diff --git a/10-MultipleRegression-FactorialDesigns.html b/10-MultipleRegression-FactorialDesigns.html
index 9c7a03b..83f90e7 100644
--- a/10-MultipleRegression-FactorialDesigns.html
+++ b/10-MultipleRegression-FactorialDesigns.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -725,15 +737,15 @@ Verwijderen van
Linear Hypotheses:
Estimate Std. Error t value Pr(>|t|)
-poison: II - I == 0 0.04686 0.01744 2.688 0.07357 .
+poison: II - I == 0 0.04686 0.01744 2.688 0.07349 .
poison: III - I == 0 0.19964 0.01744 11.451 < 0.001 ***
poison: III - II == 0 0.15278 0.01744 8.763 < 0.001 ***
treat: B - A == 0 -0.16574 0.02013 -8.233 < 0.001 ***
-treat: C - A == 0 -0.05721 0.02013 -2.842 0.05098 .
+treat: C - A == 0 -0.05721 0.02013 -2.842 0.05093 .
treat: D - A == 0 -0.13583 0.02013 -6.747 < 0.001 ***
treat: C - B == 0 0.10853 0.02013 5.391 < 0.001 ***
treat: D - B == 0 0.02991 0.02013 1.485 0.61546
-treat: D - C == 0 -0.07862 0.02013 -3.905 0.00278 **
+treat: D - C == 0 -0.07862 0.02013 -3.905 0.00288 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)
@@ -752,15 +764,15 @@ Verwijderen van
Linear Hypotheses:
Estimate lwr upr
-poison: II - I == 0 0.0468641 -0.0027970 0.0965253
-poison: III - I == 0 0.1996425 0.1499814 0.2493036
-poison: III - II == 0 0.1527784 0.1031172 0.2024395
-treat: B - A == 0 -0.1657402 -0.2230840 -0.1083965
-treat: C - A == 0 -0.0572135 -0.1145573 0.0001302
-treat: D - A == 0 -0.1358338 -0.1931776 -0.0784901
-treat: C - B == 0 0.1085267 0.0511830 0.1658704
-treat: D - B == 0 0.0299064 -0.0274373 0.0872502
-treat: D - C == 0 -0.0786203 -0.1359640 -0.0212765
+poison: II - I == 0 0.0468641 -0.0027958 0.0965241
+poison: III - I == 0 0.1996425 0.1499826 0.2493024
+poison: III - II == 0 0.1527784 0.1031184 0.2024383
+treat: B - A == 0 -0.1657402 -0.2230826 -0.1083979
+treat: C - A == 0 -0.0572135 -0.1145559 0.0001288
+treat: D - A == 0 -0.1358338 -0.1931762 -0.0784915
+treat: C - B == 0 0.1085267 0.0511844 0.1658690
+treat: D - B == 0 0.0299064 -0.0274359 0.0872488
+treat: D - C == 0 -0.0786203 -0.1359626 -0.0212779
plot(comparisons,yaxt="none")
contrastNames <- c("II-I","III-I","III-II","B-A","C-A","D-A","C-B","D-B","D-C")
axis(2,at=c(length(contrastNames):1), labels=contrastNames,las=2)
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diff --git a/10-MultipleRegression.html b/10-MultipleRegression.html
index 517c94f..04f76b8 100644
--- a/10-MultipleRegression.html
+++ b/10-MultipleRegression.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/10-MultipleRegression.pdf b/10-MultipleRegression.pdf
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diff --git a/10-Recap-AlgemeenLineairModel-AdditiefModel.html b/10-Recap-AlgemeenLineairModel-AdditiefModel.html
index fb55d37..e09cf26 100644
--- a/10-Recap-AlgemeenLineairModel-AdditiefModel.html
+++ b/10-Recap-AlgemeenLineairModel-AdditiefModel.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/10-Recap-AlgemeenLineairModel-AdditiefModel.pdf b/10-Recap-AlgemeenLineairModel-AdditiefModel.pdf
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diff --git a/10-Recap-AlgemeenLineairModel-OverigeConcepten.html b/10-Recap-AlgemeenLineairModel-OverigeConcepten.html
index 81f492f..b8a15e3 100644
--- a/10-Recap-AlgemeenLineairModel-OverigeConcepten.html
+++ b/10-Recap-AlgemeenLineairModel-OverigeConcepten.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -917,9 +929,9 @@ Besluitvorming
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
-## Age == 0 0.43999 0.01511 29.127 <1e-10 ***
-## Age + Age:Gendermale == 0 0.24840 0.01879 13.217 <1e-10 ***
-## Age:Gendermale == 0 -0.19159 0.02411 -7.946 <1e-10 ***
+## Age == 0 0.43999 0.01511 29.127 < 2e-16 ***
+## Age + Age:Gendermale == 0 0.24840 0.01879 13.217 < 2e-16 ***
+## Age:Gendermale == 0 -0.19159 0.02411 -7.946 2.66e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -930,14 +942,14 @@ Besluitvorming
##
## Fit: lm(formula = BPSysAve ~ Age * Gender, data = bpData, weights = 1/mSd$fitted^2)
##
-## Quantile = 2.3168
+## Quantile = 2.3171
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## Age == 0 0.4400 0.4050 0.4750
-## Age + Age:Gendermale == 0 0.2484 0.2049 0.2919
+## Age + Age:Gendermale == 0 0.2484 0.2048 0.2919
## Age:Gendermale == 0 -0.1916 -0.2475 -0.1357
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diff --git a/Examenvoorbeeld_oefening_1.html b/Examenvoorbeeld_oefening_1.html
index 60aeaa9..3821e4a 100644
--- a/Examenvoorbeeld_oefening_1.html
+++ b/Examenvoorbeeld_oefening_1.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -768,7 +780,7 @@
Hypotheses testen
## Estimate Std. Error t value Pr(>|t|)
## bacon == 0 -1.19081 0.13182 -9.034 <0.001 ***
## bacon + bacon:sexmale == 0 -0.87572 0.06286 -13.930 <0.001 ***
-## bacon:sexmale == 0 0.31509 0.14603 2.158 0.0735 .
+## bacon:sexmale == 0 0.31509 0.14603 2.158 0.0734 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -779,15 +791,15 @@
Hypotheses testen
##
## Fit: lm(formula = permeat ~ bacon + length + sex + bacon:sex, data = pigs)
##
-## Quantile = 2.318
+## Quantile = 2.3202
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
-## Estimate lwr upr
-## bacon == 0 -1.1908 -1.4964 -0.8852
-## bacon + bacon:sexmale == 0 -0.8757 -1.0214 -0.7300
-## bacon:sexmale == 0 0.3151 -0.0234 0.6536
+## Estimate lwr upr
+## bacon == 0 -1.19081 -1.49667 -0.88496
+## bacon + bacon:sexmale == 0 -0.87572 -1.02158 -0.72986
+## bacon:sexmale == 0 0.31509 -0.02373 0.65391
Conclusie
@@ -798,7 +810,7 @@
Conclusie
(p<<0.001) als mannelijke varkens (p<0.001). Voor zeugen, die
een verschillende dikte van speklaag hebben, is het vleespercentage
gemiddeld 1.19 %/mm lager bij zeugen met de dikste speklaag (95% BI
-[0.89 %/mm, 1.5 %/mm]). Voor beren (mannelijke varkens), die een
+[0.88 %/mm, 1.5 %/mm]). Voor beren (mannelijke varkens), die een
verschillende dikte van speklaag hebben, is het vleespercentage
gemiddeld 0.88 %/mm lager bij beren met de dikste speklaag (95% BI [0.73
%/mm, 1.02 %/mm]). Er is op het 5% significantieniveau geen significant
diff --git a/Examenvoorbeeld_oefening_1.pdf b/Examenvoorbeeld_oefening_1.pdf
index f84ecfd..f46f66f 100644
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diff --git a/Examenvoorbeeld_oefening_1_opgave.html b/Examenvoorbeeld_oefening_1_opgave.html
index 1ea52c2..9073cf5 100644
--- a/Examenvoorbeeld_oefening_1_opgave.html
+++ b/Examenvoorbeeld_oefening_1_opgave.html
@@ -390,6 +390,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/Examenvoorbeeld_oefening_2.html b/Examenvoorbeeld_oefening_2.html
index fb7b90c..a407190 100644
--- a/Examenvoorbeeld_oefening_2.html
+++ b/Examenvoorbeeld_oefening_2.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -728,24 +740,24 @@
Hypotheses testen
## Fit: lm(formula = intensity %>% log2 ~ location * tissue + patient,
## data = hart)
##
-## Quantile = 3.0268
+## Quantile = 3.0293
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
-## tissueV == 0 7.9680 6.5961 9.3398
-## tissueV + locationR:tissueV == 0 5.5038 4.1320 6.8757
-## locationR:tissueV == 0 -2.4641 -4.4042 -0.5240
+## tissueV == 0 7.9680 6.5950 9.3409
+## tissueV + locationR:tissueV == 0 5.5038 4.1308 6.8768
+## locationR:tissueV == 0 -2.4641 -4.4058 -0.5224
## Estimate lwr upr
-## tissueV 250.3757829 96.94415082 646.6406911
-## tissueV + locationR:tissueV 45.3753769 17.56910085 117.1901081
-## locationR:tissueV 0.1812291 0.04736657 0.6934001
+## tissueV 250.3757829 96.89264124 646.9844549
+## tissueV + locationR:tissueV 45.3753769 17.55976581 117.2524081
+## locationR:tissueV 0.1812291 0.04733098 0.6939215
## attr(,"conf.level")
## [1] 0.95
## attr(,"calpha")
-## [1] 3.020156
+## [1] 3.021848
Conclusie:
@@ -754,10 +766,10 @@
Conclusie:
0.001).
Het geometrisch gemiddelde van de expressie van Myosin3L is een
factor 250 hoger in het linkerventrikel dan in het linkeratrium (p <
-0.001, 95% BI [97.1, 645.8]).
+0.001, 95% BI [96.8, 647.4]).
Het geometrisch gemiddelde van de expressie van Myosin3L is een
factor 45 hoger in het rechterventrikel dan in het rechteratrium (p <
-0.001, 95% BI [17.6, 117.3]).
+0.001, 95% BI [17.5, 117.4]).
De opregulatie van Myosin3L in ventrikel vs het atrium is gemiddeld
een factor 6 hoger in de linker- dan in de rechterzijde (p = 0.019, 95%
BI [1.4, 21.1] ).
diff --git a/Examenvoorbeeld_oefening_2.pdf b/Examenvoorbeeld_oefening_2.pdf
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index 0198891..8ad85df 100644
--- a/Examenvoorbeeld_oefening_2_opgave.html
+++ b/Examenvoorbeeld_oefening_2_opgave.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/Examenvoorbeeld_oefening_2_opgave.pdf b/Examenvoorbeeld_oefening_2_opgave.pdf
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diff --git a/Exp_design_fast_1_ex.html b/Exp_design_fast_1_ex.html
index b251c64..b8d34a1 100644
--- a/Exp_design_fast_1_ex.html
+++ b/Exp_design_fast_1_ex.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/Exp_design_fast_1_sol.html b/Exp_design_fast_1_sol.html
index a5d973e..e0f61e2 100644
--- a/Exp_design_fast_1_sol.html
+++ b/Exp_design_fast_1_sol.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -702,9 +714,9 @@
Simulation
alpha <- 0.05
power <- simFast(form, rodents, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
power
-## [1] 0.2213
+## [1] 0.2248
We observe that the experiment is severly underpowered. We only have
-a power of 22.1% to pick up the treatment effect.
+a power of 22.5% to pick up the treatment effect.
@@ -721,7 +733,7 @@
Power for a balanced
predictorData <- data.frame(group = rep(c("ctrl","treat"),c(n1,n2)) %>% as.factor)
powerBalanced <- simFast(form, predictorData, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
powerBalanced
-## [1] 0.2406
+## [1] 0.2444
We observe that the power is larger for the balanced design. We could
also have known this from formula of the standard error from the
two-sample t-test.
diff --git a/Exp_design_fast_1_sol.pdf b/Exp_design_fast_1_sol.pdf
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diff --git a/Exp_design_fast_2_ex.html b/Exp_design_fast_2_ex.html
index fd98f65..f8044d1 100644
--- a/Exp_design_fast_2_ex.html
+++ b/Exp_design_fast_2_ex.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/Exp_design_fast_2_sol.html b/Exp_design_fast_2_sol.html
index d092894..3ec93e2 100644
--- a/Exp_design_fast_2_sol.html
+++ b/Exp_design_fast_2_sol.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -631,10 +643,10 @@ Power for
power <- simFast(form, Puromycin, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
power
-[1] 0.4167
+[1] 0.4189
The power to pick up a slope of \(\beta_1=10\) for this experiment is only
-41.7%.
+41.9%.
Optimal design with 12
powerOpt <- simFast(form, simData, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
powerOpt
-[1] 0.6018
+[1] 0.609
Note that the power for a design where we repeat each concentration 1
time and the minimum and maximum concentration 4 times is considerably
higher than that for the designs where we repeat all data points.
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--- a/Exp_design_fast_3_ex.html
+++ b/Exp_design_fast_3_ex.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/Exp_design_fast_3_sol.html b/Exp_design_fast_3_sol.html
index 64fa691..ab729bc 100644
--- a/Exp_design_fast_3_sol.html
+++ b/Exp_design_fast_3_sol.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -706,11 +718,11 @@ Import KPNA2 data in
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
-## grade3 == 0 1.6675 0.1827 9.127 < 0.001 ***
-## grade3 + grade3:node1 == 0 0.8928 0.1827 4.886 < 0.001 ***
-## node1 == 0 0.6577 0.1827 3.600 0.00704 **
-## node1 + grade3:node1 == 0 -0.1170 0.1827 -0.640 0.89814
-## grade3:node1 == 0 -0.7748 0.2584 -2.998 0.02654 *
+## grade3 == 0 1.6675 0.1827 9.127 <0.001 ***
+## grade3 + grade3:node1 == 0 0.8928 0.1827 4.886 <0.001 ***
+## node1 == 0 0.6577 0.1827 3.600 0.0071 **
+## node1 + grade3:node1 == 0 -0.1170 0.1827 -0.640 0.8981
+## grade3:node1 == 0 -0.7748 0.2584 -2.998 0.0268 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -769,7 +781,7 @@ power of current
power1 <- simFastMultipleContrasts(form, kpna2, betas, sd, contrasts, nSim = nSim)
power1
## graden0 graden1 nodeg1 nodeg3 interaction
-## 1.00000 0.96925 0.75830 0.02585 0.56685
+## 1.00000 0.97290 0.76090 0.02525 0.57180
We observe large powers for all contrasts, except for contrast
nodeg3, which has a small effect size.
@@ -805,15 +817,15 @@ Power for increasing
}
powers
## n graden0 graden1 nodeg1 nodeg3 interaction
-## [1,] 2 0.70550 0.22045 0.11165 0.01285 0.07505
-## [2,] 3 0.99210 0.55885 0.29940 0.01480 0.19520
-## [3,] 4 0.99985 0.79860 0.48735 0.01945 0.32390
-## [4,] 5 1.00000 0.91905 0.64665 0.01990 0.44645
-## [5,] 6 1.00000 0.97155 0.76350 0.02295 0.56855
-## [6,] 7 1.00000 0.99030 0.84685 0.02650 0.66150
-## [7,] 8 1.00000 0.99675 0.90680 0.03250 0.74885
-## [8,] 9 1.00000 0.99920 0.94710 0.03445 0.82140
-## [9,] 10 1.00000 0.99970 0.96930 0.03940 0.87100
+## [1,] 2 0.70185 0.21485 0.11215 0.01275 0.07905
+## [2,] 3 0.99225 0.57085 0.29390 0.01785 0.19150
+## [3,] 4 0.99990 0.79840 0.48920 0.01825 0.32165
+## [4,] 5 1.00000 0.92140 0.64380 0.02170 0.44810
+## [5,] 6 1.00000 0.97050 0.76700 0.02550 0.57060
+## [6,] 7 1.00000 0.99075 0.84970 0.02690 0.67360
+## [7,] 8 1.00000 0.99610 0.91025 0.03105 0.75620
+## [8,] 9 1.00000 0.99900 0.94605 0.03335 0.81880
+## [9,] 10 1.00000 0.99955 0.96645 0.03845 0.86365
powers %>%
as.data.frame %>%
gather(contrast, power, -n) %>%
@@ -843,7 +855,7 @@ Power when FC for
power3 <- simFastMultipleContrasts(form, kpna2, betas2, sd, contrasts,nSim = nSim)
power3
## graden0 graden1 nodeg1 nodeg3 interaction
-## 0.63640 0.05235 0.76450 0.02545 0.56405
+## 0.63785 0.05480 0.76230 0.02490 0.57180
It is clear that only the power of the contrasts containing \(\beta_g\) change when the effect size of
\(\beta_g\) changes.
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
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+ Practicum 1: Oplossing Voorbereiding 2
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+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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+ Practicum 1: Oplossing Voorbereiding 2
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+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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+ Practicum 1: Oplossing Voorbereiding 2
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+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
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+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
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+++ b/Practicum_1_oplossing_Two_sample_t_test.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
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--- a/Practicum_1_oplossing_anova.html
+++ b/Practicum_1_oplossing_anova.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -795,8 +807,8 @@ Post-hoc analyse
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## linseed - casein == 0 -104.83 23.28 -4.504 < 0.001 ***
-## soybean - casein == 0 -77.15 22.43 -3.440 0.00436 **
-## soybean - linseed == 0 27.68 22.43 1.234 0.44142
+## soybean - casein == 0 -77.15 22.43 -3.440 0.00428 **
+## soybean - linseed == 0 27.68 22.43 1.234 0.44152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -830,15 +842,15 @@ Post-hoc analyse
##
## Fit: lm(formula = weight ~ feed, data = chickwts)
##
-## Quantile = 2.4475
+## Quantile = 2.4463
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
-## linseed - casein == 0 -104.8333 -161.8015 -47.8651
-## soybean - casein == 0 -77.1548 -132.0507 -22.2588
-## soybean - linseed == 0 27.6786 -27.2174 82.5745
+## linseed - casein == 0 -104.8333 -161.7725 -47.8941
+## soybean - casein == 0 -77.1548 -132.0228 -22.2868
+## soybean - linseed == 0 27.6786 -27.1894 82.5466
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
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+ Practicum 1: Oplossing Voorbereiding 1
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+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
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+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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+
+ Practicum 1: Oplossing Voorbereiding 2
+
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+ Practicum 1: Oplossing Voorbereiding 3
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
+
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
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+++ b/about.html
@@ -302,6 +302,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
+
diff --git a/afspraken_ufora.html b/afspraken_ufora.html
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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+ Practicum 1: Oplossing Voorbereiding 2
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+
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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+ Practicum 1: Oplossing Voorbereiding 3
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--- a/fishtank.html
+++ b/fishtank.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
diff --git a/fishtank_sol.html b/fishtank_sol.html
index 713a5f3..fdb00fd 100644
--- a/fishtank_sol.html
+++ b/fishtank_sol.html
@@ -480,6 +480,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -693,13 +705,13 @@ Post-hoc
confDiet <- confint(multComp)
confDiet$confint
## Estimate lwr upr
-## 2 - 1 20.33333 12.79730 27.86936
-## 3 - 1 44.00000 36.46397 51.53603
-## 3 - 2 23.66667 16.13064 31.20270
+## 2 - 1 20.33333 12.80467 27.86199
+## 3 - 1 44.00000 36.47134 51.52866
+## 3 - 2 23.66667 16.13801 31.19533
## attr(,"conf.level")
## [1] 0.95
## attr(,"calpha")
-## [1] 2.793181
+## [1] 2.790448
Conclusion
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@@ -302,6 +302,18 @@
Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
+
+
+ Practicum 2: Oefening lineair model
+
@@ -391,6 +403,18 @@
Werkcolleges
href="./Practicum_1_Voorbereiding_2.html">html
Voorbereiding 3: html
+
Oplossing Voorbereiding 1: html
+
Oplossing Voorbereiding 2: html
+
Oplossing Voorbereiding 3: html
+
+
Practicum 2:
+
+- Oefening 1: Simpele lineaire regressie: html
+- Dodona oefeningen
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
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+
+ Practicum 1: Oplossing Voorbereiding 2
+
+
+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
+
+ Practicum 1: Oplossing Voorbereiding 1
+
+
+ Practicum 1: Oplossing Voorbereiding 2
+
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+ Practicum 1: Oplossing Voorbereiding 3
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+
+ Practicum 2: Oefening lineair model
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Practicum 1: Voorbereiding 3
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+ Practicum 1: Oplossing Voorbereiding 3
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+ Practicum 2: Oefening lineair model
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+ Practicum 1: Oplossing Voorbereiding 1
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+ Practicum 1: Oplossing Voorbereiding 3
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