Sampling distributions

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Intros
Lessons
  1. What is sampling?
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Examples
Lessons
  1. Understanding Proportions and Sample Size
    There are 7 supercars competing on a racetrack. The cars are labelled {1, 2, 3, 4, 5, 6, 7}.
    Find,
    1. The proportion of odd numbered cars
    2. How many different samples are there if we chose samples of 3 cars
      i. With replacement?
      ii. Without replacement?
  2. Connecting Sample Proportions to Population Proportions
    Alice, Bob, Cole, Daisy, and Eve are all the students in a 10th grade class. Alice, Cole and Eve brought a lunch to class while the rest of the class did not.
    1. Find the proportion of students who brought a lunch to class.
    2. Make a list of all samples of students of size 2
    3. Find the probability of each sample occurring.
    4. What is the sample proportion of each sample?
    5. Find the average of all the sample proportions and explain the significance of your findings.
  3. Connecting Sample Means to the Population Mean
    Uncle Sammy grows prize winning big zucchinis. The weights of his four largest zucchinis are given in the table below:

    Zucchini:

    A

    B

    C

    D

    Weight:

    7 lbs

    9 lbs

    11 lbs

    5 lbs

    1. What is the average weight of all the zucchinis Uncle Sammy grows?
    2. Make a list of all samples of size 2 for the zucchinis Uncle Sammy grows.
    3. What is the probability that each sample gets picked?
    4. What is the average weight of each sample?
    5. Find the average weight of all the sample means.
Topic Notes
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Sampling distributions

A sampling distribution is a probability distribution of a statistic obtained through a large number of samples drawn from a specific population. The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population.

Sample proportion will estimate population proportion
Sample mean will estimate population mean

N=population  size\quad N = population \;size
n=sample  size\quad n = sample \;size
μ=population  mean\quad \mu = population \;mean
x=sample  mean\quad \overline{x} = sample \;mean
p=population  proportion\quad p = population \;proportion
p^=sample  proportion\quad \hat{p} = sample\; proportion

Sampling distribution model


Example 1

Understanding Proportions and Sample Size
There are 7 supercars competing on a racetrack. The cars are labelled {1, 2, 3, 4, 5, 6, 7}.
Find:

a) \quad The proportion of odd numbered cars
From our population of cars: car 1, car 2, car 3, car 4, car 5, car 6 and car 7, there are only 4 odd numbered cars out of the seven cars in the population; therefore, the population proportion of odd numbered cars is just 4 out of 7, which is written as a ratio as follows:

p=47 \large p =\frac{4}{7}
Equation 1: Proportion of odd numbered cars in the population

b) \quad How many different samples are there if we chose samples of 3 cars

i) \quad With replacement?
If the sample that we will take out of the population of 7 cars mentioned above will contain 3 cars, and with replacement, we have 7 different possible cars as outcomes for each of these 3 cars; the amount of different possible samples that could be obtained is calculated by multiplying the 7 possible outcomes for each car that will be picked:

different samples with replacement =777=73=343 =777=7^{3} =343
Equation 2: Different possible samples with replacement

And so, there are a total of 343 possible different samples of the 7-car population when it is allowed to repeat cars within the sample (with replacement).

ii) \quad Without replacement?
Without replacement? Since we cannot replace outcomes that we have already had in one of the spaces of the sample, then the possible amount of samples that can be obtained in this case follows the calculation for a combination:

nCr=nC_{r} = number of selections of rr items taken from a set of nn distinct items

nCr=nC_{r} = n!(nr)!r!\large \frac{n!}{(n-r)!\,r!}
Equation 3: Formula for a combination

Therefore, in this case we have that:

7C3=7C_{3}= 7!(73)!3!=7×6×5×4×3×2×1(4×3×2×1)(3×2×1)=7×6×53×2×1=2106\large \frac{7!}{(7-3)!\cdot 3!} = \frac{7\times6\times5\times4\times3\times2\times1}{(4\times3\times2\times1)(3\times2\times1)} = \frac{7\times6\times5}{3\times2\times1} = \frac{210}{6} =35= 35
Equation 4: Different possible samples without replacement

We will talk about combinations extensively in later lessons, for now, you can think of it as the method to obtain the possible amount of different samples that can be obtained from a population when there is no replacement (which happens to be the most logical scenario in many cases), just as it has been defined with this problem.

Example 2

Alice, Bob, Cole, Daisy, and Eve are all the students in a 10th grade class. Alice, Cole and Eve brought a lunch to class while the rest of the class did not.

a) \quad Find the proportion of students who brought a lunch to class.
We know the population proportion is written as the ratio of the sample size to the population size, therefore:

p=nN=35=0.6p = \frac{n}{N}=\frac{3}{5} = 0.6
Equation 5: Proportion of students who brought lunch to class

Notice that this is because three people from the class brought lunch, out of a total of 5. So our total population was 5 people, and the sample who brought lunch was 3.

a) \quad Make a list of all samples of students of size 2
This is the same as looking for all the possible combinations of two students in the classroom, therefore, on this case we must use the method to find a number of samples without replacement because we cannot have a person to be repeated itself. The calculation follows the equation for a combination as shown above, and so:

nCr=5C2=nC_{r} = 5C_{2} = 5!(52)!2!=5×4×3×2×1(3×2×1)(2×1)=5×42×1=202\large \frac{5!}{(5-2)!\cdot 2!} = \frac{5\times4\times3\times2\times1}{(3\times2\times1)(2\times1)} = \frac{5\times4}{2\times1} = \frac{20}{2} =10= 10
Equation 6: Amount of possible samples

And so, there are 10 possible samples of two people at a time coming from the 10th grade class forming the next list:

{Alice, Bob}

{Alice, Cole}

{Alice, Daisy}

{Alice, Eve}

{Bob, Cole}

{Bob, Daisy}

{Bob, Eve}

{Cole, Daisy}

{Cole, Eve}

{Daisy, Eve}

c) \quad Find the probability of each sample occurring.
If we think of this scenario as one following a completely random selection, then it means that each student and therefore each pair sample, will have the same probability to occur.
With that in mind, since there are a total of 10 possible different pair samples, then each of those pairs has a 1/10 of probability of occurring (or being selected).

d) \quad What is the sample proportion of each sample?
For this case we will look at the sample proportion for bringing lunch to class of each pair, in other words, from each of the possible pairs in the classroom, what proportion of each pair brought lunch?: If both the students brought lunch, then it means their sample proportion is 1 (or 2/2); if only one out of the two students in a pair brought lunch, then the sample proportion of this pair is 0.5 (or ½); if none of the students from a pair brought lunch, then this pairs sample proportion is zero (or 0/2). Since Alice, Cole and Eve brought lunch and Bob and Daisy did not, then the sample proportions result as follows (third column):

Combination

P(x) = probability

p^\hat{p} = sample proportion

{Alice, Bob}

1/10

0.5

{Alice, Cole}

1/10

1

{Alice, Daisy}

1/10

0.5

{Alice, Eve}

1/10

1

{Bob, Cole}

1/10

0.5

{Bob, Daisy}

1/10

0

{Bob, Eve}

1/10

0.5

{Cole, Daisy}

1/10

0.5

{Cole, Eve}

1/10

1

{Daisy, Eve}

1/10

0.5

Figure 1: Table of combinations, their probabilities and their sample proportion (solutions to part c and d)

e) \quad Find the average of all the sample proportions and explain the significance of your findings.
Averaging the sample proportions from the figure above we obtain:

μp^=\mu_{\hat{p}} = 0.5+1+0.5+1+0.5+0+0.5+0.5+1+0.510=\large \frac{0.5+1+0.5+1+0.5+0+0.5+0.5+1+0.5}{10} = 6/10=0.66/10 = 0.6
Equation 7: Average of all sample proportions

As you can see, the average of the sample proportions for the people who brought lunch to school is the same as the proportion of the population that brought lunch. Therefore, in here we reaffirm what we mentioned at the beginning of this lesson: Sample proportion will estimate population proportion.

Example 3

Uncle Sammy grows prize winning big zucchinis. The weights of his four largest zucchinis are given in the table below:

Normal distribution and continuous random variable
Figure 2: Table of uncle Sammys zucchinis and their weights


a) \quad What is the average weight of all the zucchinis Uncle Sammy grows?
We obtain the average easily as we have learned before:

Average weight = 7+9+11+54=324\large \frac{7+9+11+5}{4}=\frac{32}{4} =8=8
Equation 8: Average weight of the zucchinis

And so the average weight of uncle Sammys zucchinis is 8lbs.

b) \quad Make a list of all samples of size 2 for the zucchinis Uncle Sammy grows.
Once again, we need to find the possible combinations of two zucchinis which represent all the possible different samples of size 2 from the zucchinis in the total population:

nCr=4C2=nC_{r} = 4C_{2} = 4!(42)!2!=4×3×2×1(2×1)(2×1)=4×32×1=122\large \frac{4!}{(4-2)!\, 2!} = \frac{4\times3\times2\times1}{(2\times1)(2\times1)} = \frac{4\times3}{2\times1} = \frac{12}{2} =6 =6
Equation 9: Amount of possible samples

And so, there are 6 different possible samples of two zucchinis from the total of uncle Sammys ones and they conform the next list:

{A, B}

{A, C}

{A, D}

{B, C}

{B, D}

{C, D}

c) \quad What is the probability that each sample gets picked?
Since they would be completely randomly selected, they all have the same chance at being picked. Therefore, they all have a chance of getting picked since there are a total of 6 possible outcomes all with the same probability of being picked.

d) \quad What is the average weight of each sample?
Let us make a table with the combinations, their probability of being picked and then their average weight in each of its columns. Notice that the average weight for each sample is obtained with the simple formula for average (adding all of the items from a list, and dividing by the number of items in the list).
Remember, the average weight of each sample is what we call the sample mean.

Combination

P(x) = probability

x\overline{x} = Avg. weight

{A, B}

1/6

x=7+92=8lbs\overline{x} = \frac{7+9}{2} =8 lbs

{A, C}

1/6

x=7+112=9lbs\overline{x} = \frac{7+11}{2} = 9 lbs

{A, D}

1/6

x=7+52=6lbs\overline{x} = \frac{7+5}{2} = 6 lbs

{B, C}

1/6

x=9+112=10lbs\overline{x} = \frac{9+11}{2} = 10 lbs

{B, D}

1/6

x=9+52=7lbs\overline{x} = \frac{9+5}{2} = 7 lbs

{C, D}

1/6

x=11+52=8lbs\overline{x} = \frac{11+5}{2} = 8 lbs

Figure 3: Table of combinations, their probabilities and their average weights (solutions to part c and d)


e) \quad Find the average weight of all the sample means
Simply calculating the average of all the sample means shown in the table above:

μx^=\mu_{\hat{x}} = 8+9+6+10+7+86=\large \frac{8+9+6+10+7+8}{6} = 48/6=848/6 = 8
Equation 10: Average of all sample means

And so, we reaffirm what was mentioned at the beginning of this lesson: Sample mean will estimate population mean.

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To finalize this lesson here are a few recommendations to you: A lot of terminology is covered on this lesson for the sampling distribution of the sample mean, along with a simple example. Then, this page on the sample proportion provides a review on the formulas we have used during this lesson. Both links can be useful to you to reaffirm what was learnt today and to aid in your independent studies.

This is it for our lesson of today, see you in the next one!