Answer:
The 95% confidence interval for the true average number of homes that a person owns in his or her lifetime is (4,6.2).
Step-by-step explanation:
We have the standard deviation for the sample, which means that the t-distribution is used to solve this question.
The first step to solve this problem is finding how many degrees of freedom,which is the sample size subtracted by 1. So
df = 50 - 1 = 49
95% confidence interval
Now, we have to find a value of T, which is found looking at the t table, with 49 degrees of freedom(y-axis) and a confidence level of 
. So we have T = 2.0096
The margin of error is:
In which s is the standard deviation of the sample and n is the size of the sample.
The lower end of the interval is the sample mean subtracted by M. So it is 5.1 - 1.1 = 4
The upper end of the interval is the sample mean added to M. So it is 5.1 + 1.1 = 6.2.
The 95% confidence interval for the true average number of homes that a person owns in his or her lifetime is (4,6.2).
C = 11*(2*d) Try it and see that it fits every sample you have. Again this can be simplified to
C = 22 * d
a. 1.15
b. 6.20 divided by 4 = 1.55
c. 5 divided by 4 = 1.25
d. 5.50 divided by 5 = 1.1
D is correct
So there's 8 tiles.
Radius from archway to the outer edge would be : 7 +1 = 8ft
You can use this formula to count the area of semi circle : π r^2/2
A = 3.14 x 8ft^2 = 100.48 ft ^2
A = 3.14 x 7ft^2= 76.92 ft^2
100.48 - 76.92 = 23.55 ft^2
Then you have to divide it with the total tiles
23.55/8 = 2.94 ft^2 per tile >> (rounded to nearest tenth)
hope this helps
<h2>
Answer with explanation:</h2>
When there is a linear relationship is observed between the variables, we use linear regression predict the relationship between them.
Also, we predict the values for dependent variable by modelling a linear model that best fits the data by drawing a line Y=a+bX, where X is the explanatory variable and Y is the dependent variable.
In other words: The line of best fit is a line through a scatter plot of data points that best describes the relationship between them.
That's why the regression line referred to as the line of best fit.
