given question

As we know that

f(x) and x are related by following equation

f(x)=a( {b})^{x}

where

b is the common ratio

Now first we have to compute the value for b which are as follows

\frac{6400}{5120}= 1.25

\frac{8000}{6400}= 1.25

Like if we take the ratio by this method than it comes 1.25

Therefore the common ratio, b = 1.25

So,

a = 5,120

Hence, the equation which model the relationship between the number of weeks and the number of viewers is

f(x)=5120(1.25)^{x}

8 0
3 years ago
Read 2 more answers
PLEASE HURRY! this is financial algebra
Zina [86]

Answer:

6 years

Step-by-step explanation:

Simple interest= P.R.T

I = $1300

P = 18000 deposit

Rate = 1.19%

T = ?

Putting into the formula we have

1300 = 18000x1.19/100xT

1300 = 18000x0.0119xT

1300 = 214.2T

Divide through to get T

T = 1300/214.2

= 6.069

So when we approximate T = 6 years

It would take 6 years to make $1300 in interest

7 0
3 years ago
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