(a-1) Dependent Variable:
a. Usage (kWh)
(a-2) Independent Variable:
a. Avg Temp (Fº)
In this scenario, you need to consider the relationship between the dependent variable (the response variable to be "explained") and the independent variable (the predictor or explanatory variable).
Let's consider the options:
(a-1) Dependent Variable:
a. Usage (kWh)
(a-2) Independent Variable:
a. Avg Temp (Fº)
In this case, the dependent variable is "Usage (kWh)," and the independent variable is "Avg Temp (Fº)."
The relationship between these variables could be related to energy consumption in a given context.
The assumption here is that the amount of electricity used (Usage in kWh) is dependent on the average temperature (Avg Temp in Fº).
This relationship is plausible because temperature can affect energy consumption.
For example, during hotter months, people might use more electricity for air conditioning, leading to an increase in overall energy usage.
Conducting a regression analysis or similar statistical modeling could help quantify the relationship between these variables and provide insights into how changes in average temperature may influence electricity consumption.
The resulting model might be useful for predicting energy usage based on temperature patterns, which could be valuable for energy planning, resource allocation, and other relevant applications.