Python Lesson 3: Polynomial Regression

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来自 Wesleyan University 的课程

回归建模实践

232 个评分

Wesleyan University

回归建模实践

232 个评分

课程 3（共 5 门，Specialization Data Analysis and Interpretation）

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

从本节课中

Multiple Regression

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set. When you go back to your codebooks, ask yourself a few questions like “What other variables might explain the association between my explanatory and response variable?”; “What other variables might explain more of the variability in my response variable?”, or even “What other explanatory variables might be interesting to explore?” Additional explanatory variables can be either quantitative, categorical, or both. Although you need only two explanatory variables to test a multiple regression model, we encourage you to identify more than one additional explanatory variable. Doing so will really allow you to experience the power of multiple regression analysis, and will increase your confidence in your ability to test and interpret more complex regression models. If your research question does not include one quantitative response variable, you can use the same quantitative response variable that you used in Module 2, or you may choose another one from your data set.

Python Lesson 1: Multiple Regression6:06

Python Lesson 2: Confidence Intervals3:36

Python Lesson 3: Polynomial Regression9:15

Python Lesson 4: Evaluating Model Fit, pt. 15:04

Python Lesson 5: Evaluating Model Fit, pt. 210:12

与讲师见面

Jen Rose
Research Professor
Psychology
Lisa Dierker
Professor
Psychology

So far, we have used regression to test linear associations

between our explanatory variables, and our response variable.

By linear,

we mean that the association can be explained best with a straight line.

Here is a scatter plot showing a linear association between urban rate and

Internet use rate from the gap minder data set.

That is we can a draw a straight line to the scatter plot and

this regression line does a pretty good job of catching the association.

But what if the association is not linear?

That is, what if the association is curvilinear?

For example, take the relationship between anxiety and performance.

It is a well-established phenomenon that both low anxiety and

high anxiety are related to poor performance.

But that a moderate level of anxiety is optimal to perform at your best.

If you drew a straight linear aggression line with these points.

Most points would be really far away from the line,

meaning that there's a lot of prediction there.

The best fitting line is not straight rather it is one that curve to

catch the non-linear nature of these association.

Here is an example of a less extreme curve linear association between urban rate, and

female employment rate, with a linear regression line.

Returning to the Python script for

the gap minder data set, the code to produce this scatter plot is here.

Here we are creating an object called scat1, that will be our scatter plot.

We then use the C born rig plot function,

to plot our explanatory x variable urban rate.

In our Y response variable, female employment rate separated by a comma.

Note that the variable names for

both variables have quotation marks around them.

After another comma, we add scatter=True to tell Python to plot a scatter plot and

then we tell it which data set to use.

You can see that it looks like female employment rate decreases as

urbanization rate increases.

But that around urban rates of 80 or

higher, it looks like female employment rate appears to increase.

So it looks like kind of a u-shaped association.

Just like with the anxiety and performance association a straight linear regression

line isn't doing a good job of picking up the curvilinear part of the association.

We can actually fit a line that curves, to better fit the association,

by adding a polynomial term.

For example, we could add a second order polynomial, or quadratic term,

to draw the line of best fit that captures the curvature that we're seeing.

To do this, we use the same seaborn regplot function code that was used to

draw the scatter plot with the linear brushing line.

The difference is that we add some additional code within the parentheses

with the seaborn regplot function.

We add order=2 to ask for a quadratic line that includes the second order polynomial.

If we run the code for both the linear and quadratic scatterplots at the same time.

We will get a single scatterplot with both the straight linear line and

the curved rush line.

Now my scatterplot shows the original linear regression line in blue,

and the quadratic regression line in green.

Notice how the quadratic line does a better job of capturing the association

at lower and higher urbanization rates.

The points at these levels are closer to the quadratic, or

second-order polynomial curve.

Meaning that the expected or predicted values are closer to the observed values.

So based on just looking at the two curves, it looks like the green quadratic

curve fits the data better than the blue straight line.

But we can be even more sure of this conclusion if we test to see whether

adding a second order polynomial term to our aggression model gives

us a significantly better fitting model.

I do this by simply adding another variable that is the squared value of my

explanatory x variable, x squared, to my regression model.

First, let's test my regression model for

just the linear association between urbanization rate and female employment

rate using the ols function from the stats model API formula library.

Note that we have centered our urban rate quantitative explanatory variable.

Urban rate, underscore, c.

Centering is especially important when testing a polynomial regression model.

Because it makes it considerably easier to interpret the regression coefficients.

If we look at the results, we can see from the significant P value and

negative parameter estimate that female employment rate

is negatively associated with urbanization rate.

So the linear association, the blue line in the scatter plot,

is statistically significant.

But the R-square is 9%, indicating that the linear association of urban

rate is capturing only about 9% of the variability in female employment rating.

But what happens if we allow that straight line to curve by adding a second order

polynomial to that regression equation.

The Python code to do this is here.

As you can see, it's the same code as for

the linear regression model with the exception of some additional code.

Specifically if you look at the formula we will now see that we have added another

term to the model, that is the square of the urban rate explanatory variable.

Stats model uses patsy formula language.

Which is what we include inside the quotes when we name our response and

explanatory variables, and any other variables we want to include in the model.

Patsy provides a built-in function I, called the identity function,

that just returns the input of what's in the parentheses.

So in this case, the I, urbanrate star star two, returns

a variable that it urbanrate raised to the power of two, that is, urbanrate squared.

This term is our second order polynomial, or quadratic term.

When we look at the table of results, we see that the value for the linear term for

urban rate is negative, and the p value is less than 0.05.

In addition, the quadratic term is positive insignificant, indicating that

the curvilinear pattern we observed in our scatter plot is statistically significant.

A negative linear coefficient and

a positive quadratic coefficient indicates that the curve is convex.

Such that starts high, then goes down, and then starts to go up again.

This is consistent with the bowl shape pattern, this is consistent with bowl

shaped quadratic curve we saw earlier in our scatter plot.

In addition, you see that the R square increases to .16.

Which means that adding the quadratic term for

urban rate, increase the amount of variability in female employment rate

that can be explained by urbanization rate from 9% to 16%.

Together, these results suggest that the best fitting line for

this association is one that includes some curvature.

Note the two warnings in the output.

The first warning is simply reminding you that the estimation of the standard errors

assumes that your regression assumptions are met and

that your model is correctly specified.

The second warning indicates there are variables in the model that

are highly correlated with each other.

This is an indicator of multicorrelarity,

which can create some instability in the estimation of the parameter estimates.

And make it difficult to interpret them.

Ordinarily if we have two highly correlated explanatory variables we would

wanna put only one of them in the model.

Obviously the second order polynomial which is urban rate squared is

correlated with the urban rate.

However in this case,

we want to keep it in the model because we specifically want to capture the curve of

linear relationship that was evident in our scatter plot.

This brings us to another important characteristic of centering.

Centering significantly reduces the correlation between the linear and

quadratic variables in a polynomial regression model.

We can also test more complex non linear associations by adding higher order

polynomials.

For example, you can add cubic, third order polynomial.

Or even quartic, fourth order polynomial terms for the model to account for

more complex curves.

For example, this scatter plot shows more that one curve.

In this case, adding a cubic, or third order polynomial term,

might improve the fit of the model.

One thing to keep in mind now is that modeling is more complex curve in

the sample data, but often improve the fit of the model for that sample.

However, it also increases the risk of doing something called Overfitting.

Overfitting occurs when you get a model to explain you're responsible did really well

on the sample.

But that model becomes very specific to the sample data.

That is it capitalizes on the variability that's present in that sample.

The down side is that an overfitted model that fits the sample really well,

may not fit well at all on another sample drawn from the same population.

An overfitted model is biased toward the sample it was developed on.

And consequently, the conclusions we draw from that model

may not be representative of the population.

So, we try to establish balance,

where we identify a model that fits our sample well.

But will also fit well if we tested it from another sample from

the same population.

This is called the bias-variance tradeoff and we will discuss this in more detail in

the fourth course of the specialization machine learning for data analysis.

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