1. Since -4 doesn’t equal -2, we will use the first function. All you have to do is plug in -4 to x, and you should get
-3
2. -2=-2, so we will use the second function to get the answer of
3
3. Once again we will be using the first function, so just plug 5 into the answer to get
-7.5
Hope this helped :)
Answer:
General equation of line :
--1
Where m is the slope or unit rate
Table 1)
p d
1 3
2 6
4 12
d = Number of dollars (i.e.y axis)
p = number of pound(i.e. x axis)
First find the slope
First calculate the slope of given points
---A


Substitute values in A
Thus the unit rate is 3 dollars per pound.
So, It matches the box 1 (Refer the attached figure)
Equation 1 : 

Since p is the x coordinate and d is the y coordinate
On Comparing with 1

Thus the unit rate is
dollars per pound
So, It matches the box 2 (Refer the attached figure)
Equation 2 : 

Since p is the x coordinate and d is the y coordinate
On Comparing with 1

Thus the unit rate is 9 dollars per pound
So, It matches the box 3 (Refer the attached figure)
Table 2)
p d
1/9 1
1 9
2 18
d = Number of dollars (i.e.y axis)
p = number of pound(i.e. x axis)


Substitute values in A
Thus the unit rate is 9 dollars per pound
So, It matches the box 3 (Refer the attached figure)
The solving for the first one should be like this

And the next part

After these manipulations just multiply them

And finally you need to square and simplify for the completed answer
Sampling errorThe natural discrepancy, or amount of error, between a sample statistic and its corresponding population parameter.distribution of sample means<span>The collection of sample means for all of the possible random samples of a particular size (n) that can be obtained from a population.</span>sampling distributionA distribution of statistics obtained by selecting all of the possible samples of a specific size from a population.central limit theorem<span>For any population with mean μ and standard deviation σ, the distribution of sample means for sample size n will have a mean of μ and a standard deviation of σ/√n and will approach a normal distribution as n approaches infinity.</span><span>expected value of M</span>The mean of the distribution of sample means is equal to the mean of the population of scores, μ, and is called this.<span>standard error of M</span><span>The standard deviation for the distribution of sample means. Identified by the symbol σ˯M. This standard error provides a measure of how much distance is expected on average between a sample mean (M) and the population mean (μ).</span>law of large numbers<span>States that the larger the sample size (n), the more probable it is that the sample mean is close to the population mean.</span>