## MP3 player

### Description

Conjoint data from one respondent. Ratings reflect the evaluation of 18 alternative MP3 players. The 18 profiles are an orthogonal fractional factorial design.

### Format

A data frame with 18 observations on 6 variables.

- Rating =	 Respondent rating on a scale from 1-100. Higher ratings imply a preferred profile.
- Memory	= GB of memory in the MP3 player
- Radio = Does the MP3 player have a radio yes or no
- Size = Size of the MP3 player (demo provided)
- Price = Price of the MP3 player
- Shape = Shape of the MP3 player

## Newspaper readership

### Description

Data from a sample for 580 newspaper readers indicating which newspaper they read most frequently (USA today or Wall Street Journal) and their level of income (Low income vs. High income).

### Variables

A data frame with 580 observations on 3 variables

- id = Respondent identifier
- Income = A factor indicating if a respondent has High or Low income. Factor was created using a median split on income data
- Newspaper = A factor indicating the primary newspaper the respondent reads (USA today or Wall Street Journal)

## Comic publishers

### Variables

- publisher = name of the publisher
- yr_founded = year the publisher was founded

<a href="http://stat545-ubc.github.io/bit001_dplyr-cheatsheet.html" target="_blank">Source</a>

# Movie ratings

Use collaborative filtering to create recommendations based on ratings from existing users. Set the filter to "training == 1" to predict ratings for movies 6-10 for user U11.

## Variables

* Users: Users 1-11
* Movies: Movies 1-10
* Rating: Movie rating on a 1-5 scale
* training: Training variable that is 0 for user U11 and 1 for all other users

## Retailer positioning

### Description

The data represent consumer evaluations for a set of retailers in the Chicago area on 7 attributes. In addition to attribute evaluations the dataset contains preference ratings on a scale from 1-9 for each retailer for two pre-defined consumer segments.

### Variables

A data frame with 6 rows of data on 10 variables. All numbers are averages across respondents.

- retailer = The name of the retailer consumers evaluated
- good_value = How do you perceive the value of the product offerings by retailer X to be on a scale from 1-9?
- quality_prod = How do you perceive the quality of goods sold by retailer X to be on a scale from 1-9?
- service = How do you perceive the service offered by retailer X to be on a scale from 1-9?
- convenience = How do you perceive the convenience of shopping at retailer X to be on a scale from 1-9?
- assortment = How do you perceive the assortment at retailer X to be on a scale from 1-9?
- sophisticated = How do you perceive the sophistication of shoppers at retailer X to be on a scale from 1-9?
- cluttered = How cluttered do you perceive the shopping environment at retailer X to be on a scale from 1-9?
- segment1 = Average preference score for each retailer on a scale from 1-9 across respondents assigned to segment 1
- segment2 = Average preference score for each retailer on a scale from 1-9 across respondents assigned to segment 2