Multiple Linear Regression

10 minute read


This document will demonstrate multiple approaches toward producing linear models in python using the US DoT airfare dataset.

Two Basic Linear Regression Models will are used:

  • Numpy's polyfit

  • Scipy Stats' linregress

Two Multiple Linear Regression Models are used:

  • sklearn's LinearRegression

  • statsmodels' ols

The most performant models in terms of adjusted R-squared often have some basic interaction terms applied that add useful complexity at the cost of a bit of interpretability.


In [1]:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as ss
import sklearn.linear_model as lm
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.stats.outliers_influence import variance_inflation_factor as vif


Dataset: airq402.dat

Source: U.S. Department of Transportation

Description: Airfares and passengers for U.S. Domestic Routes for 4th Quarter of 2002.


  • City1 (string) - Origin (From) city

  • City2 (string) - Destination (to) city

  • Average Fare - average cost (in USD) Origin -> Destination across all airlines

  • Distance (int) - distance (in miles) Origin -> Destination

  • Average weekly passengers - average number of people flying Origin -> Destination per week

  • market leading airline (string) - abbreviated name of airline with the largest % market share that offers Origin -> Destination flight

  • market share - percentage of market that the leading airline possesses

  • Average fare - average cost (in USD) that the leading airline charges for Origin -> Destination

  • Low price airline (string) - abbreviated name of airline offering the lowest price for Origin -> Destination flight

  • market share - percentage of market that the lowest price airline possesses

  • price - actual price paid for this flight instance Origin -> Destination

Columns with unspecified data types are numeric floating point

Data may be obtained from:

Description provided by:

In [2]:
### Uncomment line to download dataset locally ###
--2018-10-07 03:40:26--
Resolving (
Connecting to (||:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 77000 (75K) [text/plain]
Saving to: 'airq402.dat.1'

     0K .......... .......... .......... .......... .......... 66% 6.04M 0s
    50K .......... .......... .....                           100% 33.1M=0.009s

2018-10-07 03:40:27 (8.32 MB/s) - 'airq402.dat.1' saved [77000/77000]

In [2]:
# Assign new, compressed names to the columns 

#airq = pd.read_fwf('airq402.dat', names=colnames)
airq = pd.read_fwf('', names=colnames)
In [6]:
with open('D:\\Desktop\\pandar_tables\\panda_df.html', mode='w') as f:
In [3]:
CityFrom CityTo AvgFare Distance AvgWeekPsgrs MktLeadArLn MktShare AvgFareLead LowPriceArLn MktShareLow Price
0 CAK ATL 114.47 528 424.56 FL 70.19 111.03 FL 70.19 111.03
1 CAK MCO 122.47 860 276.84 FL 75.10 123.09 DL 17.23 118.94
2 ALB ATL 214.42 852 215.76 DL 78.89 223.98 CO 2.77 167.12
3 ALB BWI 69.40 288 606.84 WN 96.97 68.86 WN 96.97 68.86
4 ALB ORD 158.13 723 313.04 UA 39.79 161.36 WN 15.34 145.42

Exploratory Data Analysis

The dataset has no missing values and 4 columns with object values, these could be treated as categorical, but they do have a high cardinality. Other than the handling of the object values, there is no preprocessing required, the data is clean enough for immediate analysis.

In [6]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 11 columns):
CityFrom        1000 non-null object
CityTo          1000 non-null object
AvgFare         1000 non-null float64
Distance        1000 non-null int64
AvgWeekPsgrs    1000 non-null float64
MktLeadArLn     1000 non-null object
MktShare        1000 non-null float64
AvgFareLead     1000 non-null float64
LowPriceArLn    1000 non-null object
MktShareLow     1000 non-null float64
Price           1000 non-null float64
dtypes: float64(6), int64(1), object(4)
memory usage: 86.0+ KB
In [7]:
CityFrom        90
CityTo          85
MktLeadArLn     16
LowPriceArLn    19
dtype: int64
In [8]:
cats = ['CityFrom','CityTo','MktLeadArLn','LowPriceArLn']
In [9]:
city_stack = airq.CityFrom.append(airq.CityTo)
airln_stack = airq.MktLeadArLn.append(airq.LowPriceArLn)
print('unique locations: {} | unique airlines: {}'.format(city_stack.nunique(), airln_stack.nunique()))
unique locations: 104 | unique airlines: 19

Unsurprisingly, there is a good deal of overlap between the two city columns and two airline columns. With only 19 total airlines, the model would not be too convoluted if we were to one-hot encode them. For locations, however, a cardinality of 104 would quickly clutter the model, and since one of the most attractive aspects of linear regression is its interpretability, this is likely a poor decision.

In [10]:
airq[cats] = airq[cats].astype('category')

What can be experimented with is a simple categorical encoding, wherein each unique entry is assigned it's own number. Pandas does with relative ease by assigning desired object columns to a category dtype.

In [11]:
CityFrom        category
CityTo          category
AvgFare          float64
Distance           int64
AvgWeekPsgrs     float64
MktLeadArLn     category
MktShare         float64
AvgFareLead      float64
LowPriceArLn    category
MktShareLow      float64
Price            float64
dtype: object
In [12]:
airq_enc = airq.apply(lambda x: if == 'category' else x)

In order to actually use the numeric representation, we need to get the underlying from pandas. Note the .name in the equivalence check, because of the way pandas handles categorical dtypes, simply comparing like one would with a float or int does not work.

In [13]:
CityFrom           int8
CityTo             int8
AvgFare         float64
Distance          int64
AvgWeekPsgrs    float64
MktLeadArLn        int8
MktShare        float64
AvgFareLead     float64
LowPriceArLn       int8
MktShareLow     float64
Price           float64
dtype: object
In [12]:

From these graphs, we can see very strong linear relationships among the AvgFares and Price. This comes as no surprise as quite often, the leading airline controls such a large portion of the market share that they effectively set the price.

In [14]:
fig,ax = plt.subplots(figsize=(8,6))
sns.heatmap(airq_enc.corr(),vmin=-0.8, annot=True, cmap='coolwarm',ax=ax);

A correlation matrix provides a bit more evidence to the previous graphical analysis. We see that AvgFareLead and AvgFare are nearly perfectly correlated, and price is also strongly correlated with each of these variables.


2 Simple linear regression models are made with numpy and scipy.stats followed by 2 Multiple linear regressions models in sklearn and StatModels.

Using only 1 variable yielded an R-squared of ~0.75 for the basic models.

Using sklearn's an R-squared of ~0.816 is found.

Introducing interaction terms with StatModels' ols found the highest R-squared value at 0.900

In [16]:
# Define the model inputs
X, y = airq_enc.drop(columns=['Price']), airq_enc.Price
X_nc = X.drop(columns=X.select_dtypes('int8'))

Numpy's LinReg

numpy's in-built least squares polynomial fit can be used with deg=1 to mimic the most basic form of linear regression. We are given back just two things, the slope and intercept of the fit line.

In [17]:
np.polyfit(X.AvgFare, y, deg=1)
array([ 0.73908617, 22.44457608])

Scipy stats' LinReg

Scipy's linregress offers similar capabilities to that of numpy's polyfit, but returns a far richer representation of the model, also including the p-value, standard error and r value.

In [20]:
LinregressResult(slope=0.7390861729620773, intercept=22.44457607612793, rvalue=0.8664103374911551, pvalue=2.8682051454337093e-303, stderr=0.013483299248515771)
In [176]:
slope, intercept, r_value, p_value, std_err = ss.linregress(X.AvgFare,y)
print(f'Slope: {slope}; Intercept: {intercept}; R-squared: %0.3f' % r_value**2)
plt.plot(X.AvgFare,y, 'o', label='original data')
plt.plot(X.AvgFare, intercept + slope*X.AvgFare, 'r', label='fitted line')
Slope: 0.7390861729620773; Intercept: 22.44457607612793; R-squared: 0.751

seaborn also has lmplot which serves as a much nicer way to handle plotting of a simple linear model.

In [178]:

Sklearn's linear regression

Moving up to the multi-linear regression models, sklearn's LinearRegression uses Ordinary Least Squares method with very little set up required, it's quick and effective.

In [23]:
linreg = lm.LinearRegression()
model =,y)
print('R-squared: %0.5f' % model.score(X,y))
R-squared: 0.82447
In [24]:
print('R-squared: %0.5f' % lm.LinearRegression().fit(X_nc,y).score(X_nc,y))
R-squared: 0.81605

Using the data without any categorical values only slightly decreased the $R^2$ value, entirely excluding them from a model is an option worth considering.

StatsModels' linear regression

StatsModels arguably provides the most feature rich, robust methods of linear regression. It supports two different syntaxes for modeling, a traditional pythonic approach and, for those accustomed to R, it has a formula based approach. Here, we will be using the latter as it is slightly more convenient.

In [25]:
rform = " + ".join(X.columns); rform # setup R-style RHS/X/exogenous variables
'CityFrom + CityTo + AvgFare + Distance + AvgWeekPsgrs + MktLeadArLn + MktShare + AvgFareLead + LowPriceArLn + MktShareLow'
In [26]:
resf = smf.ols(f'Price ~ {rform}', data=airq).fit()
rtab = resf.summary()
In [27]:
from IPython.display import display_html
display_html(rtab.tables[0], rtab.tables[2])
OLS Regression Results
Dep. Variable: Price R-squared: 0.866
Model: OLS Adj. R-squared: 0.830
Method: Least Squares F-statistic: 24.16
Date: Sat, 12 Jan 2019 Prob (F-statistic): 1.66e-241
Time: 01:58:27 Log-Likelihood: -4268.0
No. Observations: 1000 AIC: 8960.
Df Residuals: 788 BIC: 1.000e+04
Df Model: 211
Covariance Type: nonrobust
Omnibus: 107.169 Durbin-Watson: 2.161
Prob(Omnibus): 0.000 Jarque-Bera (JB): 492.914
Skew: -0.384 Prob(JB): 9.23e-108
Kurtosis: 6.353 Cond. No. 2.25e+15
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 4.04e-22. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.

StatsModels resembles R's style very closely, meaning there is no need to pre-encode the categorical values as that is handled for us. Another nice feature that SM provides is warnings; a warning was displayed mentioning the possibility of strong multicollinearity.

Luckily, it also has a method for calculating VIF

In [30]:
exv = resf.model.exog
exv_names = resf.model.exog_names
vifs = [vif(exv, i) for i in range(exv.shape[1])]
C:\Users\Rygu\Anaconda3\envs\inet4061\lib\site-packages\statsmodels\stats\ RuntimeWarning: divide by zero encountered in double_scalars
  vif = 1. / (1. - r_squared_i)
In [35]:
vif_df = pd.DataFrame(data=[*zip(exv_names, vifs)],columns=['feature', 'VIF'])
vif_df[vif_df.VIF > 5].sort_values(by=['VIF'], ascending=False).head(10)
feature VIF
180 MktLeadArLn[T.G4] inf
197 LowPriceArLn[T.G4] inf
0 Intercept 603.358765
207 AvgFare 51.538090
211 AvgFareLead 46.067062
171 CityTo[T.TPA] 21.581872
124 CityTo[T.LGA] 20.834753
161 CityTo[T.SEA] 20.661764
129 CityTo[T.MCO] 19.607525
114 CityTo[T.IAD] 18.924252

Looks like there are some very large issues with collinearity, this may indicate that allowing the default means of handling categorical variables doesn't quite work with cardinalities as high as they are.

In [38]:
resf.pvalues[resf.pvalues < 0.05].sort_values()
AvgFare               7.214757e-43
MktShareLow           6.069499e-33
MktShare              3.208881e-13
LowPriceArLn[T.TZ]    2.948838e-10
LowPriceArLn[T.WN]    1.641302e-07
AvgFareLead           1.849007e-06
LowPriceArLn[T.US]    1.127341e-05
LowPriceArLn[T.CO]    9.738363e-04
CityFrom[T.SFO]       2.546461e-03
LowPriceArLn[T.SY]    3.425680e-03
LowPriceArLn[T.NW]    5.234996e-03
LowPriceArLn[T.B6]    5.590586e-03
LowPriceArLn[T.DL]    1.319163e-02
LowPriceArLn[T.NK]    1.744641e-02
Intercept             2.125757e-02
CityTo[T.CVG]         2.463609e-02
CityFrom[T.MEM]       2.723465e-02
LowPriceArLn[T.UA]    2.786192e-02
LowPriceArLn[T.G4]    2.929860e-02
MktLeadArLn[T.G4]     2.929860e-02
CityFrom[T.DEN]       3.844163e-02
dtype: float64

Though LowPriceArLn and a handful of cities do fall within the realm of statistical significance, we add a large amount of unnecessary multicolinearity and clutter the model quite badly. So, lets try the model without any of the categorical variables and add in interaction terms.

In [59]:
rform_nc = '*'.join(X_nc.columns); rform_nc

Using * rather than + builds up interaction terms (:) for every variable, as well as including the original variables.

In [85]:
resf_nc = smf.ols(f"Price ~ {rform_nc}", data=airq).fit()
rtab_nc = resf_nc.summary()
display_html(rtab_nc.tables[0], rtab_nc.tables[2])
OLS Regression Results
Dep. Variable: Price R-squared: 0.916
Model: OLS Adj. R-squared: 0.911
Method: Least Squares F-statistic: 171.2
Date: Sat, 12 Jan 2019 Prob (F-statistic): 0.00
Time: 02:56:31 Log-Likelihood: -4033.6
No. Observations: 1000 AIC: 8189.
Df Residuals: 939 BIC: 8489.
Df Model: 60
Covariance Type: nonrobust
Omnibus: 126.253 Durbin-Watson: 2.078
Prob(Omnibus): 0.000 Jarque-Bera (JB): 865.271
Skew: -0.335 Prob(JB): 1.28e-188
Kurtosis: 7.507 Cond. No. 1.62e+16
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.62e+16. This might indicate that there are
strong multicollinearity or other numerical problems.

After simply doing away with all non-numeric variables, we are left with an Adjusted $R^2$ of 0.911, a significant improvement over the previous, non-interacting model. We also can see that both the AIC and BIC scores have decreased, due to the reduction in model complexity.

In [187]:
Xsm = sm.add_constant(X_nc)
model_ols = sm.OLS(y, Xsm).fit()
In [188]:
AvgFare         1.528071e-63
MktShareLow     6.450949e-42
MktShare        3.314792e-15
AvgFareLead     2.234886e-09
const           1.495716e-04
Distance        2.631488e-03
AvgWeekPsgrs    3.123756e-02
dtype: float64


In this notebook several different means of doing linear regression in python were explored.

Basic linear regression was done in numpy and scipy.stats, multiple linear regression was performed with sklearn and StatsModels

After spending a large amount of time considering the best way to handle all the string values in the data, it turned out that the best was not to deal with them at all. Dropping any non-numeric values improved the model significantly.

There were many issues with collinearity across variables. Average Fare and Price are extremely closely related, which makes intuitive sense, considering Average Fare is just the mean price for that particular trip.

Future work

Sklearn has many other learner models that were left unexplored, Lasso, Ridge, Huber, all potential candidates for further exploration. Additionally, there are many more transformation that could be tried on the data to fit a line better. Only interaction terms were tried between the AvgFares and MktShares, many other combinations could be attempted as well as polynomial transformations.