This assignment uses R programming language in Rstudio to analyze datasets for statistical analysis. This homework assignment is a graduate level assignment. Acceptable levels of collaboration include using notes, class materials, and online resources. When providing your answers, include your approach, the code you wrote and evaluated, the output, and your analysis and insights.

There are two parts to this assignment.

PART 1:

In Rstudio load the data using the following code: prst = read.csv("https://goo.gl/z5P8ce")

This is an illustrative dataset containing consumer ratings on product attributes for four consumer electronics brands. Consumers rated each brand on nine attributes using a 7-point ratings scale.

Using this data, answer the following questions:

1. Extract the principal components from the PRST data. How many components are required to explain the majority of variance in the PRST data? Why? Provide visual evidence for your answer.

2. Using principal components for the mean adjective ratings, plot the brands against the first two components. How might you interpret the output seen in this plot?

3. Suppose you are the brand manager for Sierra, and you wish to change your position vs. the market leader, Tango. What are some strategies suggested by the PCA positions?

4. Plot a multidimensional scaling (MDS) map for the PRST brands by computing distance scores using the mean adjective ratings. Which brands are most similar and most different?

5. How does the MDS map relate to the PCA positions in the exercises earlier above? What does it suggest for the strategy you considered in Question [3] above?

PART 2:

In Rstudio load the data using the following code: autocustdata = read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vQNojE540tjGSnxeNBkdwkbd72wiVKBHyIYkA6TCCOd9IjQaTRZuKdHiffR0ZNLmf3l6sFBIY-stBl6/pub?gid=0&single=true&output=csv")

This is an illustrative dataset containing information regarding potential customers for the automotive industry. It contains the following variables:

Customer ID, Gender, Age, Work Experience, and Family Size.

Using this data, answer the following questions:

6. How many segments are in the data? Why?

7. How are the segments different from each other? What are the key variables determining these segments?

8. Visualize the segments using the top two key variables. What observations can you make from this plot?