A normal distribution is a type of continuous probability distribution for a real-valued random variable in statistics.
Yes, the large-sample confidence interval will be valid.
<h3>What is meant by normal distribution?</h3>
A normal distribution is a type of continuous probability distribution for a real-valued random variable in statistics.
The normal distribution, also known as the Gaussian distribution, is a symmetric probability distribution about the mean, indicating that data near the mean occur more frequently than data far from the mean.
The confidence interval will be valid regardless of the shape of the population distribution as long as the sample is large enough to satisfy the central limit theorem.
<h3>
What does a large sample confidence interval for a population mean?</h3>
A sample is considered large when n ≥ 30.
By 'valid', it means that the confidence interval procedure has a 95% chance of producing an interval that contains the population parameter.
To learn more about normal distribution, refer to:
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Newton's Law of Cooling
Tf=Ts+(Ti-Ts)e^(-kt) where Tf is temp at time t, Ts is temp of surroundings, Ti is temp of object/fluid. So we need to find k first.
200=68+(210-68)e^(-10k)
132=142e^(-10k)
132/142=e^(-10k)
ln(132/142)=-10k
k=-ln(132/142)/10
k≈0.0073 so
T(t)=68+142e^(-0.0073t) so how long until it reaches 180°?
180=68+142e^(-0.0073t)
112=142e^(-0.0073t)
112/142=e^(-0.0073t)
ln(112/142)=-0.0073t
t= -ln(112/142)/(0.0073)
t≈32.51 minutes
Step-by-step explanation:
Erase the dot points you already have. We are supposed to substitute those values in the right side of problem 1 into the function as x.
For example if x=-4
If x=-2
If x=0
So our point should be
-4,9
-2,7
0,5
-2,3
-4,1.
The range is all possible y values in a function. Since this is discrete and we are given the domain, our range will just be the y value of the points you graphed.
(9,7,5,3,1)
Answer:
Option (A)
Step-by-step explanation:
There are 2 main branches of statistics. They are:
1. Descriptive Statistics
2. Inferential Statistics
Descriptive statistics describes the characteristics of observed subjects or items while Inferential statistics makes inferences, based on given or derived data.
Inferential Statistics allow you to decide whether a difference between the experimental group and control group is due to <u>manipulation or chance.</u>
<u />
The Experimental group is the group that an effect is tested on while the Control group is the group that is left untested or uninfluenced. Inferential statistics allow you to decide whether a difference between these 2 groups is due to
- manipulation or interference by any force (which may be the experimenter/researcher)
or
- probability which is chance.
