ECE M148 Homework 1 Introduction to Data Science 

ECE M148 Homework 1
Introduction to Data Science 
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1. Consider the following data set A = {1, 1, 5, 9, 9}. What are the mean and median of
A? Now, consider B = {1, 1, 5, 9, 9, 11}. What are the mean and median of B? Using
the mean and median, compare A and B.
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2. In class, we discussed different ways to sample data. Explain in 1-2 sentences each the
advantages and disadvantages of:
(a) Random sampling
(b) Stratified sampling
(c) Systematic sampling
(d) Cluster sampling
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3. As discussed in class, many real-world datasets will contain missing or null values in
the data. List four different strategies you could reasonably use to address null values.
For each, clarify what the advantages and disadvantages to it are.
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4. Consider the following sampling scenarios and determine which type of sampling bias
is being demonstrated and explain your answer.
(a) Bob is a wealthy CEO who thinks taxes are too high. To confirm this hypothesis,
he asks all his wealthy CEO friends their opinion.
(b) Sally is a teacher who wants to know how her class is performing. She sends out
a survey with the following question: ”Do you feel like you will get an A in the
course or are you failing?”
(c) Constantine wants to know people’s opinion about his website. He posts a survey
link on his website asking for responses.
You may choose among the following options for the type of bias:
i) Response Bias
ii) Voluntary Bias
iii) Convenience Bias
iv) Under-coverage Bias
v) Over-coverage Bias
vi) Non-response bias
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5. Perform KNN Regression on the following data set for different values of K: (x, y) =
{(1, 1),(2, 4),(3.2, 6),(4, 3),(5, 2),(6, 2)}. Start by plotting the given points on a 2-D
grid and then fitting a KNN regressor for the different values of K:
Make sure to draw the regression plot from 0 to 7.
• K = 1
• K = 2
• K = 3
• K = 6
Contrast and compare your findings over various choices of K. Is a larger K always
better? Is K = 1 always better? Why or why not? Comment on what you think about
the KNN performing regression on all x < 1.
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