Penny
12/12/2021
This presentation and the application was build for the course Developing Data Products as part of the Coursera Data Science Specializaion.
The Shiny app developed for this assignment is available at: https://penny98.shinyapps.io/Coursera_DDP_Project/
The source codes of ui.R and server.R are available on the GitHub repo: https://github.com/Pennyyyy98/Coursera_DDP_Project.git
The idea of this application is to let users be able to predict their vehicles' MPG by entering specs such as transmission type, weight, horse power, and number of cylinders.
For this application, we used the mtcars dataset from datasets library The summary of the dataset is below:
summary(mtcars)
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
We used lm to fit a multivariable regression; mpg as outcome, am, cyl, hp, and wt as predictors. The coefficients is shown below,
lm(mpg ~ am + cyl + hp + wt, data = mtcars)
Call:
lm(formula = mpg ~ am + cyl + hp + wt, data = mtcars)
Coefficients:
(Intercept) am cyl hp wt
36.14654 1.47805 -0.74516 -0.02495 -2.60648