Predictive Modeling, Regression and Statistics using Minitab
2.6 (37 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
7,325 students enrolled

Predictive Modeling, Regression and Statistics using Minitab

Enhance the skills of predictive modeling across a number of business sectors and domains using Minitab
2.6 (37 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
7,325 students enrolled
Last updated 2/2019
English
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Current price: $83.99 Original price: $119.99 Discount: 30% off
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This course includes
  • 15.5 hours on-demand video
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn Predictive Modeling
  • Through this course you will learn about introduction to regression modeling, identify independent variable, regression equation and tabulating the values.
  • Learn Analysis of Variance ANOVA in Minitab
  • Master Correlation Techniques
Requirements
  • Prior knowledge of Quantitative Methods
  • MS Office and Paint is desired.
  • In case the participant possesses an understanding of data analysis along with the VBA toolpack in MS Excel, it will be of good use in learning this course.
  • Minitab software in your PC
Description

The objective of the course is to provide skills from the basic to the advanced level for the implementation of the concepts of Predictive Modeling with the help of the Minitab software. Although, development of the concepts of Predictive Modeling is significant, the capability of implementing it by making proper use of the appropriate software packages is of equal significance. The course attempts to fill in the gap that exists between an understanding of the various concepts and their practical implementation. T-test, Standard Deviation, Means and Descriptive Statistics are all explained by this course. The concepts of descriptive statistics that are explained by this course act as the building blocks for the other related courses that follow this course.

Quantitative methods along with the concepts of predictive modeling will be made use of in an extensive way for an understanding of the current behavior of the customers, movements of the financial markets and for the purpose of studying the tests as well as the effects in the sectors of pharma and medicine after the administration of the drugs. For the purpose of predictive analysis, the practical and also the theoretical databases are covered by the course. As the participant goes through the training that this course provides, the observations and interpretations along with the predictions and conclusions are explained then and there and relevant examples are also provided. This course will be helpful in working across a number of software packages like Paint, PDF writers, MS Office and Minitab. Apart from this, the distribution of the course has been done through six sub courses

Who this course is for:
  • Students
  • Quantitative and Predictive Modelers and Professionals
  • CFA’s and Equity Research professionals
  • Pharms and research scientists
Course content
Expand all 111 lectures 15:42:35
+ Application to Predictive Modeling (Descriptive Statistics)
16 lectures 02:28:18
Continue on Interpretation and implementation using Minitab
10:40
Observation
11:37
Results for NAV Prices
06:36
NAV Prices - Observations
10:27
Descriptive Statistics
08:09
Customer Complaints-Observations
09:57
Resting Heart Rate Observations
08:30
Results for Loan Applicant MTW
09:30
More Details on Results for Loan Applicant MTW
08:48
Features of T- Test
09:33
Loan Applicant
06:16
Paired T - Test
06:47
+ Analysis of Variance ANOVA in Minitab
6 lectures 53:00
Understanding and Implementation of ANOVA
10:25
Pairwise Comparisons
07:55
Features of Chi - Test
11:19
Preference and Pulse Rate
09:57
Diffe. btw Growth Plan ad Dividend Plan in MF
07:06
Checking NAV Price and Repurchase Price
06:18
+ Correlation Techniques
16 lectures 01:48:13
Basic Correlation Techniques
08:33
More on Basic Correlation Techniques
05:50
CT Implementation Using Minitab
10:05
Continue on Implemetation using Minitab
03:19
Interpretation of Correlation Values
06:05
Results for Return
08:42
Correlation Values - Observations
08:11
Correlation Values - Interpretations
05:55
Heart Beat - Objective
05:53
Heart Beat - Interpretation
05:19
Demographics and Living Standards
06:07
Demographics and Living Standards - Observation
06:11
Graphical Implementation
09:02
Add Regression Fit
08:46
Scatterplot with Regression
05:39
Scatterplot of Rhdeq vs Rhcap
04:36
+ Regression Modeling
66 lectures 09:36:36
Introduction to Regression Modeling
08:47
Identify Independent Variable
08:32
Regression Equation
07:45
Tabulating the Values
06:11
Interpretation and Implementation on Data Sets
07:57
Continue on Interpretation on Database
08:31
Significant Variable
07:40
Calculating Corresponding Values
08:55
Identify Dependent Variable
09:03
Generate Descriptive Statistics
08:40
Scatterplot of Energy Consumption
06:33
Identity Equation
07:58
P - Value and T - Value
07:11
Changes in Tem. and Expansion
08:17
Objective of Stock Prices
09:19
Interpretations of Example 5
08:40
Reliance Return Change
08:26
Generate Predicted Values
07:36
Scatterplot Return RIL
07:21
Basic Multiple Regression
08:36
Basic Multiple Regression Continues
08:24
Basic Multiple Regression - Interpretation
08:36
Generate Basic Statistics
07:22
Working on Scatterplot
03:57
Dependent Variable Objective
11:30
Concept of Multicollinearity
09:20
Identify Dependent Variable Y
11:41
Outputs and Observation
11:57
Interpretations - Example 3
10:23
Calculate with and without Flux
07:09
Scatterplot of Heart FLux Vs Insolation
06:13
Interpretation of Datasets
12:06
Implementation of Datasets
07:22
Example 4 Observations
09:30
Display Descriptive Statistics
06:41
Predicted Values Example 4
09:55
Scatterplot of Example 4
05:22
Calculating IV - Multiple Regression
11:47
Calculating Independent Multiple Regression
04:20
Understanding Basic Logistic Scatter Plot
10:23
Basic Logistic Scatter Plot Continues
08:15
Generation of Regression Equation
11:29
Tabulated Values
07:20
Interpretation and Implementation on Dataset
10:31
Interpretation and Implementation on dataset Continues
07:48
Output and Observation - Tabulated Values
08:41
Business Metrics Example
06:46
Example Two and Three Interpretations
06:51
Regression Equation Group
07:44
Interpretation and Implementation of Scatter Plot
09:14
More on Implementation of Scatter Plot
05:51
Plastic Case Strength
11:01
Separate Equations
10:47
Generation of Predicted Values
10:30
Scatter Plot Strength Vs Temp
10:10
Data of Cereal Purchase
11:10
Children Viewed and RE
10:06
Predicted Values for Individual Customers
11:43
Income Independent Variable
09:22
Example of Credit Card Issuing
11:13
Example Five - Tabulated Values
08:59
Generating Outputs
08:31
Example Five Interpretations
11:17
Situations Income
09:34
Adding Predicted Values
07:16
Scatter Plot Scale
08:31
+ Predictive Modeling using Microsoft Excel
7 lectures 56:28
Using Data Analysis Toolpak
06:38
Implementation of Descriptive Statistics
08:14
Descriptive statistics - Input Range
07:13
Implementation of ANOVA
06:25
Implementation of T - Test
05:50
Implementation Using Correlation
10:17
Implementation Using Regression
11:51