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Transcript
Review Statistics I
Topics
Building Blocks of my Statistics 1 course
1. Definitions
2. Data
What types of data are available?
How can data be collected?
3. Graphs
How can data be graphed?
How does the proportion of data in a range relate to probability?
4. How do you calculate population and sample averages?
5.For the population and sample, how do you calculate the typical distance a value is from its
average?
6. How do you determine the probabilities associated with the bell-shaped curve?
7. What are the characteristics of all possible sample averages: mean, standard error, and
distribution?
8. Estimation
How do you infer about the population mean given the sample mean and the
population standard error?
How is the margin of error estimated if the standard error also has to be estimated?
9. Testing Hypothesis
What are the new terms and definitions?
How do you test a claim about a population parameter?
10. Review Questions
BASIC BUILDING BLOCKS OF MY STATISTICS 1 COURSE
1. We will use random sampling: every object in the population should have the same chance
of being in your sample as any other object. When using the sample mean to estimate the
population mean, this will eliminate bias and, in most cases, reduce error.
2. Sample estimates tend to be in error: e.g., sample mean – population mean ≠ 0.
3. In order to evaluate an error, compare it to the standard error:
 sample mean  population mean 


standard error


(A)
Note (a) The standard error consists of two components: a measure of variability and a measure
of knowledge.
(b) We evaluate the error using probability
(c) If the probability is low either the sample was unlikely or one of the population values
in the above ratio is not correct.
4. The margin of error (M.O.E.) is the largest error you would expect with a specified
probability:
-(M.O.E.) ≤ sample mean – population mean ≤ (M.O.E.)
(B)
where the size of the margin of error depends on the probability.
Note (a) When you can solve for the population mean in the equation (B), the interval
sample mean -(M.O.E.) ≤ population mean ≤ sample mean + (M.O.E.)
(C)
will contain the population mean with a specified probability
(b) If the ratio of equation (A) falls between a positive and negative value with a specified
probability,
 sample mean  population mean 
-Value ≤ 
 ≤ Value
standard error


then the margin of error can be found by multiplying the standard error times the value.
For an introduction to a first level statistics go to
http://wweb.uta.edu/faculty/eakin/busa3321/IntroductionToCourse.doc
1. Definitions
Population – all the objects of interest: all cars, all households, all students
Sample – a portion of the objects of interest: some cars, some households, some students
Parameter – a number that describes some aspect of the population; e.g. the mean
Statistic – a number that describes some aspect of the sample
Example: A researcher is interested in determining information about net income (NI) of
companies based on the type of company, the region (North or South), the amount of sales,
and the amount of assets. Twenty companies are sampled.
What objects are being collected?
What would be the population and what would be the sample?
What possible descriptions might be of interest?
2. Data
a. What types of data are available?
Quantitative – Numeric Values
Qualitative – Values that fall into categories
Example: Using the previous example, which ones are quantitative and which are
qualitative?
b. Data Collection (This is not a list of every possible type just some of the most
common)
i. Convenience Samples
Data you have available; May or may not be random
ii. Judgment Samples
Data chosen based on a person’s decision about the correctness of collecting the
observation; Usually not random
iii. Random Samples (specifically a simple random sample)
Every individual or item from the frame (a list) has an equal chance of being selected
Measurements are typically direct measurements.
iv. Surveys
Type of sample where the measurement are responses from individuals.
Typically some people do not respond which can bias the results
Individual responses vary from day to day.
v. Experiments.
Similar objects are randomly placed into groups and a different treatment (drug,
teaching method, work week, etc) is applied to each one.
The effect of the treatment is measured after the application.
In many cases a cause-and-effect relationship can be established.
vi. Combinations of the above.
3. Graphs
How can data be graphed?
Qualitative Data – Bar and pie charts
Quantitative Data – Break data into ranges and count number in each range. Let
each range be a bar of the bar chart called a histogram.
Example: The net incomes of ninety companies (in millions) are measured with the
following ranges, number in each category and percentages were found:
Range in
Millions
10 up to 20
20 up to 30
30 up to 40
40 up to 50
50 up to 60
60 up to 70
70 up to 80
80 up to 90
Count Percent
32
19
14
12
8
3
1
1
36%
21%
16%
13%
9%
3%
1%
1%
Percentage Distribution of Net Incomes
40%
35%
Percentage
30%
25%
20%
15%
10%
5%
0%
10-20
20-30
30-40
40-50
50-60
60-70
70-80
80-90
Income Ranges in Millions
How does the proportion of data in a range relate to probability? If every object in the
population has the same chance of being selected, then the percentage in a range is
the probability of values being the range.
Example: What is the probability of finding a company whose net income falls in the
range from 20 million to 50 million dollars? What type of sampling is needed for
this?
4. How do you calculate population and sample averages?
Both population and sample averages are found by adding up all the values and dividing by the
number of them.
Symbols:
 is the population mean and
X is the sample mean
5. For the population and sample, how do you calculate the typical distance a value is from
its average?
Definition: The typical distance a value is from its average is called the Standard Deviation
Calculation of Variance and Standard Deviation:
a. Calculate the average of the values.
b. Subtract the average from each value to see how far each value is from the average.
c. Squaring each difference.
d. Sum all the squared values
e. To find the Variance
i. For the population, divide the sum by the number of values (Symbol: 2)
ii. For the sample, divide by the number of values minus one. (Symbol: s2)
f. To find the Standard Deviation take the square root of the average in e. (Symbol: 
for population standard deviation and s for sample standard deviation)
Both population and sample uses steps a-c and e. The difference between them occurs
at step d below:
Example: Calculate the population and sample standard deviations for a set of five
numbers.
Values
6
1
3
2
2
Step a: mean =2.8
Step b.
Step c.
Distance to
Average
Square the
Distances
(6-2.8)=3.2
(1-2.8)=-1.8
(3-2.8)=0.2
(2-2.8)=-0.8
(2-2.8)=-0.8
10.24
3.24
0.04
0.64
0.64
Step d.
Step e.
Step f.
14.8
Sum =
2=
s2=
14.8/5 =2.96
14.8/4 =3.7
=
1.720465053
s=
1.923538406
For more examples, ctrl-click on the following link. Press F9 for another example.
http://wweb.uta.edu/faculty/eakin/busa3321/calculating_variance_and_standard_deviation.xls
Suggested Exercise (Use Internet Explorer rather than Firefox):
https://wweb.uta.edu/faculty/eakin/asps/Examples/varCalcQues.asp
Example of use:
http://www.forbes.com/sport/2006/06/30/best-baseball-teams_cx_tvr_0705baseball.html
6. How do you determine the probabilities associated with the bell-shaped curve?
The empirical rule, an approximation to the bell-shaped curve: A histogram with ranges based on
the mean and standard deviation along with a specific set of percentages.
Range
Percent
2.5%
 - 3* up to  - 2*
13.5%
 - 2* up to  - 
34.0%
 -  up to  
 up to  + 
34.0%
13.5%
 +  up to  + 2* 
2.5%
 + 2* up to  + 3*
Example : Suppose the ages of the buyers of a product were collected. The buyers had an
average age of 30 with a typical deviation of 5. The ranges and percentages become:
Range
Percent
15 up to 20
2.5%
20 up to 25
13.5%
25 up to 30
34.0%
30 up to 35
34.0%
35 up to 40
13.5%
40 up to 35
2.5%
Probability of Being
Within the Range
Empirical Rule Example
40%
35%
30%
25%
20%
15%
10%
5%
0%
15 up to 20 up to 25 up to 30 up to 35 up to 40 up to
20
25
30
35
40
35
Age Intervals Based on the Mean and Standard Deviation
What is the probability that the next buyer will be between 20 and 35 years of age?
Other examples: Ctrl-click on the following link and press the F9 key for another example.
http://wweb.uta.edu/faculty/eakin/busa3321/empiricalrule_example.xls
Suggested Exercise (Use Internet Explorer rather than Firefox):
https://wweb.uta.edu/faculty/eakin/asps/Examples/empiricalruleQues.asp
Bell-Shaped Curve – If more than six ranges are considered and the tops of the histogram
bars are connected, a bell-shaped curve occurs. For an infinite number of intervals, the
bell-shaped curve is also called the normal distribution.
Example of use: http://www.hardballtimes.com/main/article/face-forward-please/
The probabilities of values being within specific intervals have been tabled based on how
far a value falls from the center in number of standard deviations. This is called the
standard normal (or Z) table.
For examples on graphing regions of the normal distribution double click the embedded
Excel file below. Change the values in red and scroll down to see the pictures of the
probabilities. Click on the Excel tabs to see probabilities greater than, less than, or
between two values.
*Values in red can be changed.


X
Z-Value
20
10
18
-0.2
The probability of finding an X value below 18 is 0.4207
If the above Excel file does not work, you can find the file at:
http://wweb.uta.edu/faculty/eakin/busa3321/graphingnormal.xls
7. Distribution of Sample Means
What are the characteristics of all possible sample averages: mean, standard error, and
distribution?
If repeated samples of the same size are drawn from a very large population, the following
result:
a. The average of all the sample averages will be the same as the average of the original
population since both use the same numbers.
b. From the introduction, the typical (or standard error) in the sample average is a function
of two items: variability and knowledge. The standard error is the fraction of the
population standard deviation divided by the square root of n.
The square root is used because of diminishing returns of n. As an analogy, you
typically learn more going from 1 to 2 years on the job than you learn from 28 to 29
years on the same job.
Symbol:
is the population standard error and
is the sample estimate of the standard error
c. The larger the sample size, the closer the distribution of a sample average is to a normal
distribution. (If the original data is normal, then samples of any size will result in means
that are normal).
Example: Suppose you take all possible random samples of size 4 from the following
population of size 6: {1, 2, 3, 4, 5, 6}. Average of the population is 3.5
Original Population
Value
Probability
1
16.7%
2
16.7%
3
16.7%
4
16.7%
5
16.7%
6
16.7%
Possible
Samples
{1, 2, 3, 4}
{1, 2, 3, 5}
{1, 2, 3, 6}
{1, 2, 4, 5}
{1, 2, 4, 6}
{1, 2, 5, 6}
{1, 3, 4, 5}
{1, 3, 4, 6}
{1, 3, 5, 6}
{1, 4, 5, 6}
{2, 3, 4, 5}
{2, 3, 4, 6}
{2, 3, 5, 6}
{2, 4, 5, 6}
{3, 4, 5, 6}
Sample
Mean
2.5
2.75
3
3
3.25
3.5
3.25
3.5
3.75
4
3.5
3.75
4
4.25
4.5
Sampling Distribution
of Sample Means
Sample
Mean
Probability
2.5
7%
2.75
7%
3
13%
3.25
13%
3.5
20%
3.75
13%
4
13%
4.25
7%
4.5
7%
Distribution of Original Data
Distribution of All Possible Sample Means
18.0%
25%
Probability of Sample Mean
Having this Value
16.0%
Probability
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
20%
15%
10%
5%
0%
0.0%
1
2
3
4
Values
5
6
2.5
2.75
3
3.25
3.5
3.75
4
4.25
4.5
Possible Sam ple Means
What is the average of the original population? Average of all possible sample means?
What is the range of the original population? What is the range of all possible sample means?
What shape is the distribution of the original data? The sample means?
Finding probabilities of sample means.
Change the value of the sample mean to a z-score and then use a table to look up the
probability. For examples click on the following link:
http://wweb.uta.edu/faculty/eakin/busa3321/graphingnormalmean.xls
8. Estimation: How do you infer about the population mean given the sample mean and the
population standard error?
8.1 Estimation of population mean when the population variation is known.
Putting all the previous information together, we estimate the population mean to be the
sample mean plus or minus some multiple of the standard error where the multiple
depends on the probability from a standard normal table. What we add and subtract is
called the margin of error and usually this is ignored in newspapers and business
reports. See
http://www.businessweek.com/the_thread/hotproperty/archives/2007/01/why_we_ignore_t.html
Probability
80%
90%
95%
98%
99%
Number of Standard Errors
1.28
1.645
1.96
2.33
2.576
Example: Suppose from a random sample of size 49, we find a sample mean of 30. It is
known that the typical distance a value is from the population (standard deviation) is
35. What is the population mean with 95% confidence?
Solution: Identifier: “What is (or estimate) the population mean?”
First calculate the typical error in a sample mean. This is value is 35 divided by the
square root of 49 = 5. Therefore when using this sample mean the typical error you
would expect is five.
Next determine how far you have to go either side of the sample mean for the specified
confidence. With 95% confidence you have to go 1.96 standard errors (1.96*5=9.8)
either side of the sample mean to have 95% confidence that the population mean is
within the interval.
With 95% confidence we can say that the population mean is 30 with a maximum
possible error of  9.8
For other examples, ctrl-then click on the following link. Press the F9 key for other examples:
http://wweb.uta.edu/faculty/eakin/busa3321/zconint.xls
Suggested Exercise (Use Internet Explorer rather than Firefox):
https://wweb.uta.edu/faculty/eakin/asps/Examples/ZConfIntForMuQuesGrad.asp
If you want to work more than one of the above exercises, then after completing one exercise
use the Back command in the Internet Explorer browser and refresh the first screen.
8.2 Estimation of the population proportion, , a special case of a population mean
8.2.1 Background:
Consider a population of size 5 where there are 3 successes and two failures. The probability
of a success in the population, p, equals 3/5= 0.60. Consider recording the five values where
successes are recorded as 1’s and failures are recorded as 0’s. Find the variance of this list of
0’s and 1’s using the rules from section 5:
Values
b. Distance to Mean
c. Squared Distance
1
1 – 0.60 = 0.40
(0.40)2= 0.16
1
1 – 0.60 = 0.40
(0.40)2= 0.16
1
1 – 0.60 = 0.40
(0.40)2= 0.16
0
0 – 0.60 = -.60
(0.60)2= 0.36
0
0 – 0.60 = -.60
(0.60)2= 0.36
a.  = 3/5 = 0.60
d. Sum = 1.20
e. 2 = 1.20/5 = 0.24 (divide by 5 since it’s a population)
Note: From a. we see the population proportion is a population mean and from e. that the
population variance is 0.60*0.40=p(1-p)
Thus when estimating the population proportion, P, the sample proportion, p̂ , becomes a
special case of a sample mean and we can use the rules of section 7 with 2 replaced by p(1p) and with the word “mean” replaced with “proportion”: [Note: in other textbooks notation
changes where  denotes the population proportion and p denotes the sample proportion]
What are the characteristics of all possible sample proportions: mean, standard error, and
distribution?
If repeated samples of the same size are drawn from a very large population, the following
result:
a. The average of all the sample proportions will be the same as the proportion of the
original population that are successes since both use the same numbers.
b. From the introduction, the typical (or standard error) in the sample proportion is a
function of two items: variability and knowledge. The standard error is the fraction of
the population standard deviation divided by the square root of n.
p̂ 
p(1  p)
p̂(1  p̂)
is the population standard error and Sp̂ 
is sample standard error
n
n
(or the estimate of the population standard error.)
c. The larger the sample size, the closer the distribution of a sample proportion is to a normal
distribution. (A sample size is large enough if both np and n(1-p) are greater than or
equal to the value 5. In the case where p is unknown, a sample size is large enough if
you have at least 5 successes and 5 failures in the sample)
8.2.2. Estimation of population proportion, P
Use the same rules as a confidence interval for a population mean with the word “mean”
replaced with the word “proportion”.
Solution Steps:
Identifier: “What is (or estimate) the population proportion?”
First calculate the standard error in a sample proportion. Since the population proportion
is not known we can only use the sample standard error.
Next determine how far you have to go either side of the sample proportion for the
specified confidence. This is called the margin of error. For example, with 95%
confidence you have to go 1.96 standard errors either side of the sample proportion to
have 95% confidence that the population proportion is within the interval.
Next make your conclusion. With a specified confidence we can say that the population
proportion is the sample proportion plus or minus its margin of error.
Example: With 90% confidence, estimate the population proportion of all students who
would understand this lecture, if you had observed a random sample of 50 students and find
20% who understand it.
Solution Steps:
Identifier: “What is (or estimate) the population proportion?”
First calculate the standard error in a sample proportion. Since the population
proportion is not known we can only use the sample standard error. The sample
standard error is the square root of [0.20 * ( 1-0.20) / 50] = 0.056569.
Next determine how far you have to go either side of the sample proportion for the
specified confidence. This is called the margin of error. In this case, the margin of
error is 1.645*0.056569 = 0.093055252
Next make your conclusion. We estimate that the population of all students who would
understand this lecture is 20%. With 90% confidence this estimate is off by no more
than plus or minus 9.3%.
More examples: https://wweb.uta.edu/faculty/eakin/busa3321/ZConIntforP.xls
Suggested Exercise (Use Internet Explorer rather than Firefox):
https://wweb.uta.edu/faculty/eakin/asps/Examples/ZConfIntforPIQuesGrad.asp
If you want to work more than one of the above exercises, then after completing one exercise
use the Back command in the Internet Explorer browser and refresh the first screen.
8.3 Estimation of population mean if the population is normal but the population standard
error is unknown
The standard normal table, given a probability, determines the number of standard errors
a sample mean is from the population mean. If the standard error is not known we use the
sample estimate of it (shown above) and we must change to a table that determines the
number of estimated standard errors a sample mean is from its population mean for a
given probability. This is the t-table:
http://wweb.uta.edu/faculty/eakin/busa3321/alternativettable.doc
There are three column headings. The second set labeled “Within” is used with
confidence intervals. Example: for 98% confidence, go to the column heading labeled
“within” and find the 0.98 column. The rows correspond to the degrees of freedom which
is n-1 for the sample mean.
Example: We wish to estimate the population mean with 90% confidence based on a
sample of size 20. Using the t-table, we would go to row 19 and column 0.05. You would
have to go 1.7291 sample standard errors either side of the sample mean to have 90%
confidence that the population mean is in the interval.
Another example: You wish to estimate the average number of housing starts in all large
cities in the United States. You have a random sample of 25 cities and obtain the number
of housing starts in each. The sample mean is 525 with a sample standard deviation of 40.
Solution:
Identifier: “What is (or estimate) the population mean?”
First calculate the typical error in a sample mean. This is value is 40 divided by the
square root of 25 = 8. Therefore when using this sample mean the typical error you
would expect is estimated to be eight.
Next determine how far you have to go either side of the sample mean for the specified
confidence. With 95% confidence and 24 degrees of freedom, you have to go 2.0639
standard errors (2.0639*8 = 16.5112) or 16.5112 either side of the sample mean to
have 95% confidence that the population mean is within the interval.
With 95% confidence we can say that average number of housing starts in all cities of
interest is 525 with a maximum possible error of  16.5112
Requirements to use a t table:
Original population must be normal or a very large sample
Simple random sample
Another Example: You are measuring the size of houses in a city in thousands of square
feet. From a random sample of 225 houses, you find a sample mean and standard deviation of
2.0 and .2 respectively. With 90% confidence, what is the estimate of the average house size
of all houses in the city and what is the estimate’s margin of error? Give the conclusion as if
you were talking to someone who has not had statistics.
Go to the following link for the solution. Notice how you solve the problem by putting the
example side-by-side with a previously solved exercise:
http://wweb.uta.edu/faculty/eakin/busa3321/ReviewStat1tCIexample.doc
For other examples, ctrl-click the link below. Press F9 to see another example.
http://wweb.uta.edu/faculty/eakin/busa3321/tconInt.xls
Suggested Exercise (Use Internet Explorer rather than Firefox):
https://wweb.uta.edu/faculty/eakin/asps/Examples/tConfIntForMuQues.asp
Use of estimation in Business: http://www.imcstlouis.org/artman/publish/article_20.shtml
Use in Baseball: http://www.baseballmusings.com/archives/009154.php
Basketball (search for “confidence interval”):
http://www.basketballprospectus.com/unfiltered/index.php?s=carmelo
(ab)use in newspapers: http://www.nytimes.com/2007/04/08/opinion/08pubed.html
8.4 Notes on when to use Z or t (In all cases you must have a random sample.)
8.4.1 use the Z distribution when
a. you are conducting a confidence interval or hypothesis test on a population mean with
the population standard deviation known AND the sample means are approximately
normally distributed:
 The sample means are normally distributed for any sample size if the original
population is normally distributed.
 The sample means are approximately normally distributed when the sample size is
at least 5 and the original population is approximately symmetric.
 If you do not know the shape of the distribution for the original population a
sample size of at least 30 will give sample means that are approximately normally
distributed for most populations.
b. you are conducting a confidence interval or hypothesis test on a population proportion
AND the sample proportions are approximately normally distributed:
The sample proportion is approximately normally distributed if the sample size is large
enough that both np and n(1-p) are greater than or equal to the value 5. In the case
where p is unknown, a sample size is large enough if you have at least 5 successes and
5 failures in the sample.
8.4.2 Use the t distribution when you are conducting a confidence interval or hypothesis test
on a population mean with the population standard deviation unknown AND:
 The sample is large (the t and z become almost the same value in that case) OR
 The original population is normally distributed.
9. Testing Hypothesis
What are the new terms and definitions?
Null hypothesis: the status quo, a given value of a population parameter, something
you wish to reject
Alternative hypothesis: the opposite of the null and something you wish to support
Example: In the past, the average paper length in the process has been 11 inches.
You wish to detect a problem with the process if it occurs. (Population means no
longer 11 inches)
Null hypothesis: The population average is still 11 inches
Alternative: the population average is no longer 11 inches.
Types of Errors
Type 1. Rejecting the null when it is true.
Type 2. Not rejecting the null when it is false.
Example: Using the above example
Type 1. Saying the process is out of control when it isn’t.
Type 2. Saying the process is in control when it is actually out of control.
Probabilities
Probability of a type 1 error is , also called the level of significance.
Probability of a type 2 error is .
Probability of rejecting the null when it is false is 1-, also called the power of the
test.
Probability of finding your sample estimate (or something more extreme) if the null
was true is called the p-value. Small p-values imply that you are either very
unlucky or the null is false.
Test Statistic: a sample calculation used to test the null hypothesis. In sample means,
this is the number of sample standard errors your sample mean is from the
hypothesized value. (You know the sample mean will not equal the population
mean so you compare the observed difference with what should be typical)
Rejection Region: values of the test statistic that would be unlikely if the null was
true.
How do you test a claim about a population parameter?
Two approaches that give the same result:
Rejection Region approach: Decide values of the test statistic that are unlikely if the
null was true based on the level of significance. Reject the null if the sample test
statistic falls into this range.
p-value approach. Determine the likelihood of obtaining your sample data (or
something more extreme) if the null was true. If this probability is less than the
significance level, reject the null.
Using p-values in baseball:
http://www.hardballtimes.com/main/article/breaking-the-pitcher
Using p-values in business
http://denver.bizjournals.com/denver/prnewswire/press_releases/national/Australia/2007/08/28/HKTU007
Example: The average paper length in a manufacturing process has been 11 inches in
the past. You think the process is producing paper that is too short. You take a
random sample of 36 sheets and determine that the sample average is 10.98 and
the sample standard deviation is 0.06 inches. At the five percent level of
significance can you say the process is out of control.
Solution:
Identifier: Does the population mean take on a specific set of values? In this case is
the average paper less than 11?
Determine the null and alternative. (What you wish to show goes in the alternative
and the equal sign goes in the null)
The null is that the average paper length is 11 inches
The alternative is that the average paper length is less than 11 inches.
Determine the values of the sample mean that would be unlikely if the average paper
length was 11 and would support the alternative. In this case small sample means
would cause you support a small population mean and lead you to reject the null
and support the alternative. Using the t-table with 35 degrees of freedom, any
sample mean more than 1.6896 sample standard errors or more below the mean
would occur only five percent of the time. (Only one side is considered so the
alpha is not divided by two.)
Determine how far your data is below the hypothesized value. In this case the sample
standard error is 0.01 (0.06 divided by the square root of 36). Your sample mean
then falls two standard errors below 11 inches. This is an unlikely number and
would cause you to say that the null is false and support the alternative.
Recapping:
Null:  = 11
Alternative:  < 11
Rejection Region: Reject Ho if t < -1.6896
Test Statistic: t = (10.98 – 11) / 0.01 = -2 ( two sample standard errors below)
Decision: Reject the null and support the alternative.
Conclusion: We can say the process is out of control and producing paper that is too
short on average.
Notes
1. If your test statistic does not fall in the rejection region, all you can conclude is
that it is possible that the population mean could still be 11. (It is possible that the
process is producing paper that is too short but our test could not detect it).
2. If you have a two-sided alternative (the mean is not 11 inches), your rejection
region uses the “outside” column heading in the t-table.
For other examples, ctrl-click the link below. Press F9 to see another example.
http://wweb.uta.edu/faculty/eakin/busa3321/thyptest.xls
YOU MUST WORK ONE OF EACH OF THE THREE TYPES: (A) A TWO-SIDED TEST,
(B) A LEFT-SIDED TEST, AND (C) A RIGHT-SIDED TEST.
Suggested Exercise: A real estate agent claims that the average house size in a city is
2,500 square feet. You take a random sample of 225 houses in that city measuring the
size of the houses in thousands of square feet. You find a sample mean and standard
deviation of 2400 and 200 respectively. At a 5% level of significance, can you
conclude that the real estate agent is incorrect? Give the conclusion as if you were
talking to someone who has not had statistics.
Examples of use in baseball :
http://www.insidethebook.com/ee/index.php/site/comments/another_nail_in_the_h
ot_hand_coffin/
Examples in business http://en.wikipedia.org/wiki/Six_Sigma
10. Review Questions – Moved to Blackboard