https://github.com/grantgasser/multiple_regression
A simple multiple regression on two datasets (lm and closed form solution)
https://github.com/grantgasser/multiple_regression
least-squares multiple-regression r
Last synced: about 1 year ago
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A simple multiple regression on two datasets (lm and closed form solution)
- Host: GitHub
- URL: https://github.com/grantgasser/multiple_regression
- Owner: grantgasser
- Created: 2018-09-25T01:57:32.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-09-27T17:42:59.000Z (almost 8 years ago)
- Last Synced: 2025-02-08T18:14:36.577Z (over 1 year ago)
- Topics: least-squares, multiple-regression, r
- Language: R
- Size: 13.7 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Multiple regression on grocery store and brand preference datasets
### Grocery Store
#### A dataset from the textbook Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, & Li
This [dataset](http://users.stat.ufl.edu/~rrandles/sta4210/Rclassnotes/data/textdatasets/KutnerData/Chapter%20%206%20Data%20Sets/CH06PR09.txt) is based on the following: A large, national grocery retailer tracks productivity and costs of its facilities
closely. Data below were obtained from a single distribution center for a one-year period. Each
data point for each variable represents one week of activity. The variables included are the
number of cases shipped (X1) the indirect costs of the total labor hours as a percentage (X2),
a qualitative predictor called holiday that is coded 1 if the week has a holiday and 0 otherwise
(X3), and the total labor hours (Y).
### Brand Preference
#### A dataset from the textbook Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, & Li
This [dataset](http://users.stat.ufl.edu/~rrandles/sta4210/Rclassnotes/data/textdatasets/KutnerData/Chapter%20%206%20Data%20Sets/CH06PR05.txt) is based on the following: In a small-scale experimental study of the relation between degree of brand
liking (Y) and moisture content (X1) and sweetness (X2) of the product, the following results
were obtained from the experiment based on a completely randomized design (data are coded).
##### Note:
In addition to using the lm function, the closed form solution of the least squares method is used.