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)
##
## 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) 139878.80484 16472.92736 8.491 < 0.0000000000000002 ***
## lotSize 7500.79232 2075.13554 3.615 0.000309 ***
## age -136.04011 54.15794 -2.512 0.012099 *
## landValue 0.90931 0.04583 19.841 < 0.0000000000000002 ***
## livingArea 75.17866 4.15811 18.080 < 0.0000000000000002 ***
## bedrooms -5766.75988 2388.43256 -2.414 0.015863 *
## bathrooms 24547.10644 3332.26775 7.366 0.000000000000271 ***
## waterfrontNo -120726.62066 15600.82783 -7.738 0.000000000000017 ***
## ---
## 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: < 0.00000000000000022Interpretation
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.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 0.0000000000197 ***
## 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: 0.00000000001334hightemp statistically significant at 5%?Yes it is statistically significant at 5% because its P value is smaller than 0.05. Its P value is 0.0000000000197, which means we are 99.9% confident that the intercept is true.
hightemp?The number of trail users increases by 6 people for each degree increase in farenheit.
Yes it is statistically significant at 5% because its P value is smaller than 0.05. Its P value is 0.00019, which means we are 99.9% confident that the intercept is true.
When there is 1 inch of precipitation, -153 people will walk the rail trails.
The model misses actual number of trail users by 97 people.
Only 42% of variations are explained by the model.
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