I am a beginner in R (and stats), so please excuse the simple nature of my question.
I have a range of variables all relating to the physical and social characteristics of households in the UK. I am investigating the variation of temperatures inside houses. So I have calculated the coefficient of variation, which is my main dependent variable (continuous).
Now I need to test which of the selected independent variables are significant in explaining the differences in the coefficients of variation.
Structure of my data
'data.frame': 24225 obs. of 13 variables: $ SiteID : Factor w/ 280 levels This is a unique identifier for each house $ GOR : Factor w/ 9 levels This is region in UK $ ACCOM_EHCS : num Type of house $ DBL_GLAZ : int If the house has double glazing $ BUILDING_AGE : int Age of the house $ HhdSize : int Number of occupants $ Inc_Group_7s : int Income $ Person_Under_5: int Person under 5yrs old $ Person_Over_64: int Person over 64 yrs old $ Date : Date, format: $ Daily_SD : num Calculated standard deviation $ Daily_mean : num Calculated daily mean $ CV : num Calculated co-efficient of variation
I am looking to test all independent variables:
CV (co-efficient of variation), to test which of the selected independent variables (8no) are significant in explaining the differences in the coefficients of variation.
I am comfortable with linear regression of two continuous variables, but this is quite different with 8no independent categorical variables and one continuous variable defining the co-efficient of variation.
- What is the best way of testing all the independent categorical variables against the one dependent continuous variable to determine which of the independent categorical variables affect the dependent variable the most?
For example: I want to test if households with or without occupants under 5 (yes/no catagorical variable) years old have a statistically significant impact on the co-efficient of variation (continuous calculated dependent variable).
I hope this makes sense...
I think in this situation that I also need to be careful of multicollinearity.
This data is sensitive, so I cannot share the data itself.
The internal temperature was measured every 45 minutes for around 6 months in 280 houses. I have since limited the temperature data for November to February - as this roughly represents the heating season here in the UK. I calculated the daily standard deviation and daily mean and from this calculated the daily coefficient of variation.
Maybe not obvious, but all of the above variables are discrete categories that define characteristics of the households with a number - for example
DBL_GLAZ contains 5 categories 1 through 5 that each define the amount of double glazing that household has.
The above double glazing example - 1 is all windows double glazed, 2 is most, 3 is some, 4 is few and 5 is none.