Best Answer:<span> </span><span>(x - 5)^2 = 9
x - 5 = ±√9
x = 5 ± 3
x = 2, 8</span>
A 99% confidence interval for the population mean of high school students that take the bus to school every day is b) ci=(44.34%, 54.43%)
We need to find the 99% confidence interval for the population mean of high school students that take the bus to school everyday
A confidence interval is a range of estimates for an unknown parameter. The confidence interval is calculated at the specified confidence level; the most common is the 95% confidence level, but sometimes other levels are used, such as 90% or 99%.
The confidence interval of proportions is given by:
π ± z √(π (1-π) /n)
π is the sample proportion.
z is the critical value.
n is the sample size.
For 99% confidence interval the value of z is 2.58
π = 321/650
The confidence interval is given by
=321/650 ± 2.58 √( (321/650) × [ 1 - (321/650) ] ÷ 650)
= (0.493846 ± 0.050594)
=(0.4434 , 0.5443)
=(44.34 % , 54.43 %)
Hence a 99% confidence interval for the population mean of high school students that take the bus to school every day is ci=(44.34%, 54.43%)
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Answer:
1 2/21
Step-by-step explanation:
We start out with:
3/7 + 2/3
In order to add them together, they must have an equal denominator. An easy way to find an equal denominator is the cross method. We divide 7 by 3 and 3 by 7, giving us a denominator of 21:
3/21 + 2/21
But what about the numerators? You do the same thing as before (multiplying by the other's denominator). Now we have:
9/21 + 14/21
We add both together (the denominator stays the same) and we get 23. 23/21 is correct but not in simplest form. We remove 21 from the numerator (because it is a whole) and finally get:
1 2/21!
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The linear regression method seeks to predict values of a(n) dependent variable based on values of a(n) independent variable.
According to the statement
we have to explain the linear regression method and explain the way by which this method is used to predict the values.
So, For this purpose we know that the
Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship.
And
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
from these definitions it is clear that the there is a presence of two types of variables which are dependent and independent variables.
So, The linear regression method seeks to predict values of a(n) dependent variable based on values of a(n) independent variable.
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Answer:
2*2h4g15
Step-by-step explanation:
count how many "g"s there are and same with "h"
