hightemp, and precip.hightemp statistically significant at 5%?hightemp?library(tidyverse)
library(scales)
options(scipen=999) data(SaratogaHouses, package="mosaicData")
houses_lm <- lm(price ~ lotSize + age + landValue +
livingArea + bedrooms + bathrooms +
waterfront,
data = SaratogaHouses)
# View summary of model 1
summary(houses_lm)Interpretation
Intercept line, for example, indicates that the coefficient is significant at 0.1% signficance level (low p-values). It means that we are 99.9% confident that the interecept is true. One the other hand, The variable age has only one star. It means that we are only 95% confident that age is meaningful in explaining home prices. If a variable had no star, it would have meant that we are not confident of the reported coefficient at all. In other words, it would be highly unlikely that changes in the variable with no star is meaningful in explaining changes in the home prices.living area is 75.18. It means that an increase of one square foot of living area is associated with a home price increase of $75, holding the other variables constant. When interpreting coeffcients, make sure to check the unit of the variables in the data.living area = 0). Of coure, living area can’t be zero. Often, interpret is meaningless.hightemp, and precip.Hint: The variables are available in the RailTrail data set from the mosaicData package.
data(RailTrail, package="mosaicData")
RailTrail_lm <- lm(volume ~ hightemp + precip,
data = RailTrail)
# View summary of model 1
summary(RailTrail)hightemp statistically significant at 5%?Yes the hightemp is statistically relevant to 5%. The P-value of hightemp is smaller than 5%.
hightemp?For every change in an additional unit of hightemp which is in degrees F’ an additional six people will be added to the volume of trail users.
No the intercept is not statistically significant at 5% because the P-value is greater than 5%.
When all predators are at the value 0 the intercept is the value of the volume of trail users.
on average the difference between the actual volume and the volume predicted by the model is 97 people we can see this is the reported residual standard.
42.47% of the variabillity in trail volumecan be explained by the model.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.