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A/B TEST 1 A_B Test 2 EXCEL 1 SQL 4 Tableau 5 CS301Proj 7 statistic knowlege review 1 An Introduction to statistical Learning 18 Take home data challenge 6
ISLR_ch3.1 Simple Linear Regression ISLR_ch3.1 Simple Linear Regression
ISLR_ch3.1 Simple Linear RegressionSimple linear regression: approach for predicting a quantitative response Y on the ba
2019-09-23 An Introduction to statistical Learning
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ISLR_ch9.1 Maximal_Margin_Classifier ISLR_ch9.1 Maximal_Margin_Classifier
ISLR_ch9.1 Maximal_Margin_ClassifierWhat Is a Hyperplane?In a p-dimensional space, a hyperplane is a flat affine subspac
2019-09-23 An Introduction to statistical Learning
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ISLR_ch8.2 Bagging,Random_Forest,Boosting ISLR_ch8.2 Bagging,Random_Forest,Boosting
ISLR_ch8.2 Bagging,Random_Forest,BoostingBaggingBootstrap aggregation, or bagging, is a general-purpose procedure for re
2019-09-23 An Introduction to statistical Learning
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ISLR_ch8.1 The_Basics_of_Decision_Trees ISLR_ch8.1 The_Basics_of_Decision_Trees
ISLR_ch8.1 The_Basics_of_Decision_TreesRegression TreesPredicting Baseball Players’ Salaries Using Regression Trees Te
2019-09-23 An Introduction to statistical Learning
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ISLR_ch6.3 Dimension_Reduction-PCA ISLR_ch6.3 Dimension_Reduction-PCA
ISLR_ch6.3 Dimension_Reduction-PCAIntro to Dimension Reduction MethodsSubset selection and shrinkage methods all use the
2019-09-23 An Introduction to statistical Learning
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ISLR_ch6.4 Considerations_In_High_Dimensions ISLR_ch6.4 Considerations_In_High_Dimensions
ISLR_ch6.4 Considerations_In_High_DimensionsHigh-Dimensional DataHigh-dimensional: Data sets containing more features th
2019-09-23 An Introduction to statistical Learning
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ISLR_ch6.2 Shrinkage Methods ISLR_ch6.2 Shrinkage Methods
ISLR_ch6.2 Shrinkage MethodsShrinkage Methods v.s. Subset Selection: Subset selection methods described involve using l
2019-09-23 An Introduction to statistical Learning
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ISLR_ch5.2 Potential Problems ISLR_ch5.2 Potential Problems
ISLR_ch5.2 Potential ProblemsApproach: A data set, which we call Z, that contains n observations. We randomly select n
2019-09-23 An Introduction to statistical Learning
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ISLR_ch6.1 Subset_Selection ISLR_ch6.1 Subset_Selection
ISLR_ch6.1 Subset_SelectionIntro to model selectionSetting: In the regression setting, the standard linear model $Y = β
2019-09-23 An Introduction to statistical Learning
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ISLR_ch5.1 Cross_Validation ISLR_ch5.1 Cross_Validation
ISLR_ch5.1 Cross_ValidationResampling methods:involve repeatedly drawing samples from a training set and refitting a mod
2019-09-23 An Introduction to statistical Learning
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ISLR_ch6.0 Intro_Model_Selection ISLR_ch6.0 Intro_Model_Selection
ISLR_ch6.0 Intro_Model_SelectionSetting: In the regression setting, the standard linear model $Y = β_0 + β_1X_1 + · · ·
2019-09-23 An Introduction to statistical Learning
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ISLR_ch4.5 Comparison_of_Classification_Methods ISLR_ch4.5 Comparison_of_Classification_Methods
ISLR_ch4.5 Comparison_of_Classification_MethodsComparing Logistic Regression, LDA, QDA, and KNNlogistic regression and L
2019-09-23 An Introduction to statistical Learning
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ISLR_ch4.4 Linear_Discriminant_Analysis ISLR_ch4.4 Linear_Discriminant_Analysis
ISLR_ch4.4 Linear_Discriminant_AnalysisLDA V.S. Logistic Regression: When the classes are well-separated, the parameter
2019-09-23 An Introduction to statistical Learning
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ISLR_ch3.5 Comparison_of_Linear_Regression_with_K-Nearest_Neighbors ISLR_ch3.5 Comparison_of_Linear_Regression_with_K-Nearest_Neighbors
ISLR_ch3.5 Comparison_of_Linear_Regression_with_K-Nearest_NeighborsParametric v.s. Non-parametricLinear regression is an
2019-09-23 An Introduction to statistical Learning
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ISLR_ch4.3 Logistic_Regression ISLR_ch4.3 Logistic_Regression
ISLR_ch4.3 Logistic_RegressionWhy Not Linear Regression?Linear regression is not appropriate in the case of a qualitativ
2019-09-23 An Introduction to statistical Learning
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ISLR_ch3.2 Multiple Linear Regression ISLR_ch3.2 Multiple Linear Regression
ISLR_ch3.2 Multiple Linear Regressionmultiple linear regression model takes the form: \begin{align} Y=\beta_0+\beta_1X_1
2019-09-23 An Introduction to statistical Learning
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ISLR_ch2.2 Assessing Model Accuracy ISLR_ch2.2 Assessing Model Accuracy
ISLR_ch2.2 Assessing Model Accuracyno free lunch in statistics: no one method dominates all others over all possible dat
2019-09-23 An Introduction to statistical Learning
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ISLR_ch3.3 Potential Problems ISLR_ch3.3 Potential Problems
ISLR_ch3.3 Potential ProblemsQualitative PredictorsPredictors with Only Two LevelsSuppose that we wish to investigate di
2019-09-23 An Introduction to statistical Learning
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