Parameter
 A number that describes the population
 Symbols we will use for parameters include m
- mean s – standard deviation p – proportion (p) a – y-intercept of LSRL b – slope of LSRL

Statistic
 A number that that can be computed from sample data without making use of any unknown parameter
 Symbols we will use for statistics include x – mean s – standard deviation p – proportion a – y-intercept of LSRL b – slope of LSRL

Identify the boldface values as parameter or statistic.
A carload lot of ball bearings has mean diameter 2.5003
cm. This is within the specifications for acceptance of the lot by the purchaser. By chance, an inspector chooses 100 bearings from the lot that have mean diameter 2.5009
cm. Because this is outside the specified limits, the lot is mistakenly rejected.

Why do we take samples instead of taking a census?
 A census is not always accurate.
 Census are difficult or impossible to do.
 Census are very expensive to do.

Consider the population – the length of fish (in inches) in my pond - consisting of the values
m
= 8.8
and standard s
= 4.0694
population?

Let’s take samples of size 2
( n = 2) from this population:
How many samples of size 2 are possible?
5
C
2
= 10 m x
= 8.8
s x
= 2.4919

Repeat this procedure with sample size n = 3
How many samples of size 3 are possible?
5
C
3
= 10 m x
= 8.8
s x
= 1.66132

What do you notice?
 The mean of the sampling distribution
EQUALS the mean of the population.
m x
= m
 As the sample size increases, the standard deviation of the sampling distribution decreases.
as n s x

Activity – drawing samples

General Properties
Rule 1: m x
= m
Rule 2: s x
= s n
This rule is approximately correct as long as no more than 5% (10%) of the population is included in the sample

General Properties
Rule 3:
When the population distribution is normal , the sampling distribution of x is also normal for any sample size n.

General Properties
Rule 4: Central Limit Theorem
When n is sufficiently large , the sampling distribution of x is well approximated by a normal curve , even when the population distribution is not itself normal.
CLT can safely be applied if n exceeds 30.

Remember the army helmets . .
.
EX) The army reports that the distribution of head circumference among soldiers is approximately normal with mean 22.8 inches and standard deviation of 1.1 inches. a) What is the probability that a randomly selected soldier’s head will have a circumference that is greater than 23.5 inches?
P(X > 23.5) = .2623

b) What is the probability that a random sample of five soldiers will have an average head circumference that is greater than 23.5 inches?
Do you expect the probability to
What normal curve are you now working with?
P(X > 23.5) = .0774

If n is large or the population distribution is normal , then z  x  s x m x  x s
 m x n has approximately a standard normal distribution.

Suppose a team of biologists has been studying the Pinedale children’s fishing pond. Let x represent the length of a single trout taken at random from the pond. This group of biologists has determined that the length has a normal distribution with mean of 10.2 inches and standard deviation of 1.4 inches. What is the probability that a single trout taken at random from the pond is between 8 and
12 inches long?
P(8 < X < 12) = .8427

What is the probability that the mean length of five trout taken at random is between 8 and 12 inches long?
be more or less than the answer to part (a)? Explain
What sample mean would be at the 95 th percentile? (Assume n = 5) x = 11.23 inches

A soft-drink bottler claims that, on average, cans contain 12 oz of soda. Let x denote the actual volume of soda in a randomly selected can.
Suppose that x is normally distributed with s
= .16 oz. Sixteen cans are to selected with a mean of 12.1 oz. What is the probability that the average of 16 cans will exceed 12.1 oz?
P(x >12.1) = .0062
Do you think the bottler’s claim is correct?
No, since it is not likely to happen by chance alone & the sample did have this mean, I do not think the claim that the average is 12 oz. is correct.

A hot dog manufacturer asserts that one of its brands of hot dogs has a average fat content of 18 grams per hot dog with standard deviation of 1 gram. Consumers of this brand would probably not be disturbed if the mean was less than 18 grams, but would be unhappy if it exceeded 18 grams. An independent testing organization is asked to analyze a random sample of 36 hot dogs.
Suppose the resulting sample mean is 18.4 grams.
Does this result indicate that the manufacturer’s claim is incorrect?
Yes, not likely to happen by chance alone.
What if the sample mean was 18.2 grams, would you think the claim was incorrect?
No