When you reply to two your classmates' posts, respectfully agree/disagree to the post and explain why, while offering an opinion with an example to support it and references. Be sure to do one or more of the following:

• Agree and explain why you agree.
• Respectfully disagree and explain why you disagree.
• Offer an opinion with an example to support it.
• Propose an idea or a suggestion.
• Tell a related personal story.
• Respond to the classmates' or the instructor's questions.
• Pose a question and be sure to include your own answer as an example.
• Explain how the discussion question connects to real-life experiences.

Classmate 1:

"An interaction effect occurs when the effect of one variable depends on the value of another variable"... "Interaction effects are common in regression models, ANOVA, and designed experiments" (Foster, 2022). This study will investigate the relationship between monthly billing and the type of fuel or home through the interaction effect. In this scenario, the kind of fuel will be the independent variable, and the dependent variable will be the monthly heating bill. Still, it may vary depending on the level of the independent variable, which is the type of home, if it's a one-level or two-level. We are trying to determine if the relationship between the kind of fuel, such as electric, natural gas, and oil, and the heating bill depends on whether the home is single-level or multi-level.

Natural gas can be more efficient in a single home because of how the home is insulated and structured. The way the house is designed and shaped can have a effect on the heating efficiency. This affects the relationship between the type of fuel used, resulting in a higher bill. As homes come in different shapes and styles, all those factors can influence the effectiveness and efficiency of the different fuels used.

Another factor that may influence the heating bill is the organization supplying the fuels, as different organizations vary in prices depending on the fuel requested, and other organizations have different fees they charge their customer, such as last minute, less than 150 gallons, terrible weather, etc. For example, energy Efficient can have heating oil at $3.50 per gallon, while Barry's oil can have it at $4.75 per gallon. Yet many customers might not be aware of energy efficiency, offering it at a cheaper price, or might be loyal customers to Barry. That customer purchasing from Barry's will result in higher heating oil. It also depends on the type of fuel used, as previously mentioned, and the price, which is another factor that may influence the bill. Therefore, the interaction effect between the type of fuel and the type of home is, in fact, determined by the monthly heating bills.

Reference:

Frost, J. (2022b, July 22). Understanding interaction effects in statistics. Statistics By Jim. https://statisticsbyjim.com/regression/interaction-effects/

Classmate 2:

Interaction effect is common in statistical analysis when looking at how different factors influence outcomes (Dawson, 2021). For example, changing a condiment in a taste test can change how much people enjoy the food (Frost, n.d.). It's important to consider these interactions in our analysis because they can affect the results. If someone asks, "Do you prefer ketchup or chocolate sauce on your food?" You would most likely respond, "It depends on the type of food." That's the "it depends" nature of an interaction effect (Frost, n.d.). All variables affecting the outcome need to be considered before an accurate conclusion can be drawn.

An interaction happens when the influence of one factor is altered based on the different levels of another factor (Dawson, 2021). The interaction effect in this situation would consider the whether the monthly heating bill amount is influenced by the heating fuel type (electric, natural gas or oil) or home type (single level or multi-level). On the surface this appears to be a simple study to determine the heating costs in comparable homes using the same heating fuel, however there are many more variables to consider to draw an accurate conclusion.

I would anticipate an interaction effect in this scenario since there are multiple factors to consider in order to gauge the cost of heating for each type of home in the sample. The square footage or layout of the home can greatly affect the efficiency of heating in both single- and two or more-story home models. Open floor plans tend to use more heating fuel, as there are no walls and doors to hold in heat. Where the home is geographically located is another variable to consider (Luo, et al, 2020). Colder climates will use more heating fuel than other parts of the country that tend to remain warmer in winter months. A home with the same exact floor plan in Alaska will use more heating fuel than a home in Massachusetts during the winter months for example.

One last variable I will mention here is how well insulated the home is will factor in to how much heating fuel is used in a comparable home with the same square footage (Luo, et al, 2020). Therefore, the connection between fuel type and monthly heating costs alone cannot provide a definitive response in this study, given multiple other variables that need to be considered based on the home's characteristics.

References:

Frost. J. (n.d.). Understanding Interaction Effects in Statistics. Statistics by Jim.

https://statisticsbyjim.com/regression/interaction-effects/Links to an external site.

Dawson, J. (2021, January 22). Testing and interpreting interaction effects. Oxford University

Press. https://doi.org/10.1093/acrefore/9780190224851.013.236Links to an external site.

Luo, X., Hong, T. & Tang, Y. (2020). Model thermal interactions between buildings in urban context. Lawrence Berkeley National Laboratory, Journal 13(9). DOI: 10.3390/en13092382

Edited by Rosemary Cruz on Mar 26 at 9:03pm