Fix various typos

10_chapter/basicCRN.jl

@@ -19,7 +19,7 @@ estMCRN = estM.(lamGrid,seed)
 plot(lamGrid,trueM,
 	c=:black, label="Expected curve")
 plot!(lamGrid,estM0,
-	c=:blue, label="No CRN estiamte")
+	c=:blue, label="No CRN estimate")
 plot!(lamGrid,estMCRN,
 	c=:red, label="CRN estimate", 
-	xlims=(0,1), ylims=(0,0.4), xlabel=L"\lambda", ylabel = "Mean")
+	xlims=(0,1), ylims=(0,0.4), xlabel=L"\lambda", ylabel = "Mean")

10_chapter/temperatureNoise.jl

@@ -32,7 +32,7 @@ smallTraj, smallHat, smallY = luenbergerTrack(kKalman, Tsmall, spikeTime)
 p1 = scatter(smallTraj, c=:blue, 
         ms=3, msw=0, label="System trajectory")
 p1 = scatter!(smallY, c = :black,
-        ms=3, msw=0, label="Measurments")
+        ms=3, msw=0, label="Measurements")
 p1 = scatter!(smallHat,c=:red, 
         ms=3, msw=0, label="Kalman filter tracking",
         xlabel = "Time", ylabel = "Temperature", 
@@ -53,4 +53,4 @@ p2 = scatter(kRange,errs, c=:black, ms=3, msw=0,
 p2 = plot!(kRange,analyticErr.(kRange), c = :red,
     xlabel="k", ylabel="MSE", label = "Analytic", ylim =(0.58,0.64))
 
-plot(p1, p2, size=(1000,400), margin = 5mm)
+plot(p1, p2, size=(1000,400), margin = 5mm)

6_chapter/proportionCI.jl

@@ -24,4 +24,4 @@ println("\nOut of $n non-missing observations, "*
 println("Hence a point estimate for the proportion "*
         "of grades at level $gradeInQuestion is $phat.")
 println("A $confidencePercent% confidence interval for "*
-        "the proprotion of level $gradeInQuestion is:\n$confInt.")
+        "the proportion of level $gradeInQuestion is:\n$confInt.")

7_chapter/infMeanDiffVarKnown.jl

@@ -12,5 +12,5 @@ pVal = 2*ccdf(Normal(), abs(testStatistic))
 
 println("Sample mean machine 1: ",xBar1)
 println("Sample mean machine 2: ",xBar2)
-println("Manually calculated test statistc: ", testStatistic)
+println("Manually calculated test statistic: ", testStatistic)
 println("Manually calculated p-value: ", pVal)

7_chapter/signTest.jl

@@ -17,9 +17,9 @@ pValB = 2*ccdf(binom, testStatisticB)
 println("Binomial mean: ", mean(binom))
 
 println("Case A: mu0: ", mu0A)
-println("\tTest statistc: ", testStatisticA)
+println("\tTest statistic: ", testStatisticA)
 println("\tP-value: ", pValA)
 
 println("Case B: mu0: ", mu0B)
-println("\tTest statistc: ", testStatisticB)
+println("\tTest statistic: ", testStatisticB)
 println("\tP-value: ", pValB)

8_chapter/linkFunctions.jl

@@ -17,12 +17,12 @@ model1 = glm(form, train, Normal(),  IdentityLink())
 model2 = glm(form, train, Poisson(), LogLink())
 model3 = glm(form, train, Gamma(),  InverseLink())
 
-invIdenityLink(x) = x
+invIdentityLink(x) = x
 invLogLink(x) = exp(x)
 invInverseLink(x) = 1/x
 
 A = [ones(numTest) test.CycT test.MMin test.MMax test.Cach test.ChMin test.ChMax]
-pred1 = invIdenityLink.(A*coef(model1))
+pred1 = invIdentityLink.(A*coef(model1))
 pred2 = invLogLink.(A*coef(model2))
 pred3 = invInverseLink.(A*coef(model3))
 
@@ -34,4 +34,4 @@ lossModel3 = norm(pred3 - actual)
 println("Model 1: ", coef(model1))
 println("Model 2: ", coef(model2))
 println("Model 3: ", coef(model3))
-println("\nLoss of models 1,2,3: ",(lossModel1 ,lossModel2, lossModel3))
+println("\nLoss of models 1,2,3: ",(lossModel1 ,lossModel2, lossModel3))

9_chapter/logisticRegressionMNIST.jl

@@ -43,8 +43,8 @@ while true
 end
 
 println("\nNumber of epochs: ", epochNum)
-acccuracy = mean([logisticMclassifier(testData[:,k]) for k in 1:nTest]
+accuracy = mean([logisticMclassifier(testData[:,k]) for k in 1:nTest]
                 .== testLabels)
-println("Accuracy: ", acccuracy)
+println("Accuracy: ", accuracy)
 
 plot(lossArray, xlabel = "Epoch", ylabel = "Cross Entropy Loss", legend = false)