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Predictive Modeling and Data Analysis with Minitab and Excel

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Predictive Modeling and Data Analysis with Minitab and Excel, available at $19.99, has an average rating of 4.58, with 111 lectures, based on 6 reviews, and has 4777 subscribers.

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You will learn about Introduction to predictive modeling using Minitab and Excel. Non-linear regression analysis and interpretation. Understanding ANOVA and control charts for data analysis. Implementation of regression models for predictive analytics. Exploring datasets and deriving descriptive statistics. Interpretation of correlation techniques and their practical applications. Hands-on experience with scatter plots, regression equations, and model fitting. Utilizing data analysis tools for hypothesis testing and predictive modeling in Excel. Application of T-tests, ANOVA, and regression analysis in real-world scenarios. Comprehensive understanding of variable clustering, subset selection, and regression modeling techniques. Generating predictive values and interpreting model outputs effectively. Practical insights into customer complaints analysis, financial modeling, and demographic studies. Enhancing decision-making skills through predictive analytics and data-driven insights. Advanced techniques in predictive modeling using Minitab, including non-linear regression and ANOVA. This course is ideal for individuals who are Data analysts seeking to enhance their skills in predictive modeling using Minitab and MS Excel. or Business professionals interested in leveraging advanced statistical techniques for data-driven decision-making. or Researchers looking to explore regression analysis and correlation techniques in their studies. or Students pursuing degrees in statistics, data science, business analytics, or related fields. or Professionals in industries such as finance, healthcare, marketing, and manufacturing requiring predictive modeling skills. or Anyone interested in gaining practical knowledge of regression analysis and its applications in real-world scenarios. or Individuals looking to enhance their proficiency in using statistical software like Minitab and MS Excel for predictive modeling. or Those aiming to improve their understanding of regression techniques, ANOVA, and hypothesis testing. or Business owners and managers seeking to utilize predictive modeling for forecasting and strategic planning. or Beginners and intermediate learners looking to transition into advanced topics in predictive analytics and statistical modeling. It is particularly useful for Data analysts seeking to enhance their skills in predictive modeling using Minitab and MS Excel. or Business professionals interested in leveraging advanced statistical techniques for data-driven decision-making. or Researchers looking to explore regression analysis and correlation techniques in their studies. or Students pursuing degrees in statistics, data science, business analytics, or related fields. or Professionals in industries such as finance, healthcare, marketing, and manufacturing requiring predictive modeling skills. or Anyone interested in gaining practical knowledge of regression analysis and its applications in real-world scenarios. or Individuals looking to enhance their proficiency in using statistical software like Minitab and MS Excel for predictive modeling. or Those aiming to improve their understanding of regression techniques, ANOVA, and hypothesis testing. or Business owners and managers seeking to utilize predictive modeling for forecasting and strategic planning. or Beginners and intermediate learners looking to transition into advanced topics in predictive analytics and statistical modeling.

Enroll now: Predictive Modeling and Data Analysis with Minitab and Excel

Summary

Title: Predictive Modeling and Data Analysis with Minitab and Excel

Price: $19.99

Average Rating: 4.58

Number of Lectures: 111

Number of Published Lectures: 111

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 111

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

  • Introduction to predictive modeling using Minitab and Excel.
  • Non-linear regression analysis and interpretation.
  • Understanding ANOVA and control charts for data analysis.
  • Implementation of regression models for predictive analytics.
  • Exploring datasets and deriving descriptive statistics.
  • Interpretation of correlation techniques and their practical applications.
  • Hands-on experience with scatter plots, regression equations, and model fitting.
  • Utilizing data analysis tools for hypothesis testing and predictive modeling in Excel.
  • Application of T-tests, ANOVA, and regression analysis in real-world scenarios.
  • Comprehensive understanding of variable clustering, subset selection, and regression modeling techniques.
  • Generating predictive values and interpreting model outputs effectively.
  • Practical insights into customer complaints analysis, financial modeling, and demographic studies.
  • Enhancing decision-making skills through predictive analytics and data-driven insights.
  • Advanced techniques in predictive modeling using Minitab, including non-linear regression and ANOVA.

Who Should Attend

  • Data analysts seeking to enhance their skills in predictive modeling using Minitab and MS Excel.
  • Business professionals interested in leveraging advanced statistical techniques for data-driven decision-making.
  • Researchers looking to explore regression analysis and correlation techniques in their studies.
  • Students pursuing degrees in statistics, data science, business analytics, or related fields.
  • Professionals in industries such as finance, healthcare, marketing, and manufacturing requiring predictive modeling skills.
  • Anyone interested in gaining practical knowledge of regression analysis and its applications in real-world scenarios.
  • Individuals looking to enhance their proficiency in using statistical software like Minitab and MS Excel for predictive modeling.
  • Those aiming to improve their understanding of regression techniques, ANOVA, and hypothesis testing.
  • Business owners and managers seeking to utilize predictive modeling for forecasting and strategic planning.
  • Beginners and intermediate learners looking to transition into advanced topics in predictive analytics and statistical modeling.

Target Audiences

  • Data analysts seeking to enhance their skills in predictive modeling using Minitab and MS Excel.
  • Business professionals interested in leveraging advanced statistical techniques for data-driven decision-making.
  • Researchers looking to explore regression analysis and correlation techniques in their studies.
  • Students pursuing degrees in statistics, data science, business analytics, or related fields.
  • Professionals in industries such as finance, healthcare, marketing, and manufacturing requiring predictive modeling skills.
  • Anyone interested in gaining practical knowledge of regression analysis and its applications in real-world scenarios.
  • Individuals looking to enhance their proficiency in using statistical software like Minitab and MS Excel for predictive modeling.
  • Those aiming to improve their understanding of regression techniques, ANOVA, and hypothesis testing.
  • Business owners and managers seeking to utilize predictive modeling for forecasting and strategic planning.
  • Beginners and intermediate learners looking to transition into advanced topics in predictive analytics and statistical modeling.

Welcome to the course on Predictive Modeling and Data Analysis using Minitab and Microsoft Excel! This comprehensive course is designed to equip you with the essential skills and knowledge required to leverage statistical techniques for predictive modeling and data analysis. Whether you’re a beginner or an experienced data analyst, this course will provide you with valuable insights and practical experience in applying predictive modeling methods to real-world datasets.

Throughout this course, you will learn how to use Minitab, a powerful statistical software, and Microsoft Excel, a widely-used tool, to perform various predictive modeling and data analysis tasks. From exploring datasets to fitting regression models and interpreting results, each section of this course is carefully crafted to provide you with a step-by-step guide to mastering predictive modeling techniques.

By the end of this course, you will have the skills and confidence to analyze data, build predictive models, and make informed decisions based on data-driven insights. Whether you’re interested in advancing your career in data analysis, improving business decision-making processes, or simply enhancing your analytical skills, this course is your gateway to unlocking the power of predictive modeling and data analysis. Let’s dive in and start exploring the fascinating world of predictive modeling together!

Section 1: Introduction

In this section, students will be introduced to the fundamentals of predictive modeling. The course begins with an overview of predictive modeling techniques and their applications in various industries. Students will gain an understanding of non-linear regression and how it can be used to model complex relationships in data. Additionally, they will learn about ANOVA (Analysis of Variance) and control charts, essential tools for analyzing variance and maintaining quality control in processes. Through practical demonstrations and hands-on exercises, students will learn how to interpret and implement predictive models using Minitab, a powerful statistical software.

Section 2: ANOVA Using Minitab

Section 2 delves deeper into the application of ANOVA techniques using Minitab. Students will explore the intricacies of ANOVA, including pairwise comparisons and chi-square tests, to analyze differences between multiple groups in datasets. Through real-world examples such as analyzing preference and pulse rate data, students will understand how ANOVA can be applied to different scenarios. Additionally, they will learn to compare growth and dividend plans in mutual funds using ANOVA techniques and examine NAV and repurchase prices to gain insights into financial data.

Section 3: Correlation Techniques

This section focuses on correlation techniques, which are essential for understanding relationships between variables in a dataset. Students will learn basic and advanced correlation methods and how to implement them using Minitab. Through hands-on exercises, they will interpret correlation results for various datasets, including return rates and heart rate data. Furthermore, students will analyze demographics and living standards data to understand the correlation between different socio-economic factors. Graphical implementations of correlation techniques will also be explored to visualize relationships between variables effectively.

Section 4: Regression Modeling

Section 4 covers regression modeling, a powerful statistical technique for analyzing relationships between variables and making predictions. Students will be introduced to regression modeling concepts and learn to identify independent and dependent variables in a dataset. They will develop regression equations and interpret the results for datasets such as energy consumption and stock prices. The section also covers multiple regression analysis, addressing multicollinearity issues, and introduces logistic regression modeling for predictive analysis of categorical outcomes.

Section 5: Predictive Modeling using MS Excel

The final section focuses on predictive modeling using Microsoft Excel, a widely-used tool for data analysis. Students will learn how to utilize Excel’s Data Analysis Toolpak to perform descriptive statistics, ANOVA, t-tests, correlation, and regression analysis. Through practical examples and step-by-step demonstrations, students will gain proficiency in applying predictive modeling techniques using Excel’s intuitive interface. This section serves as a practical guide for professionals who prefer using Excel for data analysis and predictive modeling tasks.

Course Curriculum

Chapter 1: Minitab and its applications to Predictive Modelling

Lecture 1: Introduction of Predictive Modeling

Lecture 2: Non Linear Regression

Lecture 3: Anova and Control Charts

Lecture 4: Understanding, Interpretation and implementation using Minitab

Lecture 5: Continue on Interpretation and implementation using Minitab

Lecture 6: Observation

Lecture 7: Results for NAV Prices

Lecture 8: NAV Prices – Observations

Lecture 9: Descriptive Statistics

Lecture 10: Customer Complaints-Observations

Lecture 11: Resting Heart Rate Observations

Lecture 12: Results for Loan Applicant MTW

Lecture 13: More Details on Results for Loan Applicant MTW

Lecture 14: Features of T- Test

Lecture 15: Loan Applicant

Lecture 16: Paired T – Test

Chapter 2: ANOVA Using Minitab

Lecture 1: Understanding and Implementation of ANOVA

Lecture 2: Pairwise Comparisons

Lecture 3: Features of Chi – Test

Lecture 4: Preference and Pulse Rate

Lecture 5: Diffe. btw Growth Plan ad Dividend Plan in MF

Lecture 6: Checking NAV Price and Repurchase Price

Chapter 3: Correlation Techniques

Lecture 1: Basic Correlation Techniques

Lecture 2: More on Basic Correlation Techniques

Lecture 3: CT Implementation Using Minitab

Lecture 4: Continue on Implemetation using Minitab

Lecture 5: Interpretation of Correlation Values

Lecture 6: Results for Return

Lecture 7: Correlation Values – Observations

Lecture 8: Correlation Values – Interpretations

Lecture 9: Heart Beat – Objective

Lecture 10: Heart Beat – Interpretation

Lecture 11: Demographics and Living Standards

Lecture 12: Demographics and Living Standards – Observation

Lecture 13: Graphical Implementation

Lecture 14: Add Regression Fit

Lecture 15: Scatterplot with Regression

Lecture 16: Scatterplot of Rhdeq vs Rhcap

Chapter 4: Regression Modeling

Lecture 1: Introduction to Regression Modeling

Lecture 2: Identify Independent Variable

Lecture 3: Regression Equation

Lecture 4: Tabulating the Values

Lecture 5: Interpretation and Implementation on Data Sets

Lecture 6: Continue on Interpretation on Database

Lecture 7: Significant Variable

Lecture 8: Calculating Corresponding Values

Lecture 9: Identify Dependent Variable

Lecture 10: Generate Descriptive Statistics

Lecture 11: Scatterplot of Energy Consumption

Lecture 12: Identity Equation

Lecture 13: P – Value and T – Value

Lecture 14: Changes in Tem. and Expansion

Lecture 15: Objective of Stock Prices

Lecture 16: Interpretations of Example 5

Lecture 17: Reliance Return Change

Lecture 18: Generate Predicted Values

Lecture 19: Scatterplot Return RIL

Lecture 20: Basic Multiple Regression

Lecture 21: Basic Multiple Regression Continues

Lecture 22: Basic Multiple Regression – Interpretation

Lecture 23: Generate Basic Statistics

Lecture 24: Working on Scatterplot

Lecture 25: Dependent Variable Objective

Lecture 26: Concept of Multicollinearity

Lecture 27: Identify Dependent Variable Y

Lecture 28: Outputs and Observation

Lecture 29: Interpretations – Example 3

Lecture 30: Calculate with and without Flux

Lecture 31: Scatterplot of Heart FLux Vs Insolation

Lecture 32: Interpretation of Datasets

Lecture 33: Implementation of Datasets

Lecture 34: Example 4 Observations

Lecture 35: Display Descriptive Statistics

Lecture 36: Predicted Values Example 4

Lecture 37: Scatterplot of Example 4

Lecture 38: Calculating IV – Multiple Regression

Lecture 39: Calculating Independent Multiple Regression

Lecture 40: Understanding Basic Logistic Scatter Plot

Lecture 41: Basic Logistic Scatter Plot Continues

Lecture 42: Generation of Regression Equation

Lecture 43: Tabulated Values

Lecture 44: Interpretation and Implementation on Dataset

Lecture 45: Interpretation and Implementation on dataset Continues

Lecture 46: Output and Observation – Tabulated Values

Lecture 47: Business Metrics Example

Lecture 48: Example Two and Three Interpretations

Lecture 49: Regression Equation Group

Lecture 50: Interpretation and Implementation of Scatter Plot

Lecture 51: More on Implementation of Scatter Plot

Lecture 52: Plastic Case Strength

Lecture 53: Separate Equations

Lecture 54: Generation of Predicted Values

Lecture 55: Scatter Plot Strength Vs Temp

Lecture 56: Data of Cereal Purchase

Lecture 57: Children Viewed and RE

Lecture 58: Predicted Values for Individual Customers

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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