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SVETLANKA909090 [29]
2 years ago
10

That's the answer... I don't understand how you get -43a and -6

Mathematics
2 answers:
Roman55 [17]2 years ago
4 0
You want the explanation. Or answer?
kobusy [5.1K]2 years ago
4 0
Distribute 8a by 5a and because there's two a's its a squared then distribute 8a by -6 which gives you -48a then you distribute the positive 1 by 5a which gives you 5a then distribute it the positive 1 by -6 and then combine like terms
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GET POINTS look at pictur 15 POINTS
kap26 [50]
2. y=2x+3 

When x =0 and y= something, the 'something' is your y-intercept which is b in the y=mx+b formula.  Then we look at what is changed in the x and y value to determine the m part.  the x increase by 1 and y increase by 2 so the m part is 2. 

3. y=-7x 
Same as the last one, there is no y-inter this time because the y=0 when x=0/  So the y deceased by 7 each time and x incease by 1 so it is -7 for the m part.

6 0
2 years ago
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Solve the equations to find the number and type of solutions
luda_lava [24]

Answer:

This has one real solution, x=4

Step-by-step explanation:

8 - 4x = 0

Add 4x to each side

8 - 4x+4x = 0+4x

8 =4x

Divide each side by 4

8/4 = 4x/4

2 =x

This has one real solution, x=4

6 0
3 years ago
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How would you read this inequality? Answer in a sentence. x<21
Lubov Fominskaja [6]

Answer:

x is less than twenty one

5 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
How do I solve this problem <br> 15 = w + 4
vova2212 [387]

Answer:

w = 11

Step-by-step explanation:

<u>Step 1:  Subtract 4 from both sides</u>

15 = w + 4

15 - 4 = w + 4 - 4

11 = w

Answer: w = 11

3 0
3 years ago
Read 2 more answers
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