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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.

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.

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 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 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, Benson, & Sincich, 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.

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.

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 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

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 conducting to find out if there is any correlation with the usage of electricity and the size of the home.

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. Consumers have several options for electricity providers. Company C wants to increase business retention and grow a new customer base by offering accurate electricity estimates for consumers based on the square footage of their current home or the home they are looking to purchase or build.

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.

Research Question

Does electricity usage increase proportionately with the size of a home?

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.

Conclusion

In conclusion, 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. 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

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.

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.

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.

Conclusion

In conclusion, 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 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