基於資料分析，是否自動檔汽車比手動擋更耗油，資料分析自動檔

基於資料分析，是否自動檔汽車比手動擋更耗油，資料分析自動檔
Overview

這是一個多元迴歸方程用於揭示汽車油耗和汽車屬性之間的關係，試圖回答常見的問題：是否自動檔的汽車更費油？除了自動檔這個屬性，還有其他屬性和汽車耗油之間的關係更大嗎？原文見於RPubshere.

分析基於R語言，mtcars小資料集（可以擴充到更大資料集），希望對讀者有所協助。

There are always same questions we are being asked, "Is an automatic or manual transmission better for MPG (miles per gallon)"? "Can you show me the quantitative MPG difference between automatic and manual transmissions?" such kinds of question which are related to choosing a car and saving money on gasoline. In this document we will give our answer to these questions based on our data.
This supplement was also published on RPubs here with a virtual magazine name.
Executive Summary
Firstly we setup the relationship between transmission and MPG via statistical regression analysis technology and find the result that manual transmission is better for MPG. Secondly we go deeply with data to show the detailed quantitative information on MPG between the two main transmissions. After analyzing the single variable transmission, we create new models with new variables to further our finding about which variables help increase MPG.
Analysis phase I:

In this part, we setup a regression model between transmissions and MPG. And below are the first 6 records of data.

data(mtcars)
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

here, the most left column shows cars' model and, other colums are properties of that model. while am variable is for Transmission (0 = automatic, 1 = manual) and as its names suggests mpg column is for MPG.
Bar plot with Regression Line
Now, let's draw a basic bar plot to show the general distribution of MPG(mpg) with Transmission(am) and a regression line to show the general relationship between MPG(mpg) and Transmission(am).
plot(factor(mtcars$am),mtcars$mpg)
abline(lm(mpg~am,data=mtcars),col="red",lwd=3)

There's obvious difference between these 2 variables compared their highest, mean and lowest value pairs. On any level, the manual transmission cars has a bigger MPG value.(0 for automatic and 1 for manual).

And We could also find the trend has a positive slope that means when transmission increases one unit(from 0 to 1), or to say from automatic to manual, the MPG value will increase.

Quantitative Difference
fit=lm(mtcars$mpg~factor(mtcars$am))
fit
##
## Call:
## lm(formula = mtcars$mpg ~ factor(mtcars$am))
##
## Coefficients:
##        (Intercept)  factor(mtcars$am)1  
##             17.147               7.245
Here, the intercept 17.147 is a virtual value when the regression model created, which can be regarded as a meaningless value used only for model creation(transmission equals negative value), and the slope 7.245 means every one unit increase of transmission will beget 7.245 units increase of MPG, or to say manual transmision cars has a higher MPG 7.245 than the automatic cars in general.
Getting a confidence interval
sumCoef <- summary(fit)$coefficients
sumCoef[2,1] + c(-1, 1) * qt(.975, df = fit$df) * sumCoef[2, 2]
## [1]  3.64151 10.84837
It shows the 95% confidence is 3.64151~10.84837, that make us confident for the conclusion that manual transmission have a higher MPG than automatic ones.
Residual Plot and diagnostic
Now, draw a residual point plot.
plot(mtcars$am, resid(lm(mtcars$mpg ~ factor(mtcars$am))))
 

As the plot shows both transmissions have a very scattered (-10,10 for manual ) or (-7.5,7.5 automatic) residual, which means our model may be influenced by other variables and let's do more research.
Analysis phase II:
Now, we try to introduce other variables along with transmission. Since if the number of variables is greater than 2 will confuse customers rather than help them, so our purpose is finding one of the most useful variable along with transmission. #### Variables choosen Here are all variables that could influence MPG.
wt - Car Weight (lb/1000)
gear - Number of forward gears
carb - Number of carburetors
hp - Gross horsepower
cyl - Number of cylinders
Create models based on transmission plus one more variable
fit0<-lm(mpg ~ factor(am) , data = mtcars)
fit1<-lm(mpg ~ factor(am)+wt , data = mtcars)
fit2<-lm(mpg ~ factor(am)+gear , data = mtcars)
fit3<-lm(mpg ~ factor(am)+carb , data = mtcars)
fit4<-lm(mpg ~ factor(am)+hp , data = mtcars)
fit5<-lm(mpg ~ factor(am)+factor(cyl) , data = mtcars)
Get significance value of each variable
at1<-anova(fit1);at2<-anova(fit2);at3<-anova(fit3);at4<-anova(fit4);at5<-anova(fit5)
Show P-Value results
For those variable with P-value >5%, that means it's not significant to be introduced with the better fitted model.
at1$Pr[2];at2$Pr[2];at3$Pr[2];at4$Pr[2];at5$Pr[2]
## [1] 1.867415e-07
## [1] 0.9651278
## [1] 2.752235e-06
## [1] 2.920375e-08
## [1] 8.010109e-07
From the result, we know all 4 variables may influence MPG except the second one gear.
Fit he best model
summary(fit1)$coef;summary(fit3)$coef;summary(fit4)$coef;summary(fit5)$coef
##                Estimate Std. Error     t value     Pr(>|t|)
## (Intercept) 37.32155131  3.0546385 12.21799285 5.843477e-13
## factor(am)1 -0.02361522  1.5456453 -0.01527855 9.879146e-01
## wt          -5.35281145  0.7882438 -6.79080719 1.867415e-07
##              Estimate Std. Error   t value     Pr(>|t|)
## (Intercept) 23.145836   1.294133 17.885213 3.315382e-17
## factor(am)1  7.653119   1.222958  6.257873 7.870255e-07
## carb        -2.191748   0.377814 -5.801129 2.752235e-06
##               Estimate  Std. Error   t value     Pr(>|t|)
## (Intercept) 26.5849137 1.425094292 18.654845 1.073954e-17
## factor(am)1  5.2770853 1.079540576  4.888270 3.460318e-05
## hp          -0.0588878 0.007856745 -7.495191 2.920375e-08
##                Estimate Std. Error   t value     Pr(>|t|)
## (Intercept)   24.801852   1.322615 18.752135 2.182425e-17
## factor(am)1    2.559954   1.297579  1.972869 5.845717e-02
## factor(cyl)6  -6.156118   1.535723 -4.008612 4.106131e-04
## factor(cyl)8 -10.067560   1.452082 -6.933187 1.546574e-07
Interpreter of the best second variable.
Based on the results from all 4 variables, the weight variables even reverse the effect of transmission so it could be removed from our model. The 3rd one carb doesn't impact the transmission so obviously (in this case, the transmission slope is about 7, same as the original model contains only one variable transmission), the 4th hp have a very small influence with very small slope, while the last variable cyl does impact much, its slope is smaller than -6 (for different number of cylinders, their slopes are -6.16 and -10.07) . That means the number of cylinders are significant for MPG and the more number of cylinders, the lower MPG it gets.
Conclusion
Now we could answer the most asked question confidently, the manual transmission will really get a higher MPG, besides this concern, cars with smaller Number of cylinders will get higher MPG as well. Hope it helps when you are choosing your car.
結論：

自動擋汽車確實比手動擋更耗油，除了這個參數，汽缸數量越多也更為耗油。

Appendix:
Whole data view of our data set:
mtcars
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2