# Chapter 4: Linear models#

Concept map: ## Notebook setup#

import numpy as np
import pandas as pd
import scipy as sp
import seaborn as sns
from scipy.stats import uniform, norm

# notebooks figs setup
%matplotlib inline
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize':(8,5)})
blue, orange  = sns.color_palette(), sns.color_palette()

# silence annoying warnings
import warnings
warnings.filterwarnings('ignore')


## 4.1 Linear models for relationship between two numeric variables#

• def’n linear model: y ~ m*x + b, a.k.a. linear regression

• Amy has collected a new dataset:

• Instead of receiving a fixed amount of stats training (100 hours), each employee now receives a variable amount of stats training (anywhere from 0 hours to 100 hours)

• Amy has collected ELV values after one year as previously

• Goal find best fit line for relationship $$\textrm{ELV} \sim \beta_0 + \beta_1\!*\!\textrm{hours}$$

• Limitation: we assume the change in ELV is proportional to number of hours (i.e. linear relationship). Other types of hours-ELV relationship possible, but we will not be able to model them correctly (see figure below).

### New dataset#

• The hours column contains the x values (how many hours of statistics training did the employee receive),

• The ELV column contains the y values (the employee ELV after one year) # Load data into a pandas dataframe
# df2

df2.describe()

hours ELV
count 33.000000 33.000000
mean 57.909091 1154.046364
std 28.853470 123.055405
min 4.000000 929.200000
25% 35.000000 1062.210000
50% 65.000000 1163.890000
75% 83.000000 1253.620000
max 99.000000 1384.480000
# plot ELV vs. hours data
sns.scatterplot(x='hours', y='ELV', data=df2)

<Axes: xlabel='hours', ylabel='ELV'> # linear model plot (preview)
# sns.lmplot(x='hours', y='ELV', data=df2, ci=False)


#### Types of linear relationship between input and output#

Different possible relationships between the number of hours of stats training and ELV gains: ## 4.2 Fitting linear models#

• Main idea: use fit method from statsmodels.ols and a formula (approach 1)

• Visual inspection

• Results of linear model fit are:

• beta0 = $$\beta_0$$ = baseline ELV (y-intercept)

• beta1 = $$\beta_1$$ = increase in ELV for each additional hour of stats training (slope)

• Five more alternative fitting methods (bonus material): 2. fit using statsmodels OLS 3. solution using linregress from scipy 4. solution using optimize from scipy 5. linear algebra solution using numpy 6. solution using LinearRegression model from scikit-learn

### Using statsmodels formula API#

The statsmodels Python library offers a convenient way to specify statistics model as a “formula” that describes the relationship we’re looking for.

Mathematically, the linear model is written:

$$\large \textrm{ELV} \ \ \sim \ \ \beta_0\cdot 1 \ + \ \beta_1\cdot\textrm{hours}$$

and the formula is:

ELV    ~        1  +       hours

Note the variables $$\beta_0$$ and $$\beta_1$$ are omitted, since the whole point of fitting a linear model is to find these coefficients. The parameters of the model are:

• Instead of $$\beta_0$$, the constant parameter will be called Intercept

• Instead of a new name $$\beta_1$$, we’ll call it hours coefficient (i.e. the coefficient associated with the hours variable in the model)

import statsmodels.formula.api as smf

model = smf.ols('ELV ~ 1 + hours', data=df2)
result = model.fit()

# extact the best-fit model parameters
beta0, beta1 = result.params
beta0, beta1

(1005.6736305656403, 2.562166505145919)

# data points
sns.scatterplot(x='hours', y='ELV', data=df2)

# linear model for data
x = df2['hours'].values   # input = hours
ymodel = beta0 + beta1*x  # output = ELV
sns.lineplot(x, ymodel)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In, line 7
5 x = df2['hours'].values   # input = hours
6 ymodel = beta0 + beta1*x  # output = ELV
----> 7 sns.lineplot(x, ymodel)

TypeError: lineplot() takes from 0 to 1 positional arguments but 2 were given result.summary()

Dep. Variable: R-squared: ELV 0.361 OLS 0.340 Least Squares 17.51 Fri, 19 Nov 2021 0.000218 00:55:52 -197.75 33 399.5 31 402.5 1 nonrobust
coef std err t P>|t| [0.025 0.975] 1005.6736 39.499 25.461 0.000 925.115 1086.232 2.5622 0.612 4.184 0.000 1.313 3.811
 Omnibus: Durbin-Watson: 4.012 2.135 0.135 2.166 -0.368 0.339 1.983 146

Notes:
 Standard Errors assume that the covariance matrix of the errors is correctly specified.

### Alternative model fitting methods#

1. fit using statsmodels OLS

2. solution using linregress from scipy

3. solution using minimize from scipy

4. linear algebra solution using numpy

5. solution using LinearRegression model from scikit-learn

#### Data pre-processing#

The statsmodels formula ols approach we used above was able to get the data directly from the dataframe df2, but some of the other model fitting methods require data to be provided as regular arrays: the x-values and the y-values.

# extract hours and ELV data from df2
x = df2['hours'].values  # hours data as an array
y = df2['ELV'].values    # ELV data as an array

x.shape, y.shape
# x

((33,), (33,))


Two of the approaches required “packaging” the x-values along with a column of ones, to form a matrix (called a design matrix). Luckily statsmodels provides a convenient function for this:

import statsmodels.api as sm

# add a column of ones to the x data
X.shape
# X

(33, 2)


#### 2. fit using statsmodels OLS#

model2 = sm.OLS(y, X)
result2 = model2.fit()
# result2.summary()
result2.params

array([1005.67363057,    2.56216651])


#### 3. solution using linregress from scipy#

from scipy.stats import linregress

result3 = linregress(x, y)
result3.intercept, result3.slope

(1005.6736305656411, 2.562166505145915)


#### 4. Using an optimization approach#

from scipy.optimize import minimize

def sse(beta, x=x, y=y):
"""Compute the sum-of-squared-errors objective function."""
sumse = 0.0
for xi, yi in zip(x, y):
yi_pred = beta + beta*xi
ei = (yi_pred-yi)**2
sumse += ei
return sumse

result4 = minimize(sse, x0=[0,0])
beta0, beta1 = result4.x
beta0, beta1

(1005.6734718528415, 2.5621687279414034)


#### 5. Linear algebra solution#

We obtain the least squares solution using the Moore–Penrose inverse formula: $$$\large \vec{\beta} = (X^{\sf T} X)^{-1}X^{\sf T}\; \vec{y}$$$

# 5. linear algebra solution using numpy
import numpy as np

result5 = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
beta0, beta1 = result5
beta0, beta1

(1005.6736305656412, 2.562166505145917)


#### Using scikit-learn#

# 6. solution using LinearRegression from scikit-learn
from sklearn import linear_model
model6 = linear_model.LinearRegression()
model6.fit(x[:,np.newaxis], y)
model6.intercept_, model6.coef_

(1005.673630565641, array([2.56216651]))


## 4.3 Interpreting linear models#

• model fit checks

• hypothesis tests

• is slope zero or nonzero? (and CI interval)

• caution: cannot make any cause-and-effect claims; only a correlation

• Predictions

• given best-fir model obtained from data, we can make predictions (interpolations),
e.g., what is the expected ELV after 50 hours of stats training?

### Interpreting the results#

Let’s review some of the other data included in the results.summary() report for the linear model fit we did earlier.

result.summary()

Dep. Variable: R-squared: ELV 0.361 OLS 0.340 Least Squares 17.51 Fri, 19 Nov 2021 0.000218 00:55:53 -197.75 33 399.5 31 402.5 1 nonrobust
coef std err t P>|t| [0.025 0.975] 1005.6736 39.499 25.461 0.000 925.115 1086.232 2.5622 0.612 4.184 0.000 1.313 3.811
 Omnibus: Durbin-Watson: 4.012 2.135 0.135 2.166 -0.368 0.339 1.983 146

Notes:
 Standard Errors assume that the covariance matrix of the errors is correctly specified.

### Model parameters#

beta0, beta1 = result.params
result.params

Intercept    1005.673631
hours           2.562167
dtype: float64


### The $$R^2$$ coefficient of determination#

$$R^2 = 1$$ corresponds to perfect prediction

result.rsquared

0.36091871798872777


### Hypothesis testing for slope coefficient#

Is there a non-zero slope coefficient?

• null hypothesis $$H_0$$: hours has no effect on ELV, which is equivalent to $$\beta_1 = 0$$: $$$\large H_0: \qquad \textrm{ELV} \sim \mathcal{N}(\color{red}{\beta_0}, \sigma^2) \qquad \qquad \qquad$$$

• alternative hypothesis $$H_A$$: hours has an effect on ELV, and the slope is not zero, $$\beta_1 \neq 0$$: $$$\large H_A: \qquad \textrm{ELV} \sim \mathcal{N}\left( \color{blue}{\beta_0 + \beta_1\!\cdot\!\textrm{hours}}, \ \sigma^2 \right)$$$

# p-value under the null hypotheis of zero slope or "no effect of hours on ELV"
result.pvalues.loc['hours']

0.00021840378059913016

# 95% confidence interval for the hours-slope parameter
# result.conf_int()
CI_hours = list(result.conf_int().loc['hours'])
CI_hours

[1.313270083442885, 3.811062926848953]


### Predictions using the model#

We can use the model we obtained to predict (interpolate) the ELV for future employees.

sns.scatterplot(x='hours', y='ELV', data=df2)
ymodel = beta0 + beta1*x
sns.lineplot(x, ymodel)

<AxesSubplot:xlabel='hours', ylabel='ELV'> What ELV can we expect from a new employee that takes 50 hours of stats training?

result.predict({'hours':})

0    1133.781956
dtype: float64

result.predict({'hours':})

0    1261.890281
dtype: float64


WARNING: it’s not OK to extrapolate the validity of the model outside of the range of values where we have observed data.

For example, there is no reason to believe in the model’s predictions about ELV for 200 or 2000 hours of stats training:

result.predict({'hours':})

0    1518.106932
dtype: float64


## Discussion#

Further topics that will be covered in the book:

• Generalized linear models, e.g., Logistic regression

• Everything is a linear model article

• The verbs fit and predict will come up A LOT in machine learning,
so it’s worth learning linear models in detail to be prepared for further studies.

Congratulations on completing this overview of statistics! We covered a lot of topics and core ideas from the book. I know some parts seemed kind of complicated at first, but if you think about them a little you’ll see there is nothing too difficult to learn. The good news is that the examples in these notebooks contain all the core ideas, and you won’t be exposed to anything more complicated that what you saw here!

If you were able to handle these notebooks, you’ll be able to handle the No Bullshit Guide to Statistics too! In fact the book will cover the topics in a much smoother way, and with better explanations. You’ll have a lot of exercises and problems to help you practice statistical analysis.

### Next steps#

• I encourage you to check out the book outline shared gdoc if you haven’t seen it already. Please leave me a comment in the google document if you see something you don’t like in the outline, or if you think some important statistics topics are missing. You can also read the book proposal blog post for more info about the book.

• Check out also the concept map. You can print it out and annotate with the concepts you heard about in these notebooks.

• If you want to be involved in the stats book in the coming months, sign up to the stats reviewers mailing list to receive chapter drafts as they are being prepared (Nov+Dec 2021). I’ll appreciate your feedback on the text. The goal is to have the book finished in the Spring 2022, and feedback and “user testing” will be very helpful.