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Business Business Analytics & Intelligence Minitab

Predictive Modeling, Regression and Statistics using Minitab

Enhance the skills of predictive modeling across a number of business sectors and domains using Minitab
Rating: 2.7 out of 52.7 (43 ratings)
7,336 students
Created by Smart Coders Hub
Last updated 2/2019
English
English [Auto]
30-Day Money-Back Guarantee

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

5 sections • 111 lectures • 15h 42m total length

  • Preview09:40
  • Preview10:51
  • Preview09:57
  • Preview11:00
  • 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

  • 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

  • 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

  • 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

  • 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

Instructor

Smart Coders Hub
Learn Data Science from the Expert
Smart Coders Hub
  • 3.4 Instructor Rating
  • 1,036 Reviews
  • 44,654 Students
  • 11 Courses

I am a passionate coder and a data scientist. I love teaching. I love sharing knowledge with students and enthusiastic learners. I believe any one can code. If you are passionate about learning, you will learn it. I keep learning from my work and keep teaching students. I love travelling. Its always a great feeling to see your students successful. I have lived those moments. I am obsessed with data and passionate about teaching and sharing those knowledge. Come and join me in this journey.

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