Delta Arlines ( DL )

1 - Look for direct correlation between specific ppm (price per mile) on selected ‘future-departing day’ and ‘Revenue’

2 - Box charts future-departing days price per mile variation

3 - Building revenue predictive model based on ppm for all Days

# Model A:
modelA <- lm( REVENUE ~ PPM_DAY0+PPM_DAY1+PPM_DAY2+PPM_DAY3+PPM_DAY4+PPM_DAY5+PPM_DAY6+PPM_DAY7+PPM_DAY15+PPM_DAY21+PPM_DAY30+PPM_DAY60+PPM_DAY90+PPM_DAY180 , data = dfa )
# Stats summary:
summary( modelA )
## 
## Call:
## lm(formula = REVENUE ~ PPM_DAY0 + PPM_DAY1 + PPM_DAY2 + PPM_DAY3 + 
##     PPM_DAY4 + PPM_DAY5 + PPM_DAY6 + PPM_DAY7 + PPM_DAY15 + PPM_DAY21 + 
##     PPM_DAY30 + PPM_DAY60 + PPM_DAY90 + PPM_DAY180, data = dfa)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -505.45 -148.46   23.48  162.18  459.86 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4646.91    1018.59   4.562  0.00017 ***
## PPM_DAY0    -1028.57     808.27  -1.273  0.21708    
## PPM_DAY1    -1459.56    1577.05  -0.925  0.36522    
## PPM_DAY2     -715.46    2349.26  -0.305  0.76371    
## PPM_DAY3     4140.05    2291.83   1.806  0.08520 .  
## PPM_DAY4      592.89    1314.33   0.451  0.65654    
## PPM_DAY5    -1372.34    2382.37  -0.576  0.57071    
## PPM_DAY6    -1632.51    2263.23  -0.721  0.47867    
## PPM_DAY7    -3521.53    3425.67  -1.028  0.31565    
## PPM_DAY15    3236.08    2746.03   1.178  0.25180    
## PPM_DAY21    -708.64     538.13  -1.317  0.20207    
## PPM_DAY30     -71.73      42.63  -1.682  0.10728    
## PPM_DAY60    -891.88    2110.30  -0.423  0.67686    
## PPM_DAY90    4125.07    2616.51   1.577  0.12984    
## PPM_DAY180  -3556.99    2614.95  -1.360  0.18817    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 262.8 on 21 degrees of freedom
## Multiple R-squared:  0.5742, Adjusted R-squared:  0.2904 
## F-statistic: 2.023 on 14 and 21 DF,  p-value: 0.07008
dfa$modelA <- predict( modelA , dfa)

# Plot scattered chart: Model Prediction vs Actual
plot( data.frame(cbind(actuals=dfa$REVENUE, predicteds=dfa$modelA )) )

# Plot Hexa Binning chart: Model Prediction vs Actual
plot(hexbin(dfa$REVENUE, dfa$modelA , xbins=5) , main="Hexagonal Binning")

4 - Building revenue predictive model based on ppm for future depart days 0, 3, 6, 30 and 180

#modelB <- lm( REVENUE ~ PPM_DAY0+PPM_DAY3+PPM_DAY4+PPM_DAY15+PPM_DAY21+PPM_DAY30 , data = dfa )
# Model B:
modelB <- lm( REVENUE ~ PPM_DAY0+PPM_DAY3+PPM_DAY6+PPM_DAY30+PPM_DAY180 , data = dfa )
summary( modelB )
## 
## Call:
## lm(formula = REVENUE ~ PPM_DAY0 + PPM_DAY3 + PPM_DAY6 + PPM_DAY30 + 
##     PPM_DAY180, data = dfa)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -474.8 -192.8  -13.0  162.8  550.9 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3276.00     606.81   5.399 7.56e-06 ***
## PPM_DAY0    -1864.25     622.27  -2.996  0.00545 ** 
## PPM_DAY3     2259.14    1084.32   2.083  0.04583 *  
## PPM_DAY6       12.22    1242.66   0.010  0.99222    
## PPM_DAY30     -77.13      33.54  -2.299  0.02863 *  
## PPM_DAY180  -1035.25    1351.17  -0.766  0.44955    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 268.5 on 30 degrees of freedom
## Multiple R-squared:  0.3652, Adjusted R-squared:  0.2594 
## F-statistic: 3.452 on 5 and 30 DF,  p-value: 0.01395
dfa$modelB <- predict( modelB , dfa)

# Plot scattered chart: Model Prediction vs Actual
plot( data.frame(cbind(actuals=dfa$REVENUE, predicteds=dfa$modelB )) )

# Plot Hexa Binning chart: Model Prediction vs Actual
plot(hexbin(dfa$REVENUE, dfa$modelB , xbins=5) , main="Hexagonal Binning")

5 - Line Chart showing ppm variation around the mean for each ppm range, revenue and both predictor models

Chart below is representing normalized (around their means) the fields: revenue, prediction, day 0 to 180