hightemp, and precip.hightemp statistically significant at 5%?hightemp?library(tidyverse)
library(scales)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)
##
## Call:
## lm(formula = price ~ lotSize + age + landValue + livingArea +
## bedrooms + bathrooms + waterfront, data = SaratogaHouses)
##
## Residuals:
## Min 1Q Median 3Q Max
## -220208 -35416 -5443 27570 464320
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.399e+05 1.647e+04 8.491 < 2e-16 ***
## lotSize 7.501e+03 2.075e+03 3.615 0.000309 ***
## age -1.360e+02 5.416e+01 -2.512 0.012099 *
## landValue 9.093e-01 4.583e-02 19.841 < 2e-16 ***
## livingArea 7.518e+01 4.158e+00 18.080 < 2e-16 ***
## bedrooms -5.767e+03 2.388e+03 -2.414 0.015863 *
## bathrooms 2.455e+04 3.332e+03 7.366 2.71e-13 ***
## waterfrontNo -1.207e+05 1.560e+04 -7.738 1.70e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 59370 on 1720 degrees of freedom
## Multiple R-squared: 0.6378, Adjusted R-squared: 0.6363
## F-statistic: 432.6 on 7 and 1720 DF, p-value: < 2.2e-16Interpretation
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 canโt 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_lm)
##
## Call:
## lm(formula = volume ~ hightemp + precip, data = RailTrail)
##
## Residuals:
## Min 1Q Median 3Q Max
## -271.311 -56.545 5.915 48.962 296.453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -31.5197 55.2383 -0.571 0.56973
## hightemp 6.1177 0.7941 7.704 1.97e-11 ***
## precip -153.2608 39.3071 -3.899 0.00019 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 96.68 on 87 degrees of freedom
## Multiple R-squared: 0.4377, Adjusted R-squared: 0.4247
## F-statistic: 33.85 on 2 and 87 DF, p-value: 1.334e-11hightemp statistically significant at 5%?The coefficient of hightemp is statistically significant at 5% and we can tell from the three stars put next to the information. And its P value is smaller than 5%.
hightemp?The coefficient of hightemp is 6.1177 and that means that an increase in one degree of temperature will lead to an increase in trail users by 6.
The intercept is not statistically significant at 5%. The p value is much larger than .05.
The intercept is 31.52. This means that when the highest temperature is 0 and there is no percipitation, there are -31 people on the trail.
The reidual standard error is 96.68. This means that the model misses the the data points on average by 97 trail users.
The reported adjusted R squared is 42%. This means that 42% of the variations are explained by the model.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.