Modeling R Minitab SPSS SAS

Course content

Sections: 9

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Activities: 0

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Resources: 361

1 Overview of R Programming
2 Downloading and Installing R Studio
3 How to use R Studio
4 How to use R Studio Continues
5 R Studio Basics
6 Basic Data Type R
7 Vectors
8 More on Vector
9 Matrix
10 Matrix Continues
11 What is List
12 What is List Continues
13 Data Frame in R
14 Data Frame in R Sub Clip
15 Decision Making
16 Conditional Statements
17 Loops in R
18 Implementing Loop with Practical Examples
19 While Loop
20 Break Statement
21 Functions
22 Alternative Loops
23 Alternative Loops Continue
24 User Define Function
25 Power of GGPLOT
26 GGPLOT 2 Visuals
27 Use of Function

1 Introduction and Importing Dataset
2 IV Calculation
3 Plotting Variables
4 Splitting
5 Building Logistic Model
6 Making Optimal Model
7 Making Lift Chart for Training Set
8 Checking Model Performance
9 Model Performance in Test Set
10 Saving Model in R
11 Fitting Decision Tree Model
12 Fitting Decision Tree Model Continue
13 Prediction of Decision Tree and Model Performance

1 Overview and History of R
2 Datatypes and Basic Operations Part1_1 part 01
3 Datatypes and Basic Operations Part1_1 part 02
4 Datatypes and Basic Operations Part1_2 Part 01
5 Datatypes and Basic Operations Part1_2 Part 02
6 Datatypes and Basic Operations Part1_2 Part 03_part01
7 Datatypes and Basic Operations Part1_2 Part 03_part 02 summary
8 Datatypes and Basic Operations Part2_1
9 Datatypes and Basic Operations Part2_2
10 ReadingData 1
11 ReadingData 2
12 ReadingData 3
13 ReadingData 4a
14 ReadingData 4b
15 Debugging 1
16 ControlStructures
17 Functions Part 01
18 Functions Part 02
19 ScopingRules1 Part 01
20 ScopingRules1 Part 02
21 ScopingRules2
22 Looping1
23 Looping2
24 Looping3
25 Simulation_part 1
26 Simulation_part 2
27 Plotting1
28 Plotting2
29 Plotting3_part 1
30 Plotting3_part 2
31 Plotting4
32 Plotting5
33 Plotting Colors 1
34 Plotting Colors 2
35 Date and TimePart1and 5Date and TimePart2
36 Date andTimePart3
37 RegEx1
38 RegEx2
39 RegEx3_part 1
40 RegEx3_part 2
41 Classes and Methods1_part 1
42 Classes and Methods1_part 2
43 Classes and Methods2_part 1
44 Classes and Methods2_part 2
45 Debugging Part2

1 Introduction to Minitab
2 Types of Data
3 Measure of Dispersion
4 Descriptive Stats
5 Data Sorting
6 Histograms
7 Pie Charts
8 Bar Charts
9 Line Graphs
10 Scatter plots
11 Box Plot
12 Discrete Random Variable
13 Binomial Distribution
14 Normal Distribution
15 Normality Test
16 Data Transformation
17 Sampling and Sample Size
18 Sample Size for Estimation
19 Parameter Estimation
20 Power Analysis
21 Measurement System Analysis
22 MSA Gage R and R
23 MSA Attribute Agreement Analysis
24 Process Capability Analysis
25 Hypothesis Testing
26 Hypothesis Testing Mean
27 Paired T Test
28 Anova
29 Pareto Analysis
30 Correlation
31 Regression
32 Regression Continue
33 Control Charts
34 P Chart

110 Implementation Using Correlation
103 Scatterplot
102 Situations Income
1 Introduction of Predictive Modeling
2 Non Linear Regression
3 Anova and Control Charts
4 Understanding Interpretation and implementation using Minitab
5 Continue on Interpretation and implementation using Minitab
6 Observation
7 Results for NAV Prices
8 NAV Prices Observations
9 Descriptive Statistics
109 Implementation of T Test
10 Customer Complaints Observations
11 Resting Heart Rate Observations
107 Descriptive statistics Input Range
12 Results for Loan Applicant MTW
13 More Details on Results for Loan Applicant MTW
14 Features of T Test
15 Loan Applicant
19 Features of Chi Test
16 Paired T Test
17 Understanding and Implementation of ANOVA
18 Pairwise Comparisons
20 Preference and Pulse Rate
21 Diffe btw Growth Plan ad Dividend Plan in MF
22 Checking NAV Price and Repurchase Price
23 Basic Correlation Techniques
24 More on Basic Correlation Techniques
25 CT Implementation Using Minitab
26 Continue on Implemetation using Minitab
27 Interpretation of Correlation Values
28 Results for Return
29 Correlation Values Observations
30 Correlation Values Interpretations
31 Heart Beat Objective
32 Heart Beat Interpretation
33 Demographics and Living Standards
34 Demographics and Living Standards Observation
35 Graphical Implementation
36 Add Regression Fit
37 Scatterplot with Regression
38 Scatterplot of Rhdeq vs Rhcap
39 Introduction to Regression Modeling
40 Identify Independent Variable
41 Regression Equation
42 Tabulating the Values
43 Interpretation and Implementation on Data Sets
44 Continue on Interpretation on Database
45 Significant Variable
46 Calculating Corresponding Values
47 Identify Dependent Variable
48 Generate Descriptive Statistics
49 Scatterplot of Energy Consumption
50 Identity Equation
51 P Value and T Value
52 Changes in Tem and Expansion
53 Objective of Stock Prices
54 Interpretations of Example 5
55 Reliance Return Change
56 Generate Predicted Values
57 Scatterplot Return RIL
58 Basic Multiple Regression
59 Basic Multiple Regression Continues
60 Basic Multiple Regression Interpretation
61 Generate Basic Statistics
62 Working on Scatterplot
63 Dependent Variable Objective
64 Concept of Multicollinearity
65 Identify Dependent Variable Y
66 Outputs and Observation
67 Interpretations Example 3
68 Calculate with and without Flux
69 Scatterplot of Heart FLux Vs Insolation
70 Interpretation of Datasets
71 Implementation of Datasets
72 Example 4 Observations
73 Display Descriptive Statistics
74 Predicted Values Example 4
75 Scatterplot of Example 4
76 Calculating IV Multiple Regression
77 Calculating Independent Multiple Regression
78 Understanding Basic Logistic Scatter Plot
79 Basic Logistic Scatter Plot Continues
80 Generation of Regression Equation
81 Tabulated Values
82 Interpretation and Implementation on Dataset
83 Interpretation and Implementation on dataset Continues
84 Output and Observation Tabulated Values
85 Business Metrics Example
86 Example Two and Three Interpretations
87 Regression Equation Group
88 Interpretation and Implementation of Scatter Plot
89 More on Implementation of Scatter Plot
90 Plastic Case Strength
91 Separate Equations
92 Generation of Predicted Values
93 Scatter Plot Strength Vs Temp
94 Data of Cereal Purchase
95 Children Viewed and RE
96 Predicted Values for Individual Customers
97 Income Independent Variable
98 Example of Credit Card Issuing
99 Example Five Tabulated Values
100 Generating Outputs
108 Implementation of ANOVA
101 Example Five Interpretations
104 Scatter Plot Scale
105 Using Data Analysis Toolpak
106 Implementation of Descriptive Statistics
111 Implementation Using Regression

1 Implementation using SPSS
2 Implementation using SPSS Continues
3 Importing Datasets in Text and CSV
4 Other Concepts of Understanding Mean SD
5 Software Menus
6 Understanding Mean Standard Deviation
7 Understanding User Operating Concepts

10 Input Variable
11 Input Variable Continues
12 Values of R Square
13 More on Variable Selection
14 Binary Target Variable
15 Variable and Effect Summary
16 Variable Selection Variable IDs
17 Variable Frequency Table
18 Variable S Updating Model Comparison
19 Run Data Partition Node
1 Introduction of SAS Enterprise Miner
20 Variable Selection Fit Statistics
21 Understanding Transformation of Variables
22 Score Ranking Overlay Res
23 Update Transformation of Variables
24 Combination of Different Models
25 Properties of Neural Network
26 Analyzing the Output Variable
27 Combination of Regression Model
28 Combination Result of Regression Node
29 Combination Iteration Plot
2 Select a SAS Table
30 Subseries Plot
31 Creating Densemble Diagram
32 SAS Code
33 Decision Tree Model
34 Run and Upadate Decision Tree Model
35 Creating Dscore Node
36 DT Resulf of Model Comparison
37 Leaf Statistics and Tree Map
38 Interactively Decision Trees
39 Result Node Data Partition
3 Creating Input Data Node
40 Interactively Trees Window
41 Building a Decision Trees
42 Neural Network Model
43 Neural Network Model Output
44 Model Weight History
45 Neural Network Final Weight
46 ROC Chart
47 Neural Network Iteration Plot
48 Neural Network SAS Code
49 Neural Network Cumulative Lift
4 Metadata Advisor Options
50 Decision Processing
51 Results of Auto Neural Node
52 Run Model Comparison
53 DEX Variable IDs
54 Average Square Error
55 Score Rating overlay Event
56 Run Dmine Regression Node
57 Regression with Binary Target
58 Regression Table Effect Plots
59 Result of Regression Model
5 Add More Data Sources
60 Update Regression Node
61 Creating Flow Diagram
6 Sample Statistics
7 Trial report
8 Properties of Cluster Node
9 Variable Selection

10 What is Time Series
11 Smoothing Methods Moving Averages
12 Smoothing Methods Double Exponential Smoothing
13 Regression Algorithms Exponential
14 Clustering Algorithms Definition
15 Clustering Algorithms Fuzzy C Means Clustering
16 Neural Network Algorithm
17 Support Vector Machines
1 What is Predictive Modelling
2 Predictive Modelling
3 How to Build A Predicative Model
4 Types of Variables
5 Difference Between Variables
6 Other Types Extraneous Variables
7 How to Build A Predicative Model Steps
8 Algorithms
9 Forecasting Methods

10 Example of Interpretations
11 Volatility Graphs
12 Generating returns Interpretation and Graphs
13 Generating returns Interpretation Continues
14 Basic Correlation Theory
15 Generating Correlation Matrix in Eviews
16 Generating Correlation Matrix in Eviews Continues
17 Mutual Funds Correlation Matrix Percentage
18 Scatter Plots Using Eviews
19 Generating Correlation Matrix
1 Introduction to Eview Training
20 Scatter Plots and Volatility Graphs
21 Generating Correlation Matrix and Interpretations
22 Generating Correlation Interpretations
23 Generating Correlation Interpretations Continues
24 Scatter Plots
25 Working on Scatter Plots
26 Basic Regression Modelling Theory
27 Generating Returns and Estimation Output
28 More on Generating Returns
29 Understanding Estimation Output
2 Eviews GUI
30 Understanding Estimation Output Continues
31 Example of Interpretations
32 Generating Estimation Output
33 Interpretations and Volatility Scatter Plots
34 More on Volatility Scatter Plots
35 Estimation Output Interpretations and Graphs
36 Estimation Output Interpretations and Graphs Continues
37 Example 3 NAV Price Study
38 Working on Volatility Graphs
39 Correlation Matrix
3 Eviews GUI Continues
40 Correlation Matrix Continues
41 Example 4 Estimation Output
42 Basic Regression Modelling
43 Basic Regression Modelling Continues
44 Interpretations and Scatterplot Analysis
45 More on Scatterplot Analysis
46 Equation Estimation
4 Generating Log Returns
5 Example of Descriptive
6 Interpretation and Graphs
7 Interpretation and Graphs Continues
8 Generating Log Returns and Descriptive
9 Generating Log Returns and Descriptive Continue

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