Find the common difference for the given sequence<br> 1.2, 1.85, 2.5, 3.15, ...

Each player to hit the balloon up and it bonce into the air but when one should not allow it to touch the ground.,
Players would be tied together in twos and they will juggle a lot of balloon and it have to be more than 1 balloon with one of their hands tied to their back.

A scenario of the worksheet game whose expected value is 0 is given below:

Assume that it costs about $1 for a player to play the billon game and as such, if the player hits a balloon, they will be given $3. what can you say. Can you say that it this game is fair or not? and who has the biggest advantage.

Solution

Note that a game is ”fair” if the expected value is said to be 0. When a player is said to hits a balloon, their net profit often increase by $4. So when the player do not hit a balloon, it drops to $1.

(4)(0.313) + (-1)(0.313)

= 0.939 approximately

Thus, the expected value is $0.939 which tells that the game is fair.

Learn more about fair play from

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4 0

2 years ago

(07.05)

Llana [10]

Answer: n = -2

Step-by-step explanation:

3n +2(n + 2) = 9n + 12

(simplify 2(n + 2)) =

3n + 2n + 4 = 9n + 12

(subtract 9n from both sides)

3n + 2n - 9n + 4 = 12

-4n + 4 = 12

(subtract 4 from both sides)

-4n = 8

(divide -4 from both sides)

n = -2

5 0

3 years ago

Read 2 more answers

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: