Answer:
The standard deviation used in the test statistic is 0.04.
Step-by-step explanation:
A single proportion <em>z</em>-test can be used to determine whether the proportion of people in Norway with a blood type of A positive is different from that in the United States.
The proportion of people in Norway with a blood type of A positive is 36% or 0.36.
The hypothesis can be defined as:
<em>H₀</em>: The proportion of people in Norway with a blood type of A positive is 36%, i.e. <em>p</em> = 0.36.
<em>Hₐ</em>: The proportion of people in Norway with a blood type of A positive is different from 36%, i.e. <em>p</em> ≠ 0.36.
The test statistic is:
The information provided is:
<em>p</em> = 0.36
<em>n</em> = 150
Compute the standard deviation used in the test statistic as follows:
Thus, the standard deviation used in the test statistic is 0.04.
Answer:
Option (3) is correct.
side length of square is (3x + 13 ) units.
Step-by-step explanation:
For a square with side 'a'. Perimeter is defined as the sum of length of side. Since, Square has four sides. Thus Perimeter of square = 4 × side
Given square has perimeter = 12x + 52
Comparing both sides,
4 × side = 12 x + 52
⇒ Side =
⇒ Side =
⇒ Side = 3x + 13
Thus, side length of square is (3x + 13 ) units.
Thus, option (3) is correct.
Answer:
The probabilities of each outcome are the following:
for X = 1 is 0.6
for X = 2 is 0.35
for X = 3 is 0.049
and for X = 4 is 0.001
Step-by-step explanation:
Let's consider X as the random variable for the sum of outcomes "S" exceeds 3, this is:
Let's now analyze and consider the ways that X equals the different values:
X = 1: I throw the dice and the result is: 4, 5 and 6.
Then: P(X=1) = P(4) + P(5) + P(6) = 0.2+0.1+0.3 = 0.6
Thus P(X=1) = 0.6
X = 2: The result after following the dice two times can be:
1 and 3, 1 an 4.... and so on until 1 and 6
2 and 2, 2 and 3... and so on until 2 and 6
3 and 1... and so on until 3 a 6
Then P(X=2) = P(1)xP(3,4,5,6) + P(2)xP(2....6) + P(3)x(1.....6)
Theory of Probability: Sum of all possible outcomes P(1)+P(2).......P(6) = 1
Then P(1......6) = 1
Then P(X=2) = P(1)x[1-P(1)-P(2)]+P(2)x[1-P(1)]+P(3) = 0.1x(1-0.1-0.2) + 0.2x(1-0.1) + 0.1 = 0.1 x 0.7 + 0.2 x 0.9 + 0.1 = 0.35
Thus P(X=2) = 0.35
X = 3: The result can be
1 and 1 and 2, 1 and 1 and 3.... until 1 and 1 and 6
1 and 2 and 1, 1 and 2 and 2..... until 1 and 2 and 6
2 and 1 and 1, 2 and 1 and 2.... until 2 and 1 and 6
Then P(X=3) = P(1)xP(1)xP(2....6) + P(1)xP(2)xP(1.....6) + P(1)xP(2)xP(1.....6)
P(X=3) = 0.1 x 0.1 x (1-0.1) + 0.1 x 0.2 x 1 + 0.2 x 0.1 x 1 = 0.01 x 0.9 + 0.2 + 0.2 = 0.049
Thus P(X=3) = 0.049
Finally, for X to be 4, I only have the following possibilities
1 and 1 and 1 and 1.... until 1 and 1 and 1 and 6
Then P(X=4) = P(1)xP(1)xP(1)xP(1.....6) = 0.1x0.1x0.1x1 = 0.001
Thus P(X=3) = 0.001
Answer:
The residual value is -1.8 when x = 3
Step-by-step explanation:
We are given the following table
x | y
0 | -3
2 | -1
3 | -1
5 | 5
6 | 6
Residual value:
A residual value basically shows the position of a data point with respect to the line of best fit.
The residual value is calculated as,
Residual value = Observed value - Predicted value
Where observed values are already given in the question and the predicted values are calculated by using the equation of line of best fit.
When we substitute x = 3 in the above equation then we would get the predicted value.
So the predicted value is 0.8
From the given table, the observed value corresponding to x = 3 is -1
So the residual value is,
Residual value = Observed value - Predicted value
Residual value = -1 - 0.8
Residual value = -1.8
Therefore, the residual value is -1.8 when x = 3
Note: A residual value closer to 0 is desired which means that the regression line best fits the data.