Setup a database with one table containing sample data.

Run: ./build.sh

This Docker image uses Flyway to manage the database migration scripts for the 'sample-data-db' database used by MIP.

This database contains the data used for testing and debugging purposes.

Run:

$ docker run -i -t --rm -e FLYWAY_HOST=`hostname` hbpmip/sample-data-db-setup:0.7.1 migrate

where the environment variables are:

• FLYWAY_HOST: database host, default to 'db'.
• FLYWAY_PORT: database port, default to 5432.
• FLYWAY_DATABASE_NAME: name of the database or schema, default to 'data'
• FLYWAY_URL: JDBC url to the database, constructed by default from FLYWAY_DBMS, FLYWAY_HOST, FLYWAY_PORT and FLYWAY_DATABASE_NAME
• FLYWAY_DRIVER: Fully qualified classname of the jdbc driver (autodetected by default based on flyway.url)
• FLYWAY_USER: database user, default to 'data'.
• FLYWAY_PASSWORD: database password, default to 'data'.
• FLYWAY_SCHEMAS: Optional, comma-separated list of schemas managed by Flyway
• FLYWAY_TABLE: Optional, name of Flyway's metadata table (default: schema_version)

After execution, you should have:

• A table named SAMPLE_DATA containing the values of dataset linreg_sample coming from file linreg_sample.csv
• A table named CHURN containing the values of dataset churn coming from file churn.csv
• A table named IRIS containing the values of dataset iris coming from file iris.csv
• A table named SAMPLE_DATA containing the values of dataset linreg_sample coming from file linreg_sample.csv
• A table named cde_features_A containing the values of datasets desd-synthdata coming from file desd-synthdata.csv
• A table named cde_features_B containing the values of datasets nida-synthdata coming from file nida-synthdata.csv
• A table named cde_features_C containing the values of datasets qqni-synthdata coming from file qqni-synthdata.csv
• A table named mip_cde_features containing the values of datasets desd-synthdata, nida-synthdata, qqni-synthdata coming from file desd-synthdata.csv, nida-synthdata.csv and qqni-synthdata.csv respectively

Run: ./build.sh

Run: ./publish.sh

Dataset generated from https://richarddmorey.shinyapps.io/test/solve.Rmd using ID number 2049324 and secret 's3Cr34'

First perform the linear regression analysis with only score.math.course1 as the independent variable, then perform the linear regression analysis with both high school math courses as predictors.

Solution
term 	estimate 	std.error 	statistic 	p.value
(Intercept) 	1.1577 	1.7023 	0.6801 	0.4975
score.math.course1 	1.0253 	0.0288 	35.5961 	0.0000

Report (p) values for both slopes.
Solution
term 	estimate 	std.error 	statistic 	p.value
(Intercept) 	-2.2264 	2.0310 	-1.0962 	0.2748
score.math.course1 	0.2020 	0.2856 	0.7072 	0.4806
score.math.course2 	0.8098 	0.2796 	2.8964 	0.0044

Report (R^2) and adjusted (R^2).
Solution
	Dependent variable:

	college.math
score.math.course1 	0.202
	(0.286)

score.math.course2 	0.810***
	(0.280)

Constant 	-2.226
	(2.031)

Observations 	150
R2 	0.901
Adjusted R2 	0.900
Residual Std. Error 	9.551 (df = 147)
F Statistic 	669.368*** (df = 2; 147)
Note: 	p<0.1; p<0.05; p<0.01

Copyright (C) 2017 LREN CHUV

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

This work has been funded by the European Union Seventh Framework Program (FP7/2007­2013) under grant agreement no. 604102 (HBP)

This work is part of SP8 of the Human Brain Project (SGA1).