Grant Brown grantbrown

## ScoreData.csv

          
            AvgScore
            Age
            Caffeine
            NumberOfGamesPlayed

            
              172.231758565115
              41
              8.27310138781406
              5

            
              344.080816385481
              45
              166.788674010494
              4

            
              158.243974439236
              47
              0
              5

            
              161.762852648026
              52
              0
              4

            
              450.046699030962
              50
              341.247032165972
              7

            
              468.416022742364
              63
              376.400720606928
              4

            
              374.036406183761
              38
              228.098363606319
              3

            
              312.256335733073
              59
              144.104941199508
              11

            
              466.517013106131
              55
              339.59090467707
              6

## MachineData.csv

          
            partQuality
            machineNumber
            speedSetting
            shopTemperature
            shopHumidity
            dayOfWeek
            materialHardness
            materialColor
            volumeRemoved

            
              24
              1
              3
              66
              55
              1
              33.3
              red
              66.4977751111701

            
              100
              2
              4
              82
              63
              2
              75
              green
              141.814186119158

            
              40
              2
              4
              65
              69
              5
              33.3
              red
              52.6887914269801

            
              -2
              2
              1
              67
              60
              5
              33.3
              blue
              90.0792218314648

            
              19
              1
              1
              67
              72
              6
              75
              green
              72.4268319792361

            
              141
              1
              8
              72
              54
              4
              33.3
              red
              160.989211437966

            
              60
              2
              5
              68
              76
              6
              33.3
              red
              100.177360139193

            
              308
              2
              9
              68
              44
              6
              75
              green
              121.351584377482

            
              31
              3
              3
              72
              53
              5
              33.3
              red
              74.7642070437819

## Hw6P2.R

# Change my working directory to wherever the data file is stored
setwd("/home/grantbrown/dev/171-162-Materials/Section4/Homework5/")

CancerData = read.csv("Homework5.csv", head = TRUE)

# Fit the reduced model
ReducedModel = lm(AllCancerMortalityCrude ~ HospitalBeds, data = CancerData)

# Fit the full model

## HousingData.csv

          
            HouseAge
            HouseSize
            HousePrice

            
              201.527404291113
              2
              71070.5079595287

            
              94.2024885946334
              2
              74816.492188726

            
              152.27541964276
              3
              82961.80114856

            
              147.102697959987
              3
              93921.3139520607

            
              54.6764066301875
              3
              92954.4300141439

            
              45.3843956164123
              2
              78265.1130040834

            
              27.0776491481433
              1
              63702.3190749916

            
              101.407357022128
              2
              79153.6384587674

            
              56.8029020549195
              1
              57338.8476613027

## SBP.csv

          
            Age
            Gender
            SBP

            
              58
              0
              158.65711773507

            
              65
              0
              159.342444046151

            
              56
              0
              153.516056039745

            
              61
              0
              155.236047551483

            
              65
              1
              149.324298269595

            
              48
              0
              140.730171877834

            
              53
              1
              137.740735663033

            
              54
              1
              141.256213419939

            
              67
              0
              159.101029802287

## Homework5Template.R

# Change my working directory to wherever the data file is stored
setwd("/home/grantbrown/dev/171-162-Materials/Section4/Homework5/")

CancerData = read.csv("Homework5.csv", head = TRUE)

# Perform the regression:
MyMLR = lm(AllCancerMortalityCrude ~ GraduationRate + HospitalBeds + PctFarmCorn, data = CancerData)

# Take a look at the regression summary:

## Homework5.csv

          
            County
            AllCancerMortalityCrude
            PctFarmCorn
            GraduationRate
            HospitalBeds
            NursingFacilities
            CCDI_Facilities

            
              adair
              308.3
              27.8
              0.97
              25
              3
              1

            
              adams
              275.5
              24.2
              0.947368421
              22
              1
              0

            
              allamakee
              244.2
              8.1
              0.961538462
              25
              4
              1

            
              appanoose
              299.1
              14
              0.782258065
              45
              2
              0

            
              audubon
              335
              37.3
              0.961538462
              25
              2
              2

            
              benton
              185.9
              40.1
              0.95
              25
              3
              1

            
              black.hawk
              210.4
              40.3
              0.901639344
              675
              11
              1

            
              boone
              238.5
              40.3
              0.921259843
              42
              4
              2

            
              bremer
              187.3
              35.6
              0.91503268
              50
              4
              1

## IQ_DI.txt
IQ DI
73 38
82 43
84 36
88 34
89 30
91 23
98 26
98 32
101 19

## Homework3Template.R
# Homework 3 R code template

# Before doing anything else, get a copy of HeartDiseaseDiabetes.csv
# This file is available on the github site, and an excel version is
# available on icon. Remember that to use the excel version, you need
# to open it in excel or libre office and save a copy as a csv.


# Once you have obtained a copy of the data set in csv format, take
# note of where you've saved it. We need to tell R to use that folder

## ConfidenceBandExample.R


# To illustrate the use of confidence bands, let's just make up
# a data example. First, to ensure that everyone's simulated data
# looks the same in case you want to work together, let's tell R
# to use the same starting point for random number generation.
# (don't worry about how this works)

set.seed(12345)
AvgScore	Age	Caffeine	NumberOfGamesPlayed
172.231758565115	41	8.27310138781406	5
344.080816385481	45	166.788674010494	4
158.243974439236	47	0	5
161.762852648026	52	0	4
450.046699030962	50	341.247032165972	7
468.416022742364	63	376.400720606928	4
374.036406183761	38	228.098363606319	3
312.256335733073	59	144.104941199508	11
466.517013106131	55	339.59090467707	6
partQuality	machineNumber	speedSetting	shopTemperature	shopHumidity	dayOfWeek	materialHardness	materialColor	volumeRemoved
24	1	3	66	55	1	33.3	red	66.4977751111701
100	2	4	82	63	2	75	green	141.814186119158
40	2	4	65	69	5	33.3	red	52.6887914269801
-2	2	1	67	60	5	33.3	blue	90.0792218314648
19	1	1	67	72	6	75	green	72.4268319792361
141	1	8	72	54	4	33.3	red	160.989211437966
60	2	5	68	76	6	33.3	red	100.177360139193
308	2	9	68	44	6	75	green	121.351584377482
31	3	3	72	53	5	33.3	red	74.7642070437819

	# Change my working directory to wherever the data file is stored
	setwd("/home/grantbrown/dev/171-162-Materials/Section4/Homework5/")

	CancerData = read.csv("Homework5.csv", head = TRUE)

	# Fit the reduced model
	ReducedModel = lm(AllCancerMortalityCrude ~ HospitalBeds, data = CancerData)

	# Fit the full model
HouseAge	HouseSize	HousePrice
201.527404291113	2	71070.5079595287
94.2024885946334	2	74816.492188726
152.27541964276	3	82961.80114856
147.102697959987	3	93921.3139520607
54.6764066301875	3	92954.4300141439
45.3843956164123	2	78265.1130040834
27.0776491481433	1	63702.3190749916
101.407357022128	2	79153.6384587674
56.8029020549195	1	57338.8476613027
Age	Gender	SBP
58	0	158.65711773507
65	0	159.342444046151
56	0	153.516056039745
61	0	155.236047551483
65	1	149.324298269595
48	0	140.730171877834
53	1	137.740735663033
54	1	141.256213419939
67	0	159.101029802287
County	AllCancerMortalityCrude	PctFarmCorn	GraduationRate	HospitalBeds	NursingFacilities	CCDI_Facilities
adair	308.3	27.8	0.97	25	3	1
adams	275.5	24.2	0.947368421	22	1	0
allamakee	244.2	8.1	0.961538462	25	4	1
appanoose	299.1	14	0.782258065	45	2	0
audubon	335	37.3	0.961538462	25	2	2
benton	185.9	40.1	0.95	25	3	1
black.hawk	210.4	40.3	0.901639344	675	11	1
boone	238.5	40.3	0.921259843	42	4	2
bremer	187.3	35.6	0.91503268	50	4	1
	# Homework 3 R code template

	# Before doing anything else, get a copy of HeartDiseaseDiabetes.csv
	# This file is available on the github site, and an excel version is
	# available on icon. Remember that to use the excel version, you need
	# to open it in excel or libre office and save a copy as a csv.


	# Once you have obtained a copy of the data set in csv format, take
	# note of where you've saved it. We need to tell R to use that folder


	# To illustrate the use of confidence bands, let's just make up
	# a data example. First, to ensure that everyone's simulated data
	# looks the same in case you want to work together, let's tell R
	# to use the same starting point for random number generation.
	# (don't worry about how this works)

	set.seed(12345)