All categories

3 years ago

Pahelppo plss plsshele me

SAT

2 answers:

MAVERICK [17]3 years ago

5 0

answer is 21 toothpicks

RUDIKE [14]3 years ago

4 0

Answer:

for every new hexagon, you just add 5 toothpicks, that's even true for the first one, if we lay down one starter toothpick before the first step

then we can write it as s formula:

t(x) = 1 + 5x

wich gives us the required number of toothpicks by plugging in the desired ammount of hexagons (x)

t(5) = 1 + 5*5 = 25

t(12) = 1 + 5*12 = 61

76 = 1 + 5*x

75 = 5x

15 = x

he can build 15 hexagons

101 = 1 + 5x

100 = 5x

20 = x

he can build 20 hexagons

hope it helps. have a great day

You might be interested in

i went out today with cuffed jeans and a tucked in shirt... i had a snapback hat on thumbrings... i had maybe 7 girls ask me if

Harman [31]

I hate these stereotypes because people think "this is how you should dress if you are this." But that's not the case. People have different tastes in clothing and this is also a good example of the First Amendment: Freedom of Expression. People can express themselves however they want to! Nobody NEEDS to fit into these stereotypes nor should they fit into them.

4 0

2 years ago

What is national tree of japan?

iren [92.7K]

The Japanese Red Cedar Sugi is Japan’s national tree!

6 0

3 years ago

Find the residual values, and use the graphing calculator tool to make a residual plot.

finlep [7]

Answer:

No, the points are evenly distributed about the x-axis.

Explanation:

1. Write the table with the data:

x given predicted residual

1 - 3.5 - 1.1

2 - 2.9 2

3 - 1.1 5.1

4 2.2 8.2

5 3.4 1.3

2. Complete the column of residuals

The residual is the observed (given) value - the predicted value.

residual = given - predicted.

Thus, the complete table, with the residual values are:

x given predicted residual

1 - 3.5 - 1.1 - 2.4

2 - 2.9 2 - 4.9

3 - 1.1 5.1 - 6.2

4 2.2 8.2 - 6.0

5 3.4 1.3 2.1

3. Residual plot

You must plot the last column:

x residual

1 - 2.4

2 - 4.9

3 - 6.2

4 - 6.0

5 2.1

See the plot attached.

Does the residual plot show that the line of best fit is appropriate for the data?

Ideally, a residual plot for a line of best fit that is appropiate for the data must not show any pattern; the points should be randomly distributed about the x-axis.

But the points of the plot are not randomly distributed about the x-axis: there are 4 points below the x-axis and 1 point over the x-axis: there are more negative residuals than positive residuals. This is a pattern. Also, you could say that they show a curve pattern, which drives to the same conclusion: the residual plot shows that the line of best fit is not appropiate for the data.

Thus, the conclusion should be: No, the points have a pattern.

1. "Yes, the points have no pattern": false, because as shown, the points do have a pattern, which makes the residual plots does not show that the line of best fit is appropiate for the data.

2. "No, the points are evenly distributed about the x-axis": true. As already said the points have a pattern. It is a curved pattern, and this shows the line of best fit is not appropiate for the data.

3. "No, the points are in a linear pattern": false. The points are not in a linear pattern.

4. "Yes, the points are in a curved pattern": false. Because the points are in a curved pattern, the residual plot shows that the line of best fit is not appropiate for the data.