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Regression Analysis & Predictive Modelling
Rating: 3.8 out of 5(16 ratings)
703 students

Regression Analysis & Predictive Modelling

Become confident to make predictions by Regression Analysis
Created byDr. Sumit Saha, Ph.D
Last updated 1/2025
English
English [Auto],

What you'll learn

  • Regression Analysis
  • Making Prediction and data analysis
  • concept of modelling construction
  • Do not think you I am teaching you remotely, I am always happy to clear your doubts. Just message me anytime!

Course content

1 section • 13 lectures • 1h 32m total length

  • Know your Instructor0:24

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  • Concept and types of Regression Analysis8:50

    Explore how correlation leads to regression analysis, distinguishing simple versus multiple regression and linear versus nonlinear forms, with ordinal regression for categorical data and historical data insights.

  • Linear Regression6:35

    Explore the linear regression line y = a + b x, linking dependent and independent variables, with correlation checks and dropping noncorrelated variables to ensure normality and no multicollinearity.

  • Minimum Least Square Method10:05

    Learn how the minimum least squares method finds the best fit line by minimizing the sum of squared errors and estimating coefficients in y = a + b x.

  • Calculating coefficients values4:44

    Calculate regression coefficients b1 and b0 in simple regression using deviations from means and standard deviations to predict the dependent variable from x, with algebra for multiple variables.

  • Ascombe's Quartet4:54
  • Regression Equations- x on y & y on x8:27

    Explains regression equations for two variables, x on y and y on x, defines regression coefficients using r and standard deviations, and presents their B_xy and B_yx formulas.

  • Regression-solved problem-15:54

    Calculate the correlation coefficient using the given standard deviations of X and Y and the values X=63 and Y=50, yielding approximately 0.78.

  • Predicting mom's height based on daughter's height11:00
  • Regression-Solved problem-210:38

    solve a regression problem via cross‑multiplication to obtain x-bar=13 and y-bar=17, then compute b_x, b_y, and r, confirming a positive relationship.

  • Probable Error- Calculating correlation coefficient of POPULATION8:39

    Explore how to estimate the population correlation coefficient from sample data using the probable error formula, interpret thresholds, and understand assumptions about random sampling and normal population.

  • Solved Problem_Calculation of Probable Error3:24
  • Coefficient of Determination8:46

Requirements

  • Basic Descriptive statistical concept is important to know
  • Additionally one can take my Udemy course "Descriptive Statistics Masterclass" for better understanding

Description

Regression Analysis examines the influence of one or more independent variables on a dependent variable, and Predictive modelling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question "what might possibly happen in the future?"

Taught 3000+ students offline and now extending the course and experience to online students like you.

Winners don't do different things, they do things differently. Complete course guide, separate guide/link of 700+ practice questions, downloadable resources, supportive animation/videos for better understanding.

By the end of this course, you will be confidently implementing techniques across the major situations in Statistics, Business, and Data Analysis.

You'll Also Get:

- Downloadable workout Notes for competitive exams and future reference purpose

- Lifetime Access to course updates

- Fast & Friendly Support in the Q&A section

-Certificate of Completion Ready for Download


Don't Miss Out!

Every second you wait is costing you a valuable chance for learning and to outstand.

These courses come with a 30-day money-back guarantee - so there's no risk to get started.

Who this course is for:

  • For all students who want to have a strong base of Statistics

  • Anyone looking to start a pick up highly paid freelancing skills

  • Consultants who don't know where to get started

  • People who want a career in Business Intelligence & Data Science

  • Business executives & Business Analysts

Who this course is for:

  • Anyone

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