Linear Regression#

Mathematicians do not study objects, but relations among objects; they are indifferent to the replacement of objects by others as long as relations do not change. Matter is not imporant, only form interests them.

—Henri Poincare

In this project, we will take the techniques we have learned for bivariate data analysis and apply to them to three datasets from real life. We will examine and classify the correlation between variables and then use sample statistics to determine whether the relationship observed between the variables is significant. We will fit a Linear Regression Model to the data and use the model parameters to make predictions and extrapolations.

In order to do this, as you may have guessed, we will use Python; Python provides several functions that make bivariate analysis a quick and painless process.

Instructions#

Download all three csv datasets in the Datasets section and place them in the Linux Files folder on your folder system where you save your .py scripts.
Create a Python .py script named NAME_project_four.py in your Linux Files folder on your file system. You can do this by opening an IDLE session, creating a new file and then saving it. Replace NAME with your name.
Create a docstring at the very top of the script file. Keep all written answers in this area of the script.
Read the Background section.
Read the Loading Data section.
Read the Bivariate Analysis section.
Load in the data from the .csv file using the technique outlined in the Loading Data section.
Perform all exercises and answer all questions in the Project section. Label your script with comments as indicated in the instructions of each problem.
When you are done, zip your script and the csv files in a zip file named NAME_project_four.zip
Upload the zip file to the Google Classroom Project Four Assignment.

Background#

Correlation#

The correlation coefficient is defined by the formula,

$r_xy = \frac{1}{n-1} \cdot \sum_{i=1}^{n} (\frac{x_i - \bar{x}}{s_x}) \cdot (\frac{y_i - \bar{y}}{s_y})$

Notice the correlation coefficient is the average of a product of z-scores. In this formula, both the $x$ and $y$ variable are standardized and the product of each z-score is summed and then divided by $n-1$ to find the average. When the z-scores of the $x$ and $y$ variable are simultaneously positive or simultaneously negative, this results in a positive correlation. When the z-scores of the $x$ and $y$ variable simultaneously take on opposite signs (i.e. when one is negative, the other is positive and visa versa), this results in a negative correlation.

Linear Regression Model#

The Linear Regression Model is a special statistical method for modelling a linear relationship that exists between correlated variables. A regression model (not necessarily linear) applies to a sample of bivariate data,

$S = \{ (x_1, y_1), (x_2, y_2), ... , (x_n, y_n) \}$

Where $n$ is the total number of samples. The $x_i$ variable is referred to as the predictor variable (or sometimes the independent variable); the $y_i$ variable is referred to as the response varaible (or sometimes the dependent variable). If a statistically significant linear correlation exists between the predictor and response variable, the Linear Regression Model can be used to predict a value of $y_i$ given a value of $x_i$ . The model equation for $\hat{y_i}$ is given by,

$\hat{y_i} = \mathcal{B}_1 \cdot x_i + \mathcal{B}_0 + \varepsilon_i$

Where the term $\varepsilon_i$ is a normally distributed error term centered around 0 with standard deviation equal to the square root mean squared error of the model,

$\varepsilon \sim \mathcal{N}(0, \sqrt{\text{MSE}})$

Residuals#

The error term :varepsilon_i is sometimes called a residual. The value of a residual for a given $i$ can be found by subtracting the predicted value $\hat{y_i}$ from the actual value of $y_i$ ,

$\varepsilon_i = y_i - \hat{y_i}$

A crucial assumption of the Linear Regression Model is the normality of the residuals. If this assumption is violated, then there is evidence to suggest the model is incomplete, i.e. there is another variable influencing the response variable. Moreover, if the normality assumption is violated, the model can no longer be used to extrapolate outside of the range of the predictor variable.

Datasets#

In this project, we will examine three datasets, each of which exemplifies a different feature of bivariate data and presents its only unique challenges.

Old Faithful#

The first dataset we will look at is the Old Faithful dataset. This is a dataset we have seen many times and we finally have the tools to listen to what it is telling us. The individuals recorded in this dataset are eruptions of the Old Faithful geyser. Recall the two variables being measured are the length of an eruption and the waiting time until the next the eruption. Both variables are measured in minutes.

Spice Girls Songs#

The second dataset we will consider is the Spice Girls dataset. The individuals in this datasets are song tracks on Spice Girl studio albums. The dataset contains many variables, but the two of interest for this project are song length in milliseconds and song order on the album. Song length should hopefully be self-explanatory; the song order refers to the order in which the song appears on the album when played from front to back.

Celebrity Twitter#

The third and final dataset in this project is the Celebrity Twitter dataset. This is another dataset we have seen several times already. The individuals recorded in this dataset are Twitter accounts. In this project, we will see the entire dataset, which contains many variables that do not interest us; the variables at which we will look are the number of followers and the number of tweets.

Warning

This dataset is slightly different from the one we saw on our calculators. Our calculators have limited memory, so in order to get the data points imported, the values were scaled. In addition, some of the data was trimmed.

In other words, in this project, we will be working with the full dataset.

Bivariate Analysis#

Before we begin, let’s import all the appropriate libraries and create some dummy data to test out the bivariate functions Python’s statistics package has available,

import matplotlib.pyplot as mpl
import statistics as stat

# you can break long lists over multiple lines!
bivariate_data = [
        (2, 1), (8, 3), (1, 1), (2, 0), (9, 4), (3, 2), (5, 3),
        (1, 0), (7, 3), (6, 3), (3, 2), (0, 0), (0, 1), (8, 4),
        (0, 0), (3, 1), (4, 3), (7, 3), (1, 1), (10, 6), (6, 4),
        (3, 2), (7, 3), (6, 2), (9, 5), (5, 3), (1, 1), (4, 2),
        (1, 0), (7, 3)
]
# note: we are creating THREE subplots!
(fig, axes) = mpl.subplots(3)

The sections that follow assume you have these lines added to the top of your script.

Scatter Plots#

A scatterplot is a very simple and easy to understand graphical representation of data. The predictor variable is plotted on the horizontal axis versus the response variable on the vertical axis. A scatterplot can be created in matplotlib using the scatter() function,

# separate x and y data
x_data = [ obs[0] for obs in bivariate_data ]
y_data = [ obs[1] for obs in bivariate_data ]

axes[0].scatter(x_data, y_data)

# label axes
mpl.title("Scatterplot Example")
axes[0].set_ylabel("y observation")
axes[0].set_xlabel("x observation")

mpl.show()

Note

The x and y variables must be split into separate lists before calling the scatter() function.

The code snippet shown above will create the following graph,

(Source code, png, hires.png, pdf)

Inspecting a scatterplot is often a good first step in determining whether a correlation exists between two variables. For example, the dummy data we created in the previous section has an obvious positive, linear relationship. When a linear relationship exists between variables, the correlation coefficient can be used to quantify the strength of the relationship.

Correlation#

The correlation coefficient can be easily calculated using the statistics package in Python.

Important

The correlation() function was introduced into the statistics package in version 3.10 of Python. If you are on an earlier version, the code in this section not work! Email me or talk to me after class and we will get you sorted!

The following snippet illustrates its calculation,

r = stat.correlation(x_data, y_data)
print("correlation coefficient: ", round(r, 4))

Output:

correlation coefficient: 0.9195

A correlation near 1 suggests a strong relationship. To establish statistical significance, we compare this sample and correlation coefficient against the pearson_correlation_coefficient_table. If the absolute value of the correlation coefficient is greater than the table value, we say there is a statistically significant correlation.

We will explore why we compare the sample correlation to the table in a later chapter, we start talking about Sampling Distributions and statistical_significance.

Regression Parameters#

The regression parameters, $\mathcal{B}_0$ and $\mathcal{B}_1$ , can also be easily calculated using the statistics package in Python.

Important

The linear_regression() function was introduced into the statistics package in version 3.10 of Python. If you are on an earlier version, the code in this section not work! Email me or talk to me after class and we will get you sorted!

(slope, intercept) = stat.linear_regression(x_data, y_data)
print("beta zero: ", round(slope, 4))
print("beta one: ", round(intercept,4))

Output:

beta zero: 0.1765

beta one: 0.4706

Notice the output of the linear_regression function is an ordered pair (in Python, ordered pairs are called Tuples). The first element of the ordered pair is the slope of the Linear Regression Model, or symbolically $\mathcal{B}_1$ ; the second element of the ordered pair is the intercept of the Linear Regression Model, or symbolically $\mathcal{B}_0$ .

Line of Best Fit#

The Regression line can be plotted over top the scatter plot using the plot() function from matplotlib. In order to use the plot() function, we will need to create a list of data representing $\hat{y}$ . This can be done by iterating over the x_data list with Comprehension,

predicted_y = [ slope * x + intercept for x in x_data ]

Then, we pass the x_data and predicted_y list in the plot() function,

axes[0].plot(x_data, predicted_y, color="red", label="regression line")

Note you can change the color of the line and add a label to it. The label will not appear, unless the legend is shown on the graph. To display the label on a legend, you can call,

axes[0].legend()

Before calling,

mpl.show()

If you are following along, you should see the following graph when you execute your module code,

(Source code, png, hires.png, pdf)

../../_images/scatterplot_regression_line.png

Residuals#

The final step of the regression process is validating the model assumptions; in other words, we must check to make sure the distribution of residuals is approximately normal. In order to do this, we must first create a list containing the residuals. To create the list of residuals, we will use the Zip Function. The zip() function allows us to iterate over two list simultaneously. The following code snippet steps over the predicted_y and y_data lists at the same time, grabbing elements with the same index along the way,

residuals = [ y - yhat for (yhat, y) in zip(predicted_y, y_data) ]

Note

The order of (y_hat, y) corresponds to the order in which predicted_y and y_data are passed into the zip() function.

Once we have the list of residuals, we can create a histogram of their distribution,

axes[1].hist(residuals, density=True)

This provides a quick way of checking the normality of the residuals, but a better way is constructing a QQ Plot, as we did in the previous project. Refer to the Normality project page to review the logic behind the construction of a QQ Plot. The code is reproduced below without explanation,

residuals.sort()
res_n = len(residuals)
res_mean = stat.mean(residuals)
res_sd = stat.stdev(residuals)
standardized_res = [ (r - res_mean)/res_sd for r in residuals ]
z = stat.NormalDist(0, 1)
theoretical_per = [ z.inv_cdf((i+1)/(n+1)) for i in range(res_n) ]

After the standardized distribution and the theoretical distribution are found, they can be plotted using a scatterplot,

axes[2].scatter(standardized_res, theoretical_per)
axes[2].set_xlabel("ranked z-scores")
axes[2].set_ylabel("theoretical z-scores")

Recall a Normal distribution should have a linear QQ Plot. Any departures from linearity imply the underlying distribution is not Normal.

Project#

Old Faithful#

Scatter Plot#

Construct a scatter plot for this dataset using the Eruption Length as the predictor variable and the Waiting Time as the response variable. Ensure the graph is labelled appropriately.
In your Docstrings, describe the correlation in this dataset. Is it positive or negative? Is it strong or weak? Is it linear or non-linear? If it is non-linear, what type of functional form could be used to model the dataset?
In your Docstrings, answer the following question: Based on your answer to the previous question, would a linear regression model be a good fit for this dataset?

Correlation#

Using the Correlation function, calculate the correlation between the Eruption Length and the Waiting Time. Label the calculation with a comment and print the correlation to screen.
Calculate the total number of samples using the Length Function. Label the calculation with a comment and print the number of samples to screen.
Consult the pearson_correlation_coefficient_table and determine the critical value for a statistically significant correlation. In your Docstrings, answer the following question: Is the correlation between the Eruption Length and Waiting Time statistically significant?

Regression#

Using the Linear Regression function, calculate the model parameters for the Linear Regression Model. Label the calculations with comments and print the model coefficients to screen.
In your Docstrings, interpret the meaning of the regression parameters in terms of the variables being observed.
Using matplotlib’s plot() function, plot the regression line on top of the scatterplot. Label the line with a python_string that contains the Linear Regression Model line of best fit. Make sure to display the legend on the graph.

Hint

Make sure the color of the regression line is set to something different than the color of the scatterplot points.

Make an extrapolation using the Linear Regression Model. Label the calculation with comments. In your Docstrings, write a sentence explaining the meaning of the calculated value.

Hint

Recall an extrapolation is when the Linear Regression Model is used to predict a value of $y$ outside of the observed range of x.

In your Docstrings, answer the following question: Based on your answer to #3 in the previous section (the one about statistical significance), should this extrapolation be used to make a prediction? Why or why not?

Residual Analysis#

Create a histogram for the distribution of residuals for the Linear Regression Model. In your Docstrings, describe the distribution residuals in a few sentence. Be sure to include descriptions of: shape, location, variation and outliers.
Create a QQ plot for the distribution of residuals for the Linear Regression Model. In your Docstrings, answer the following question: does the QQ Plot for the residuals support or contradict the model assumption of normality?

Conclusion#

Are the assumptions of the Linear Regression Model satisifed by this dataset? In other words, is the Linear Regression Model a good way of modelling the relationship between the two variables? Why or why not?

Spice Girl Song Length#

Scatter Plot#

Construct a scatter plot for this dataset using the Track Number as the predictor variable and the Song Length as the response variable. Ensure the graph is labelled appropriately.
In your Docstrings, describe the correlation in this dataset. Is it positive or negative? Is it strong or weak? Is it linear or non-linear? If it is non-linear, what type of functional form could be used to model the dataset?
In your Docstrings, answer the following question: Based on your answer to the previous question, would a linear regression model be a good fit for this dataset?

Correlation#

Using the Correlation function, calculate the correlation between the Track Number and the Song Length. Label the calculation with a comment and print the correlation to screen.
Calculate the total number of samples using the Length Function. Label the calculation with a comment and print the number of samples to screen.
Consult the pearson_correlation_coefficient_table and determine the critical value for a statistically significant correlation. In your Docstrings, answer the following question: Is the correlation between the Track Number and Song Length statistically significant?

Regression#

Using the Linear Regression function, calculate the model parameters for the Linear Regression Model. Label the calculations with comments and print the model coefficients to screen.
In your Docstrings, interpret the meaning of the regression parameters in terms of the variables being observed.
Using matplotlib’s plot() function, plot the regression line on top of the scatterplot. Label the line with a python_string that contains the Linear Regression Model line of best fit. Make sure to display the legend on the graph.

Hint

Make sure the color of the regression line is set to something different than the color of the scatterplot points.

Make an extrapolation using the Linear Regression Model. Label the calculation with comments. In your Docstrings, write a sentence explaining the meaning of the calculated value.

Hint

Recall an extrapolation is when the Linear Regression Model is used to predict a value of $y$ outside of the observed range of x.

In your Docstrings, answer the following question: Based on your answer to #3 in the previous section (the one about statistical significance), should this extrapolation be used to make a prediction? Why or why not?

Residual Analysis#

Create a histogram for the distribution of residuals for the Linear Regression Model. In your Docstrings, describe the distribution residuals in a few sentence. Be sure to include descriptions of: shape, location, variation and outliers.
Create a QQ plot for the distribution of residuals for the Linear Regression Model. In your Docstrings, answer the following question: does the QQ Plot for the residuals support or contradict the model assumption of normality?

Conclusion#

Are the assumptions of the Linear Regression Model satisifed by this dataset? In other words, is the Linear Regression Model a good way of modelling the relationship between the two variables? Why or why not?

Celebrity Twitter#

Scatter Plot#

Construct a scatter plot for this dataset using the Tweet Count as the predictor variable and the Follower Count as the response variable. Ensure the graph is labelled appropriately.
In your Docstrings, describe the correlation in this dataset. Is it positive or negative? Is it strong or weak? Is it linear or non-linear? If it is non-linear, what type of functional form could be used to model the dataset?
In your Docstrings, answer the following question: Based on your answer to the previous question, would a linear regression model be a good fit for this dataset?

Correlation#

Using the Correlation function, calculate the correlation between the Tweet Count and the Follower Count. Label the calculation with a comment and print the correlation to screen.
Calculate the total number of samples using the Length Function. Label the calculation with a comment and print the number of samples to screen.
Consult the pearson_correlation_coefficient_table and determine the critical value for a statistically significant correlation. In your Docstrings, answer the following question: Is the correlation between the Tweet Count and Follower Count statistically significant?

Regression#

Using the Linear Regression function, calculate the model parameters for the Linear Regression Model. Label the calculations with comments and print the model coefficients to screen.
In your Docstrings, interpret the meaning of the regression parameters in terms of the variables being observed.
Using matplotlib’s plot() function, plot the regression line on top of the scatterplot. Label the line with a python_string that contains the Linear Regression Model line of best fit. Make sure to display the legend on the graph.

Hint

Make sure the color of the regression line is set to something different than the color of the scatterplot points.

Make an extrapolation using the Linear Regression Model. Label the calculation with comments. In your Docstrings, write a sentence explaining the meaning of the calculated value.

Hint

Recall an extrapolation is when the Linear Regression Model is used to predict a value of $y$ outside of the observed range of x.

In your Docstrings, answer the following question: Based on your answer to #3 in the previous section (the one about statistical significance), should this extrapolation be used to make a prediction? Why or why not?

Residual Analysis#

Create a histogram for the distribution of residuals for the Linear Regression Model. In your Docstrings, describe the distribution residuals in a few sentence. Be sure to include descriptions of: shape, location, variation and outliers.
Create a QQ plot for the distribution of residuals for the Linear Regression Model. In your Docstrings, answer the following question: does the QQ Plot for the residuals support or contradict the model assumption of normality?

Conclusion#

Are the assumptions of the Linear Regression Model satisifed by this dataset? In other words, is the Linear Regression Model a good way of modelling the relationship between the two variables? Why or why not?

Extra Credit: Data Transformations#

One of the three datasets we have examined in this project has a non-linear correlation. In order to fit a Linear Regression Model to this dataset, one of the variables must be transformed before the model parameters are estimated. In your Docstrings, explain which of the calculated correlations is non-linear and then answer the following question:

What type of data transformation would make the relationship between the variables linear?

Then, taking the transformation you have identified, transform the indicated dataset and re-apply the analysis we have done in this lab: create the graphical representations, perform the calculations, write the analysis and then answer the following question:

Compare the mean square error of both models. In other words, once the dataset is transformed and a model is fitted to the transformed data, what happens to the mean square error of the prediction? Label your calculations with comments.

Datasets#

Loading Data#

The following code snippet will load in a CSV spreadsheet named example.csv, parse it into a list and then print it to screen, assuming that CSV file is saved in the same folder as your script. Modify this code snippet to fit the datasets in this lab and then use it to load in the provided datasets in Datasets section.

import csv

# read in data
with open('example.csv') as csv_file:
    csv_reader = csv.reader(csv_file)
    raw_data = [ row for row in csv_reader ]

# separate headers from data
headers = raw_data[0]
columns = raw_data[1:]

# grab first column from csv file and ensure it's a number (not a string)
column_1 = [ float(row[0]) for row in columns ]

print(column_1)

Celebrity Twitter#

You can download the full dataset here

The following table is a preview of the data you will be using for this project.

Celebrity Twitter Followers and Tweet Count#
twitter_username	twitter_userid	domain	name	followers_count	tweet_count
BarackObama	813286	obamabook.com	BarackObama	13444655	16467
justinbieber	27260086	smarturl.it	Justin Bieber	114357427	31399
katyperry	21447363	katyperry.com	KATY PERRY	108900656	11625
rihanna	79293791	rihannanow.com	Rihanna	106201663	10630
Cristiano	155659213		Cristiano Ronaldo	99274403	3780
taylorswift13	17919972	grmypro.co	Taylor Swift	90373941	716
ladygaga	14230524		The Countess	84576292	9744
elonmusk	44196397		Elon Musk	82898543	17487
TheEllenShow	15846407	ellentube.com	Ellen DeGeneres	77595645	23819

The fifth column represents the number of followers for a given Twitter user. The sixth column represents the number of tweets for a given Twitter user.

Old Faithful#

You can download the full dataset here.

The following table is a preview of the data you will be using for this project.

Old Faithful Eruption and Waiting Times#
eruptions	waiting
3.6	79
1.8	54
3.333	74
2.283	62
4.533	85
2.883	55
4.7	88
3.6	85
1.95	51
4.35	85
1.833	54
3.917	84
4.2	78
1.75	47
4.7	83
2.167	52

The first column represents the length of the eruption in minutes. The second column represents the waiting time in minutes until the next eruption.

Spice Girls Song Length#

You can download the full dataset here.

The following table is the a preview of the data you will be using for this project.

Spice Girl Song Lengths#
album_release_date	album_release_year	duration_ms	track_name	track_number	album_name	key_name	mode_name	key_mode
2000-01-01	2000	255866	Holler	1	Forever	A#	minor	A# minor
2000-01-01	2000	254666	Tell Me Why	2	Forever	F	minor	F minor
2000-01-01	2000	298293	Let Love Lead The Way	3	Forever	B	major	B major
2000-01-01	2000	251000	Right Back At Ya	4	Forever	G#	major	G# major
2000-01-01	2000	226266	Get Down With Me	5	Forever	C#	minor	C# minor
2000-01-01	2000	254773	Wasting My Time	6	Forever	F	minor	F minor

The third column represents the song length in milliseconds. The fifth column represents the track number of the song on the studio album on which it was released.

Linear Regression

Contents

Linear Regression#

Instructions#

Background#

Correlation#

Linear Regression Model#

Residuals#

Datasets#

Old Faithful#

Spice Girls Songs#

Celebrity Twitter#

Bivariate Analysis#

Scatter Plots#

Correlation#

Regression Parameters#

Line of Best Fit#

Residuals#

Project#

Old Faithful#

Scatter Plot#

Correlation#

Regression#

Residual Analysis#

Conclusion#

Spice Girl Song Length#

Scatter Plot#

Correlation#

Regression#

Residual Analysis#

Conclusion#

Celebrity Twitter#

Scatter Plot#

Correlation#

Regression#

Residual Analysis#

Conclusion#

Extra Credit: Data Transformations#

Datasets#

Loading Data#

Celebrity Twitter#

Old Faithful#

Spice Girls Song Length#

References#