What is your DANL 101 session schedule?

MWF 9:30am-10:20am TTh 3:30pm-4:45pm

What is your first name?

What is your last name?

Please enter your Geneseo email address without @geneseo.edu. For example, if your full Geneseo email address is abcd234@geneseo.edu, you should enter only abcd234. If you do not have a Geneseo email, please provide your full email address (e.g., abcd@gmail.com).

What is your GitHub username?

How many times have you submitted this assignment so far?

1. Complete [?] to compute the weighted mean of the vector scores <- c(85, 90, 88, 92, 87) with corresponding weights weights <- c(1, 2, 1, 1, 3).

scores <- c(85, 90, 88, 92, 87)
weights <- c(1, 2, 1, 1, 3)
weighted_mean <- [?]

2. Complete [Blank 1], [Blank 2], and [Blank 3] to compute the interquartile range (IQR) of the following vector x <- c(5, 7, 6, 9, 100, 8, 5, 7, 6) manually, without using the IQR() function.

x <- c(5, 7, 6, 9, 100, 8, 5, 7, 6)
q1 <- [Blank 1](x, 0.25)
q3 <- [Blank 2](x, [Blank 3])
iqr_value <- q3 - q1

3. Complete [Blank 1] and [Blank 2] to detect the outliers in the vector x <- c(5, 7, 6, 9, 100, 8, 5, 7, 6) using the 1.5*IQR rule.

lower_bound <- q1 - 1.5 * iqr_value
upper_bound <- q3 + 1.5 * iqr_value
outliers_lower <- [Blank 1]
outliers_upper <- [Blank 2]

4. Complete [?] to calculate the skewness of the vector x <- c(3, 5, 8, 12, 14, 15, 18, 20) without using any external R package. Skewness is defined as: \[ \text{Skewness} = \frac{n}{(n-1)(n-2)} \sum \left(\frac{x_i - \bar{x}}{s}\right)^3, \] where \(s\) is the standard deviation of the vector x.

x <- c(3, 5, 8, 12, 14, 15, 18, 20)
n <- length(x)
mean_x <- mean(x)
sd_x <- sd(x)
skewness <- [?]

5. Complete [Blank 1] and [Blank 2] to calculate mode_v, the mode of a numeric vector v <- c(2, 3, 5, 5, 6, 7, 3) using the mfv() function provided by the R package modeest.

v <- c(2, 3, 5, 5, 6, 7, 3, 5)
mode_v <- [Blank 1]::[Blank 2](v)

6. Complete [?] to calculate, \(z\), the vector for the standardized values in the vector x <- c(10, 20, 30, 40, 50). The standardized value of the individual value, \(z_{i}\), is defined as: \[ z_{i} = \frac{x_{i} - \bar{x}}{s}, \] where:

\(x_{i}\): \(i^{th}\) value in the vector \({x}\)
\(\bar{x}\): the mean of values in \({x}\)
\(s\): the standard deviation of values in \(x\)
\(z_{i}\): \(i^{th}\) value in the vector \({z}\), the vector of the standardized values in \(x\)

x <- c(10, 20, 30, 40, 50)
z <- [?]

1. Data analytics aims to replace traditional business and economics courses. (True/False)

True False

2. Data analysts are the only professionals who benefit from data analytics skills. (True/False)

True False

3. Python and R are the only programming languages used in data science. (True/False)

True False

4. The use of generative AI requires a deep understanding of the subject matter to apply it effectively. (True/False)

True False

5. Machine learning algorithms need to be explicitly programmed for each task they perform. (True/False)

True False

6. Which of the following skills is NOT typically covered in traditional business or economics classes?

Finding and cleaning datasets Economic modeling Market analysis Investment portfolio management

7. Which of the following is a key reason why R is widely used in data analysis?

It is a paid software It is closed source It is specifically designed for statistical computing It is mostly used for web development

8. Which question would you ask to analyze season ticket renewals in sports analytics?

How many players are on the team? What factors drive last-minute individual seat ticket purchases? What are the financial benefits of dynamic pricing? Which type of fan engages most with team merchandise?

9. Which of the following is a benefit of using Git in software development?

It helps analyze financial data It tracks changes and helps manage multiple versions of a project It predicts future trends using machine learning It assists in generating reports and dashboards

10. Which of the following is a key application of data analytics in the retail sector?

Enhancing physical store layouts based on customer behavior Developing machine learning algorithms Analyzing sports team tactics Predicting election results

11. Why are R and Python considered important tools for data analytics?

12. Explain why understanding the output of Generative AI tools like ChatGPT is important for data analysts or data scientists.

13. How does dynamic ticket pricing work in sports analytics?

14. How do business intelligence (BI) tools assist in decision-making for businesses?

Name, Email, and Session

R Basics

Short-Answer Questions