Question 1 3 pts <p>Money has value because:</p> Money has value because: the supply is unlimited. the demand is limited. both the demand and supply are limited. the supply is limited while the demand is unlimited. the demand is limited while the supply is unlimited.   Flag this Question Question 2 3 pts <p>Which one of the following statements is correct?</p> Which one of the following statements is correct? Paper currency is printed in Washington, D.C. and Philadelphia, PA. All the paper currency in the U.S. is printed in Washington, D.C. Coins, such as nickels and dimes, are minted by the U.S. Mint in Washington, D.C. Federal Reserve notes are no longer available in the U.S. The U.S. dollar bill is backed by the full faith and credit of the U.S. government.   Flag this Question Question 3 3 pts <p>What is the APY of 9 percent compounded quarterly?</p> What is the APY of 9 percent compounded quarterly? 2.25 percent 9.00 percent 9.16 percent 9.31 percent 9.42 percent   Flag this Question Question 4 3 pts <p>Which one of the following statements is correct?</p> Which one of the following statements is correct? Simple interest is the same as interest compounded annually. A borrower would prefer a loan with 6 percent interest compounded monthly over a loan with 6 percent interest compounded daily. Investors are indifferent to the interest compounding period. Investors prefer interest be compounded annually rather than quarterly. Unless you invest for more than one year, it makes no difference how frequently interest on your savings is compounded.   Flag this Question Question 5 3 pts Skip to question text. Kate deposited $500 five years ago into a savings account and earns 6 percent simple interest. Rachel deposited $500 five years ago and earns 6 percent interest compounded annually. Which one of the following statements is correct given this information? Kate has earned a total of $300 in interest. Rachel has a savings account balance today of $728.22. Rachel and Kate have the same amount in their savings accounts today. Rachel has $22.48 more in her savings account today than Kate has. Kate has an account balance today of $650.00.   Flag this Question Question 6 3 pts <p>You want to have $25,000 saved in fifteen years to purchase a boat. How much would you have to deposit today at 7 percent interest, compounded annually, to meet this goal? Assume you do not add any additional funds to your investment account.</p> You want to have $25,000 saved in fifteen years to purchase a boat. How much would you have to deposit today at 7 percent interest, compounded annually, to meet this goal? Assume you do not add any additional funds to your investment account. $7,288.47 $8,605.81 $9,061.15 $9,748.27 $10,224.60   Flag this Question Question 7 3 pts <p>Faith has saved $600 a year for the past 20 years. Isaac has saved $600 a year for the past 10 years. How much more does Faith have in her account today as compa ...

1. Question 1
3 pts
<p>Money has value because:</p>
Money has value because:
the supply is unlimited.
the demand is limited.
both the demand and supply are limited.
the supply is limited while the demand is unlimited.
the demand is limited while the supply is unlimited.
Flag this Question
Question 2
3 pts
<p>Which one of the following statements is correct?</p>
Which one of the following statements is correct?
Paper currency is printed in Washington, D.C. and Philadelphia,
PA.
All the paper currency in the U.S. is printed in Washington,
D.C.
Coins, such as nickels and dimes, are minted by the U.S. Mint
in Washington, D.C.
Federal Reserve notes are no longer available in the U.S.

2. The U.S. dollar bill is backed by the full faith and credit of the
U.S. government.
Flag this Question
Question 3
3 pts
<p>What is the APY of 9 percent compounded quarterly?</p>
What is the APY of 9 percent compounded quarterly?
2.25 percent
9.00 percent
9.16 percent
9.31 percent
9.42 percent
Flag this Question
Question 4
3 pts
<p>Which one of the following statements is correct?</p>
Which one of the following statements is correct?
Simple interest is the same as interest compounded annually.
A borrower would prefer a loan with 6 percent interest
compounded monthly over a loan with 6 percent interest
compounded daily.
Investors are indifferent to the interest compounding period.

3. Investors prefer interest be compounded annually rather than
quarterly.
Unless you invest for more than one year, it makes no
difference how frequently interest on your savings is
compounded.
Flag this Question
Question 5
3 pts
Skip to question text.
Kate deposited $500 five years ago into a savings account and
earns 6 percent simple interest. Rachel deposited $500 five
years ago and earns 6 percent interest compounded annually.
Which one of the following statements is correct given this
information?
Kate has earned a total of $300 in interest.
Rachel has a savings account balance today of $728.22.
Rachel and Kate have the same amount in their savings accounts
today.
Rachel has $22.48 more in her savings account today than Kate
has.
Kate has an account balance today of $650.00.
Flag this Question
Question 6

4. 3 pts
<p>You want to have $25,000 saved in fifteen years to purchase
a boat. How much would you have to deposit today at 7 percent
interest, compounded annually, to meet this goal? Assume you
do not add any additional funds to your investment
account.</p>
You want to have $25,000 saved in fifteen years to purchase a
boat. How much would you have to deposit today at 7 percent
interest, compounded annually, to meet this goal? Assume you
do not add any additional funds to your investment account.
$7,288.47
$8,605.81
$9,061.15
$9,748.27
$10,224.60
Flag this Question
Question 7
3 pts
<p>Faith has saved $600 a year for the past 20 years. Isaac has
saved $600 a year for the past 10 years. How much more does
Faith have in her account today as compared to Isaac? Both
Faith and Isaac earn 5 percent interest compounded
annually.</p>
Faith has saved $600 a year for the past 20 years. Isaac has
saved $600 a year for the past 10 years. How much more does
Faith have in her account today as compared to Isaac? Both
Faith and Isaac earn 5 percent interest compounded annually.

5. $6,000.00
$8,429.67
$9,003.13
$11,475.68
$12,292.83
Flag this Question
Question 8
3 pts
<p>Martha would like a retirement income of $45,000 a year for
30 years. How much money does she need to have saved on the
day she retires if she can earn 6 percent compounded annually
on her investments?</p>
Martha would like a retirement income of $45,000 a year for 30
years. How much money does she need to have saved on the day
she retires if she can earn 6 percent compounded annually on
her investments?
$619,417.40
$778,006.16
$802,429.98
$997,878.78
$1,003,414.06

6. Flag this Question
Question 9
3 pts
<p>Matt wants to purchase a new vehicle which costs $28,795.
He has cash and a trade-in with a combined value of $8,500.
What will his monthly payment be if he finances the remainder
for three years at 6 percent interest, compounded monthly?</p>
Matt wants to purchase a new vehicle which costs $28,795. He
has cash and a trade-in with a combined value of $8,500. What
will his monthly payment be if he finances the remainder for
three years at 6 percent interest, compounded monthly?
$524.63
$598.03
$617.41
$688.09
$724.50
Flag this Question
Question 10
3 pts
<p>Theo wants to buy a new motorcycle. He has a down
payment of $500 and can afford monthly payments of $265 per
month for four years. What price of motorcycle can he afford?
His bank has agreed to lend him the funds at 9 percent
interest.</p>
Theo wants to buy a new motorcycle. He has a down payment of
$500 and can afford monthly payments of $265 per month for
four years. What price of motorcycle can he afford? His bank
has agreed to lend him the funds at 9 percent interest.

7. $9,786.48
$10,648.97
$10,899.09
$11,148.97
$11,399.09
Raw DataSizeUsageUsage/Sq Ft(kilowatt-hours(square
feet)(kilowatt-hours)per square foot)Home
11,2901,1820.92Home 21,3501,1720.87Home
31,4701,2640.86Home 41,6001,4930.93Home
51,7101,5710.92Home 61,8401,7110.93Home
71,9801,8040.91Home 82,2301,8400.83Home
92,4001,9560.82Home 102,7102,0070.74Home
112,9301,9840.68Home 123,0001,9600.65Home
133,2102,0010.62Home 143,2401,9280.60Home
153,5201,9450.55
ChartDataSheet_This worksheet contains values required for
MegaStat charts.Boxplot 2/28/2015
20:43.50212900.5525568182216550.6652332196316550.665233
2196322300.8251121076122300.8251121076322300.825112107
6329650.9136950904229650.9136950904235200.933125229650
.9136950904129650.9136950904116550.6652332196216550.66
523321961-2275-0.0801523933-2275-0.0801523931-
3100.29254041333-
3100.2925404133149301.2863878967349301.286387896716895
1.6590807031368951.6590807031Dotplot 2/28/2015
20:43.50129010.55255681821135010.59506172841147010.6233
644861160010.65333333331171010.67713310581184010.74059
040591198010.8151223010.82511210761240010.859863945612
71010.86814814811293010.91111111111300010.916279069813

8. 21010.91871345031324010.92989130431352010.9331251Norm
alPlot 2/28/2015 20:43.501290-1.53412054441350-
1.15034938041470-0.8871465591600-0.67448975021710-
0.48877641111840-0.3186393641980-
0.15731068462230024000.157310684627100.31863936429300.
488776411130000.674489750232100.88714655932401.1503493
80435201.5341205444NormalPlot 2/28/2015
20:43.500.5525568182-1.53412054440.5950617284-
1.15034938040.623364486-0.8871465590.6533333333-
0.67448975020.6771331058-0.48877641110.7405904059-
0.3186393640.815-
0.15731068460.825112107600.85986394560.15731068460.8681
4814810.3186393640.91111111110.48877641110.91627906980.
67448975020.91871345030.8871465590.92989130431.1503493
8040.9331251.5341205444
VariablesSize (square feet)Usage (kilowatt-hours per square
foot)Home 11,2900.92Home 21,3500.87Home 31,4700.86Home
41,6000.93Home 51,7100.92Home 61,8400.93Home
71,9800.91Home 82,2300.83Home 92,4000.82Home
102,7100.74Home 112,9300.68Home 123,0000.65Home
133,2100.62Home 143,2400.60Home 153,5200.55
OutputWeek 5Descriptive statisticsSize (square
feet) Usage (kilowatt-hours per square
foot) count15 15 mean2,2990.79sample standard
deviation7580.14sample
variance573,8270.02minimum1,2900.55maximum3,5200.93rang
e2,2300.38sum34,480.00 11.8193 sum of squares87,291,600.00
9.5691 deviation sum of squares (SSX)8,033,573.33 0.2560
population variance535,571.56 0.0171 population standard
deviation731.83 0.1306 standard error of the mean195.59
0.0349 confidence interval 95.% lower1,915.32 0.7195
confidence interval 95.% upper2,682.01 0.8564 margin of
error383.35 0.0684 z1.96 1.96 confidence interval 95.%
lower1,8790.71confidence interval 95.% upper2,7180.86
margin of error4190.07t(df = 14)2.145 2.145 empirical rule
mean - 1s1,541.15 0.6527 mean + 1s3,056.18 0.9232

9. percent in interval (68.26%)60.0%66.7% mean - 2s783.64
0.5175 mean + 2s3,813.69 1.0584 percent in interval
(95.44%)100.0%100.0% mean - 3s26.13 0.3823 mean +
3s4,571.21 1.1936 percent in interval
(99.73%)100.0%100.0%tolerance interval 99.73% lower26.13
0.3823 tolerance interval 99.73% upper4,571.21 1.1936 half-
width2,272.54 0.4057 skewness0.18 -0.5162 kurtosis-1.48 -
1.3419 coefficient of variation (CV)32.95%17.16%1st
quartile1,6550.67median2,2300.833rd
quartile2,9650.91interquartile
range1,3100.25modeERROR:#N/AERROR:#N/Alow extremes0
0 low outliers0 0 high outliers0 0 high extremes0 0 suggested
interval width500.00.05normal curve GOFp-value.5134 .1889
chi-square(df=2)1.333 3.333 E3.000 3.000 O(-0.84)4 4 O(-
0.25)3 2 O(+0.25)2 1 O(+0.84)2 3 O(inf.)4 5 Stem and Leaf plot
forSize (square feet) stem unit =1000leaf unit
=100FrequencyStem Leaf31 2 3 441 6 7 8 922 2 422 7 933 0
2 213 515Stem and Leaf plot forUsage (kilowatt-hours per
square foot) stem unit =0.1leaf unit =0.01FrequencyStem
Leaf25 5 916 226 5 717 40728 1 228 5 659 1 1 1 2
3152/28/2015 20:43.50 (2)2/28/2015 20:43.50 (2)2/28/2015
20:43.50 (1)2/28/2015 20:43.50 (2)Frequency Distribution -
QuantitativeSize (square feet)cumulative
loweruppermidpointwidth frequencypercent
frequencypercent1,000 <1,500 1,250 500 3 20.0 3 20.0
1,500 <2,000 1,750 500 4 26.7 7 46.7 2,000 <2,500 2,250
500 2 13.3 9 60.0 2,500 <3,000 2,750 500 2 13.3 11
73.3 3,000 <3,500 3,250 500 3 20.0 14 93.3 3,500 <4,000
3,750 500 1 6.7 15 100.0 3999.9915 100.0 Frequency
Distribution - QuantitativeUsage (kilowatt-hours per square
foot)cumulative loweruppermidpointwidth frequencypercent
frequencypercent0.55 <0.60 0.58 0.05 2 13.3 2 13.3 0.60
<0.65 0.63 0.05 1 6.7 3 20.0 0.65 <0.70 0.68 0.05 2 13.3
5 33.3 0.70 <0.75 0.73 0.05 1 6.7 6 40.0 0.75 <0.80
0.78 0.05 0 0.0 6 40.0 0.80 <0.85 0.83 0.05 2 13.3 8
53.3 0.85 <0.90 0.88 0.05 2 13.3 10 66.7 0.90 <0.95 0.92

10. 0.05 5 33.3 15 100.0 0.949900000000000415 100.0
BoxPlot
1290 1655 1655 2230 2230 2230 2965 2965 3520 2965 2965 1655
1655 2 2 3 3 1 3 3 2 2 2 1
1 2
Size (square feet)
0.55000000000000004 0.6 0.65 0.7 0.75 0.8 0.85 0.9
0.94990000000000041 13.333333333333334
6.666666666666667 13.333333333333334
6.666666666666667 0 13.333333333333334
13.333333333333334 33.333333333333329
Usage (kilowatt-hours per square foot)
Percent
1290 1350 1470 1600 1710 1840 1980 2230 2400 2710 2930 3000
3210 3240 3520 0.91627906976744189
0.86814814814814811 0.85986394557823131
0.93312499999999998 0.9187134502923977
0.92989130434782608 0.91111111111111109
0.82511210762331844 0.81499999999999995
0.740590405904059 0.67713310580204777
0.65333333333333332 0.62336448598130845
0.59506172839506177 0.55255681818181823
Size (square feet)
Usage (kilowatt-hours per square ft)
BoxPlot
0.55255681818181823 0.66523321956769055
0.66523321956769055 0.82511210762331844
0.82511210762331844 0.82511210762331844
0.91369509043927644 0.91369509043927644
0.93312499999999998 0.91369509043927644
0.91369509043927644 0.66523321956769055

11. 0.66523321956769055 2 2 3 3 1 3 3
2 2 2 1 1 2
Usage (kilowatt-hours per square foot)
DotPlot
1290 1350 1470 1600 1710 1840 1980 2230 2400 2710 2930 3000
3210 3240 3520 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1
Size (square feet)
DotPlot
0.55255681818181823 0.59506172839506177
0.62336448598130845 0.65333333333333332
0.67713310580204777 0.740590405904059
0.81499999999999995 0.82511210762331844
0.85986394557823131 0.86814814814814811
0.91111111111111109 0.91627906976744189
0.9187134502923977 0.92989130434782608
0.93312499999999998 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1
Usage (kilowatt-hours per square foot)
Runs Plot
1290 1350 1470 1600 1710 1840 1980 2230 2400 2710 2930 3000
3210 3240 3520
Time
Size (square feet)
Runs Plot
0.91627906976744189 0.86814814814814811
0.85986394557823131 0.93312499999999998

12. 0.9187134502923977 0.92989130434782608
0.91111111111111109 0.82511210762331844
0.81499999999999995 0.740590405904059
0.67713310580204777 0.65333333333333332
0.62336448598130845 0.59506172839506177
0.55255681818181823
Time
Usage (kilowatt-hours per square foot)
Normal Curve Plot
-1.5341205443525459 -1.1503493803760083 -
0.88714655901887607 -0.67448975019608193 -
0.48877641111466941 -0.3186393639643752 -
0.1573106846101707 0 0.1573106846101707
0.3186393639643752 0.48877641111466941
0.67448975019608193 0.88714655901887607
1.1503493803760083 1.5341205443525465 1290
1350 1470 1600 1710 1840 1980 2230 2400 2710 2930 3000
3210 3240 3520
Normal Score
Size (square feet)
Normal Curve Plot
-1.5341205443525459 -1.1503493803760083 -
0.88714655901887607 -0.67448975019608193 -
0.48877641111466941 -0.3186393639643752 -
0.1573106846101707 0 0.1573106846101707
0.3186393639643752 0.48877641111466941
0.67448975019608193 0.88714655901887607
1.1503493803760083 1.5341205443525465
0.55255681818181823 0.59506172839506177
0.62336448598130845 0.65333333333333332

13. 0.67713310580204777 0.740590405904059
0.81499999999999995 0.82511210762331844
0.85986394557823131 0.86814814814814811
0.91111111111111109 0.91627906976744189
0.9187134502923977 0.92989130434782608
0.93312499999999998
Normal Score
Usage (kilowatt-hours per square foot)
1000 1500 2000 2500 3000 3500 3999.99 20
26.666666666666668 13.333333333333334
13.333333333333334 20 6.666666666666667
Size (square feet)
Percent
Business Research Project Part 4
Company C “provides a valuable combination of competitive
prices, reliable electricity supply, and service to 1.4 million
homes, businesses, and industries in the southern two-thirds of
Alabama. It is one of four U.S. utilities operated by Southern
Company, one of the nation's largest producers of electricity.
More than 78,000 miles of power lines carry electricity to
customers throughout 44,500 square miles” (Alabama Power,
2014). Company C offers different programs to help customers
control energy costs. One example is an energy checkup that
involves the company estimating the electricity used per
household or business. According to the U.S. Department of
Energy, electricity consumption by residents of Alabama is
growing faster than the actual population in Alabama (U.S.
Department of Energy, 2015). Our research will be conducted to
find out if there is any correlation with the usage of electricity
and the size of the home.

14. Variables
Independent – square footage of a home
Dependent – electricity usage of a home
Business Problem
Due to economic pressures, consumers are increasingly
concerned with tightening their budgets. Limiting electricity
consumption is one way to accomplish this. However, limiting
electricity use is not always a viable option. Because consumers
have several options for electricity providers, Company C has to
offer services beyond supplying electricity, such as consumer
education and incentives. Additionally, Company C wants to
increase business retention and grow a new customer base by
offering innovative services such as the proposed accurate
electricity estimates for consumers based on the square footage
of their current home or the home they are looking to purchase
or build. Fischer’s (2008) study found the following: The most
successful feedback is given frequently over a long period of
time, provides specific breakdown of electricity usage and is
presented in a clear and appealing way.
Team’s Role
Team “C” will carefully analyze the production,
distribution, and consumption of electricity per household in the
state of Alabama to determine if there is a correlation between
the size of homes and the amount of energy consumed. The team
will pay particular attention to key variables that directly affect
the amount of energy consumed such as insulation requirements,
material construction type, and age of the average home in the
state. All of these variables will play a critical role in
determining the correlation between energy usage and home
size. According to the IEA (International Energy Agency),
material used during construction and the use of building
envelopes or building shells have a dramatic impact on the
energy efficiency of a home (International Energy Agency,
2013). It is the team’s challenge to analyze all of these factors
carefully and arrive at a conclusion on how home size affects
the consumption of energy in Alabama homes.

15. To figure out how home size affects energy usage, the
team must use the collected data to calculate the correlation
coefficient between the square footage of the home and the
electricity consumption. The correlation coefficient measures
the strength of linear relationship between two variables
(McClave, Benson, & Sincich, 2011). Then the team will
complete a hypothesis test of the correlation coefficient.
Additional factors that can affect energy usage are lifestyles,
appliance usage, and social behaviors of home owner. “The
Residential Energy Consumption Survey (RECS) identified five
lifestyle factors reflecting social and behavioral patterns
associated with air conditioning, laundry usage, personal
computer usage, climate zone of residence, and TV use. These
factors were also estimated for 2001 RECS data.” (Sanquist,
Orr, Shui, & Bittner, 2011)
Research Question
Does electricity usage increase proportionately with the size of
a home?
According to the residential energy consumption survey, larger
homes tend to have more energy efficient features. And, as
overall home square footage increases, the likelihood that the
home has key energy efficient features also rises. Changes in
equipment, appliances, and construction standards in the last 15
years are tempering energy consumption in these larger homes
(U.S. EIA, 2009).
Hypothesis Statements
H0: The electric usage per square foot of a residential home
decreases as the size of the home increases.
H1: The electric usage per square foot of a residential home
does not decrease as the size of the home increases.
Sampling and Data Collection Plan
Company C is conducting a study to determine whether
electricity usage increases proportionately with the size of a
home. Company C must identify its target population for the
study, the method of obtaining the data, the appropriate sample
size, and the method of random sampling. Additionally, the

16. study must be conducted in a manner that will ensure the
reliability and validity of the data used.
Population and Size
The population involved in this study being conducted by
Company C is all households in Alabama. According to the U.S.
Census Bureau (2015), there were 1,838,683 households in
Alabama during the years 2009 through 2013. Because the
number of households is far too numerous to include all of them
in the study, Company C will next narrow these down to a target
population.
The team will use residential customers that have at least one
consecutive year of usage data available in the company
historical databases. This ensures that the residential data used
is the most accurate and a true representation of energy
consumption used in homes relative to their square footage.
Target Population and Reasoning
Company C’s target population is households utilizing
electricity as their primary source of power, with electricity as
the primary source of energy used to cool and heat the homes.
Company C will also focus on those households where it
currently provides electricity to customers and has the potential
for gaining new customers.
Sampling Element
Because Company C already provides electricity to many
households in the southern two-thirds of Alabama, data mining
will be the primary method of obtaining data for this survey.
Per Furnas (2012), “Data mining is used to simplify and
summarize the data in a manner that we can understand, and
then allow us to infer things about specific cases based on the
patterns we have observed.” Company C maintains an extensive
database including electricity usage information, household
square footage, and other data that may be useful in this
analysis.
Sample Size
Company C has a dataset available including the electricity
usage and square footage of 15 households. It is important to

17. ensure a variety of household sizes are included in the analysis
to provide an accurate response to the company’s research
question and determine if the hypothesis is true or false.
Calculation
The formula generally used to calculate sample size is:
A 95% degree confidence corresponds to = 0.05. Each of the
shaded tails in the following figure has an area of = 0.025. The
region to the left of and to the right of = 0 is 0.5 – 0.025, or
0.475 (Six Sigma, 2014). In the table of the standard normal ()
distribution, an area of 0.475 corresponds to a value of 1.96.
The critical value is therefore = 1.96. (Six Sigma, 2014).
Although the available dataset is 15, because the population is
201,332, the margin of error is 5% at a 95% confidence level,
the minimum recommended size for this survey should be 384.
Method of Random Sampling
The most appropriate method of random sampling for this study
is stratified random sampling. Stratified random sampling is
appropriate when the population can be divided into two or
more groups of sampling units, or strata (McClave et al., 2011).
Company C wants to ensure that an equal number of houses of
different size categories are utilized in the study. If an uneven
distribution of houses is used, then outliers in any one of the
strata have a greater potential to sway the results of the survey
and provide inaccurate results. The stratified random sampling
will divide households obtaining electricity from Company C
into three groups:
· Small = 800 – 1,799
· Medium = 1,800 – 2,799
· Large = 2,800 – 3,799
Houses with an area less than the minimum square footage for
the small grouping or greater than the maximum square footage
for the larger grouping will be excluded from the analysis.
Including households that are substantially smaller or larger
than most households in the population being examined has the
potential to skew the results.

18. Validity and Reliability
For Company C’s study to be successful, the validity and
reliability of the data used in the survey must be ensured. To
ensure the validity of the data, Company C will only include
households in the study that use electricity as the primary
source of heating and cooling. If households utilizing other
forms of energy are included in the analysis, the results will be
inaccurate.
Data Collection and Protection
The data used in this study is owned by Company C. Company
C will work with its information technology department to
obtain the data needed. The data is stored in a secure database
and will be pulled into Microsoft Excel for this purpose of this
analysis. Any household-specific data retrieved from the
database will not be shared outside of Company C. Only
aggregate results of the study will be shared to protect
individual household information.Descriptive Statistics
“Descriptive statistics utilizes numerical and graphical methods
to look for patterns in a data set, to summarize the information
revealed in a data set, and to present the information in a
convenient form” (McClave et al., 2011). Since the purpose of
this study is to determine whether the square footage of a home
impacts the electricity usage per square foot, the appropriate
descriptive statistics to use for this study are square footage and
electricity usage per square foot. The square footage and
electricity usage must be determined for each home included in
the study (i.e. we cannot use the square footage of one home
and match it with the electricity usage of a different home).
Square Footage
Distribution is not normally distributed.
Central Tendency: Median = 2,230 square feet
Dispersion: Interquartile Range = 1,310 square feet / 2 = ± 655
square feet
Number: 15 homes
Min/Max: 1,290 square feet and 3,520 square feet

19. Confidence Interval: Not applicable (data is not normally
distributed)
See the raw data in Appendix A, the histogram and scatterplot
in Appendix B, and the descriptive statistics in Appendix C.
Electricity Usage – Average Kilowatt Hours per Month per
Square Foot
Distribution is not normally distributed.
Central Tendency: Median = 0.83 kilowatt-hours per square
foot
Dispersion: Interquartile Range = 0.25 kilowatt-hrs per sq ft / 2
= ± 0.12 kilowatt-hrs per sq ft
Number: 15 homes
Min/Max: 0.55 kilowatt-hours per square foot and 0.93
kilowatt-hours per square foot
Confidence Interval: Not applicable (data is not normally
distributed)
See the raw data in Appendix A, the histogram and scatterplot
in Appendix B, and the descriptive statistics in Appendix C.
Descriptive Statistics Interpretation
Square Footage
The first variable used in this survey is square footage per
household. For this study, square footage was defined as the
total square footage of usable space within a household. Crawl
spaces and other areas within these households that do not
utilize electricity were excluded from the square footage
calculation. The sample was obtained from homes in the same
region to eliminate the potential impact of variations in
temperature.

20. The data was skewed. Fifteen homes were randomly selected.
Their sizes were between 1,290 and 3,520 square feet, with a
variance of plus or minus 655 square feet. One half or more
homes were 2,230 square feet or larger. The middle half of the
homes’ sizes fell between 1,655 and 2,965 square feet. There
was no mode.
Electricity Usage - Average Kilowatt Hours per Month per
Square Foot
The second variable used in this survey is electricity usage. The
measurement utilized was kilowatt-hours because this is the
most common measurement of electricity usage. Kilowatt-hours
were monitored for a 12-month period and averaged to eliminate
any potential impact from seasonality.
The data was skewed. The same fifteen homes randomly
selected for determining square footage were used to measure
electricity usage. Their usage was between 0.55 and 0.93
kilowatt-hours per square foot, with a variance of plus or minus
0.12 kilowatt-hours per square foot. The usage of half or more
homes was 0.83 kilowatt-hours per square foot or higher. The
middle half of the homes’ electricity usage fell between 0.67
and 0.91 kilowatt-hours per square foot. There was no mode.
Conclusion
Company C has determined the appropriate data for
completing this analysis. The value of conducting this study is
largely dependent on using appropriate data and ensuring its
validity and reliability. Methods for data collection and
analysis must consider the ultimate goal of the study.
Additionally, the statistics test determined how much data
needed to be collected to detect any significant deviation
between the null hypothesis and analysis.
Team C has compiled important data to analyze how residential
homes in the state of Alabama utilize electricity. By gathering
this data, we can test the impact that square footage has on
electricity consumption. To do so, the team must use this
information to figure out the correlation coefficient. Then the
team will do a hypothesis test of the correlation coefficient

21. between the square footage of the building and the electricity
usage. This information can help customers understand the
amount of electricity needed for a particular size building when
purchasing or building a home. The key variables of square
footage and electricity usage will be used throughout this
process to help Company C better serve the customers of
Alabama.
References
Alabama Power. (2014). About us. Retrieved from
http://www.alabamapower.com/residential/save-money-
energy/energy-checkup.asp
Fischer, C. (2008, May). Feedback on Household Electricity
Consumption: a tool for saving energy?. Springer Science and
Business Media, 1(1), 79-104.Furnas, A. (2012). Everything you
wanted to know about data mining but were afraid to ask. The
Atlantic. Retrieved from
http://www.theatlantic.com/technology/archive/2012/04/everyth
ing-you-wanted-to-know-about-data-mining-but-were-afraid-to-
ask/255388/International Energy Agency. (2013). Technology
roadmap: energy efficient building envelopes.
Retrieved from
http://www.iea.org/publications/freepublications/publication/tec
hnology-roadmap-energy-efficient-building-envelopes.html
McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics
for business and economics (11th
ed.). Boston, MA: Prentice Hall. Retrieved from the University
of Phoenix eBook
Collection database.
Sanquist, T., Orr, H., Shui, B., & Bittner, A. (2011). Lifestyle
factors in U.S. residential electricity consumption. Energy
Policy,42, 354-364.
Six Sigma. (2014). Retrieved from
http://www.isixsigma.com/tools-templates/sampling-data/how-
determine-sample-size-determining-sample-size/
United States Census Bureau. (2015). State and county

22. quickfacts. Retrieved from
http://quickfacts.census.gov/qfd/states/01000.html
U. S. Department of Energy. (2015). Alabama residential energy
consumption. Retrieved from
http://apps1.eere.energy.gov/states/residential.cfm/state=AL#ele
c
U.S. Energy Information Administration. (2009). The impact of
increasing home size on energy
demand. Residential Energy Consumption Survey. Retrieved
from
http://www.eia.gov/consumption/residential/reports/2009/square
-footage.cfm
Appendix A
Home Size and Electricity Usage Raw Data

23. Appendix B
Home Size and Electricity Usage Histograms
Scatterplot – Size versus Energy Usage per Square Foot
Running head: BUSINESS RESEARCH PROJECT PART 4
15
BUSINESS RESEARCH PROJECT PART 4
15

24. Appendix C
Descriptive Statistics
SizeUsageUsage/Sq Ft
(kilowatt-hours
(square feet)(kilowatt-hours)per square foot)
Home 11,290 1,182 0.92
Home 21,350 1,172 0.87
Home 31,470 1,264 0.86
Home 41,600 1,493 0.93
Home 51,710 1,571 0.92
Home 61,840 1,711 0.93
Home 71,980 1,804 0.91
Home 82,230 1,840 0.83
Home 92,400 1,956 0.82
Home 102,710 2,007 0.74
Home 112,930 1,984 0.68
Home 123,000 1,960 0.65
Home 133,210 2,001 0.62
Home 143,240 1,928 0.60
Home 153,520 1,945 0.55
051015202530
PercentSize (square feet)
05101520253035
PercentUsage (kilowatt-hours per square foot)
y = -0.000 x + 1.174 R² = 0.887
0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 1,000
1,500 2,000 2,500 3,000 3,500 4,000
Usage (kilowatt-hours per square ft)Size (square feet)
Size
(square feet)

25. Usage
(kilowatt-hours
per square foot)
count15 15
mean2,299 0.79
sample standard deviation758 0.14
sample variance573,827 0.02
minimum1,290 0.55
maximum3,520 0.93
range2,230 0.38
confidence interval 95.% lower1,879 0.71
confidence interval 95.% upper2,718 0.86
margin of error419 0.07
t(df = 14)2.145 2.145
1st quartile1,655 0.67
median2,230 0.83
3rd quartile2,965 0.91
interquartile range1,310 0.25
mode#N/A#N/A
low extremes0 0
low outliers0 0
high outliers0 0
high extremes0 0
normal curve GOF
p-value.5134 .1889
chi-square(df=2)1.333 3.333
E3.000 3.000