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Question 1 The objective of this question is to give practice with logical indexing. Suppose Andy Dwyer tracks his commute time to his women's studies class for ten days and records the following times (in minutes): 14 12 20 15 19 20 28 20 18 20 (a) Store Andy's commute times as a vector in your workspace called commute_times. Use a logical index to determine which days Andy had a commute time that was more than one standard deviation away (longer or shorter) from the average (mean) commute time? What were those commute times? Hint: In addition to the lecture notes, you may use the abs() function, if necessary. The abs() function computes the absolute value of the elements of a numeric vector. (b) Using the same logical index from (a), determine which days Andy had a commute time that was within one standard deviation (longer or shorter) of the mean commute time? What were those commute times? (c) Using the same logical index from (a), what proportion of days did Andy have a commute time that was within one standard deviation of the mean commute time? Hint: Can arithmetic operators/functions for numeric vectors work for logical vectors? What do sum() and mean() compute for logical vectors? Question 2 The objective of this question is to help further your understanding of Boolean operators and what NA represents. Logical vectors in R can contain NA in addition to TRUE and FALSE. An NA in a logical context can be interpreted as "unknown." Consider the following commands: NA & TRUE [1] NA NA & FALSE [1] FALSE NA | TRUE [1] TRUE NA | FALSE [1] NA Explain why there is a difference in the output of these four commands. Why are they not all NA? Hint: Does the output rely on knowing what the unknown value NA represents?

          Question 1
The objective of this question is to give practice with logical indexing.
Suppose Andy Dwyer tracks his commute time to his women's studies class for ten days and records the following times (in minutes):
14 12 20 15 19 20 28 20 18 20
(a)
Store Andy's commute times as a vector in your workspace called commute_times. Use a logical index to determine which days Andy had a commute time that was more than one standard deviation away (longer or shorter) from the average (mean) commute time? What were those commute times?
Hint: In addition to the lecture notes, you may use the abs() function, if necessary. The abs() function computes the absolute value of the elements of a numeric vector.
(b)
Using the same logical index from (a), determine which days Andy had a commute time that was within one standard deviation (longer or shorter) of the mean commute time? What were those commute times?
(c)
Using the same logical index from (a), what proportion of days did Andy have a commute time that was within one standard deviation of the mean commute time?
Hint: Can arithmetic operators/functions for numeric vectors work for logical vectors? What do sum() and mean() compute for logical vectors?
Question 2
The objective of this question is to help further your understanding of Boolean operators and what NA represents.
Logical vectors in R can contain NA in addition to TRUE and FALSE. An NA in a logical context can be interpreted as "unknown." Consider the following commands:
NA & TRUE
[1] NA
NA & FALSE
[1] FALSE
NA | TRUE
[1] TRUE
NA | FALSE
[1] NA
Explain why there is a difference in the output of these four commands. Why are they not all NA?
Hint: Does the output rely on knowing what the unknown value NA represents?

Question 1
The objective of this question is to give practice with logical indexing.
Suppose Andy Dwyer tracks his commute time to his women's studies class for ten days and records the following times (in minutes):
14 12 20 15 19 20 28 20 18 20
(a)
Store Andy's commute times as a vector in your workspace called commutetimes. Use a logical index to determine which days Andy had a commute time that was more than one standard deviation away (longer or shorter) from the average (mean) commute time? What were those commute times?
Hint: In addition to the lecture notes, you may use the abs() function, if necessary. The abs() function computes the absolute value of the elements of a numeric vector.
(b)
Using the same logical index from (a), determine which days Andy had a commute time that was within one standard deviation (longer or shorter) of the mean commute time? What were those commute times?
(c)
Using the same logical index from (a), what proportion of days did Andy have a commute time that was within one standard deviation of the mean commute time?
Hint: Can arithmetic operators/functions for numeric vectors work for logical vectors? What do sum() and mean() compute for logical vectors?
Question 2
The objective of this question is to help further your understanding of Boolean operators and what NA represents.
Logical vectors in R can contain NA in addition to TRUE and FALSE. An NA in a logical context can be interpreted as "unknown." Consider the following commands:
NA TRUE
[1] NA
NA FALSE
[1] FALSE
NA | TRUE
[1] TRUE
NA | FALSE
[1] NA
Explain why there is a difference in the output of these four commands. Why are they not all NA?
Hint: Does the output rely on knowing what the unknown value NA represents?

Added by Eric W.

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