## Demand in the UK

### Description

We have access to data from a random sample of grocery stores in the UK. The data includes information on unit sales in a category during one month in each store in a store panel in the UK. The data set contains one variable, 'demand_uk'. 

### Variables

A data frame with 572 observations on 2 variable

* id = Store identifier
* demand_uk = Total category sales in units per store

## Diamond prices

Prices of 3,000 round cut diamonds

### Description

A dataset containing the prices and other attributes of a sample of 3000 diamonds. The variables are as follows:

### Variables

- price = price in US dollars ($338--$18,791)
- carat = weight of the diamond (0.2--3.00)
- clarity = a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
- cut = quality of the cut (Fair, Good, Very Good, Premium, Ideal)
- color = diamond color, from J (worst) to D (best)
- depth = total depth percentage = z / mean(x, y) = 2 * z / (x + y) (54.2--70.80)
- table = width of top of diamond relative to widest point (50--69)
- x = length in mm (3.73--9.42)
- y = width in mm (3.71--9.29)
- z = depth in mm (2.33--5.58)
- date = shipment date

### Additional information

<a href="http://www.diamondse.info/diamonds-clarity.asp" target="_blank">Diamond search engine</a>

## DVD

### Description

The data contain information on a sample of 20,000 customers who received an "instant coupon." The value of the coupon was varied between \$1 and \$5 and randomly assigned to the selected customers.

Our interest is in estimating the effect of the coupon on purchase of a newly released DVD. We will also investigate the role of two additional variables: `purch` is a measure of frequency of purchase and `last` is a measure of the recency of the last purchase by a customer. These measures are often used in practice to predict response rates.

Customers who received the coupon and purchased the DVD are identified in the data by the variable `buy`. Because the variable we want to predict is binary (`buy` = `yes` if the customer purchased the DVD and `buy` = `no` if she did not), logistic regression is appropriate.

### Variables

* buy = `yes` if the customer purchased the DVD and `no` if she did not
* coupon = value of an "instant coupon" in dollars. the value varies between \$1 and \$5
* purch = number of purchases by the customer in the past year
* last = days since the last purchase by the customer
* training = 70/30 split, 1 for training sample, 0 for validation sample

### Source

The dataset `dvd.rds` is available for download from <a href = "https://radiant-rstats.github.io/docs/examples/dvd.rds" target="_blank">GitHub</a>.