A set of elementary school student heights are normally distributed with a mean of 105105105 centimeters and a standard deviatio
2 answers:
Answer:
The proportion of student heights that are between 94.5 and 115.5 is 86.64%
Step-by-step explanation:
We have a mean and a standard deviation
. For a value x we compute the z-score as
, so, for x = 94.5 the z-score is (94.5-105)/7 = -1.5, and for x = 115.5 the z-score is (115.5-105)/7 = 1.5. We are looking for P(-1.5 < z < 1.5) = P(z < 1.5) - P(z < -1.5) = 0.9332 - 0.0668 = 0.8664. Therefore, the proportion of student heights that are between 94.5 and 115.5 is 86.64%
You might be interested in
Answer:
<em>H₀</em>: <em>μ</em>₁ = <em>μ</em>₂ vs, <em>Hₐ</em>: <em>μ</em>₁ > <em>μ</em>₂.
Step-by-step explanation:
A two-sample <em>z</em>-test can be performed to determine whether the claim made by the owner of pier 1 is correct or not.
It is provided that the weights of fish caught from pier 1 and pier 2 are normally distributed with equal population standard deviations.
The hypothesis to test whether the average weights of the fish in pier 1 is more than pier 2 is as follows:
<em>H₀</em>: The weights of fish in pier 1 is same as the weights of fish in pier 2, i.e. <em>μ</em>₁ = <em>μ</em>₂.
<em>Hₐ</em>: The weights of fish in pier 1 is greater than the weights of fish in pier 2, i.e. <em>μ</em>₁ > <em>μ</em>₂.
The significance level of the test is:
<em>α</em> = 0.05.
The test is defined as:
The decision rule for the test is:
If the <em>p</em>-value of the test is less than the significance level of 0.05 then the null hypothesis will be rejected and vice-versa.
From the equation of motion, we know,
Where s= displacement
u= initial velocity
a= gravitational force
t= time
Displacement is 0 since the ball comes back to the same point from where it was thrown.
A = since the ball is thrown upwards.
Plug the known values into the equation.
=>
Solving for u gives :
u= 16.67 m/ sec ....... equation (1)
At maximum height, final velocity i.e v is 0
Time take to reach the top =
=>
Solving for s we get
s= 14.16 m
Answer:
A. Predict a dichotomous variable from continuous or dichotomous variables.
Step-by-step explanation:
Logistic regression is used when you want to predict a dichotomous variable from continuous or dichotomous variables.
Mathematically, it is given by the expression;
Logistic regression with
,
........
Where;
y represents the dichotomous dependent variable.
,
........
represents the predictable variables, which are categorical in nature such as alive or dead, win or lose, sick or healthy, pass or fail, etc.