Data Analysis with Python Week 4 Quiz Answer

Data Analysis with Python 

Week 4 Quiz Answer

Practice Quiz 1

Linear Regression and Multiple Linear Regression

Q1) consider the following lines of code, what is the name of the column that contains the target values:

from sklearn.linear_model import LinearRegression 
1m=Linear regression() 
X = df[[ highway-mpg']]
Y = df['price']
lm.fit(X,Y)
Yhat=lm.predict(X)

• 'price'

• 'highway-mps

Q2) consider the following equation:

what is the parameter b_0 (b subscript 0)

• the predictor or independent variable

• the target or dependent variable

• the intercept 

• the slope

Practice Quiz 2

Model Evaluation using Visualization

Q1) Consider the following Residual Plot, is our linear model correct :

• yes

• Incorrect

Q2) Consider the following Residual Plot. is our linear model correct:

• yes

• No

Practice Quiz 3

Polynomial Regression and Pipelines

Q1) What functions are used to generate Polynomial Regression with more than one dimension

f=np.polyfit(x,y,3)
P=np.polyld(f)

pr=PolynomialFeatures(degree=2) 

pr.fit_transform([1,2], include_bias=False)

Q2) Select the line of code that imports the module required for polynomial features

• from sklearn.linear_model import LinearRegression

• from sklearn.preprocessing import PolynomialFeatures

• from sklearn.preprocessing import StandardScaler

Practice Quiz 4

Measures for In-Sample Evaluation

Q1) Consider the following lines of code: what value does the variable out contain?

lm = LinearRegression() 

Im.score(x,y) 

X = df[['highway-mpg']] 

Y = df['price'] 

lm.fit(X, Y) 

out=1m. score(x,y)

• The Coefficient of Determination or R^2

• Mean Squared Error

Q2) What value of R^2 (coefficient of determination) indicates your model performs worst?

• 1

• 0

Practice Quiz 5

Model Development

Q1) If the predicted function is:

The method is:

• Polynomial Regression

• Multiple Linear Regression

Q2) What steps do the following lines of code perform?

Input=[('scale',StandardScaler()), ('model', Linear Regression())]

pipe-Pipeline(Input)

pipe.fit(2,y) 

ypipe=pipe.predict(z)

• Standardize the data, then perform a polynomial transform on the features Z

• Find the correlation between Z and y

• Standardize the data, then perform a prediction using a linear regression model using the features Z and targets y

3. We create a polynomial feature as follows "PolynomialFeatures(degree=2)": what is the order of the polynomial?

• 0
• 1
• 2

Q4) Which statement is true about Polynomial linear regression?

• Polynomial linear regression is not linear in any way

• Although the predictor variables of Polynomial linear regression are not linear the relationship between the parameters or coefficients is linear

• Polynomiallinear regression uses linear Wavelets

Q5) The larger the mean square error, the better your model has performed

• True

• False

Q6) Consider the following equation:

What is the parameter b_0 (b subscript 0)?

• The predictor or independent variable
• The target or dependent variable
• The intercept 
• The slope

_____________________________________________________________

Data Analysis with Python

• Data Analysis with Python Week 1 Quiz Answer

• Data Analysis with Python Week 2 Quiz Answer

• Data Analysis with Python Week 3 Quiz Answer

• Data Analysis with Python Week 4 Quiz Answer

• Data Analysis with Python Week 5 Quiz Answer

_____________________________________________________________