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zubka84 [21]
3 years ago
13

Equations is the inverse of y = x^2-5?

Mathematics
1 answer:
Fynjy0 [20]3 years ago
6 0

Answer:

Step-by-step explanation:

1.  Interchange x and y.  We get:  x = y^2 - 5

2. Solve for y:  y^2 = x + 5, so that y = ±√(x + 5)

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It takes 5 days to repair 1,250 meters of road. At this rate, how many days will it take to repair 8 kilometers of road?
dolphi86 [110]

Answer :

no. of days used to repair 1250m of road = 5

no. of days used to repair 1m of road = 5/1250

no. of days used to repair 8000m of road (8*1000m ) = 5/1250*8000= 32 days

6 0
3 years ago
Read 2 more answers
Please help I’m literally slow
trapecia [35]

Answer:

1/5^7 (the last one)

Step-by-step explanation:

When dividing exponents you subtract the the two exponents. In this case you subtract 3 from -4. -4-3=-7. So you have 5^-7. That is also equal to 1/5^7

4 0
4 years ago
What are the x and y intercepts of 3y=2x-6
sasho [114]
How to find the x intercept of 3y=2x-6: (when y is 0)

3(0) = 2x - 6

     0 = 2x - 6
   +6        + 6
-------------------------
     6 = 2x
  ------ -----
     2     2
      
     x = 3


How to find the y intercept of 3y=2x-6: (when x  is 0)

 3y = 2(0) - 6

 y = 0 - 6

  3y = -6
------  -----
   3      3

   y = -2















8 0
3 years ago
Help me out here pleaseeeeeeeeeee
Drupady [299]

Answer:

22 feet

Step-by-step explanation:

33 * 2/3=22

Hope I helped!

5 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
3 years ago
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