Close Menu
  • Home
  • Apple
  • Android
  • Software
  • Technology
  • Windows
  • Security
  • Microsoft
  • Web Design
  • Legal
    • Law
What's Hot

Betslagning Online Ramverk Upppackad ♬ Norrland 🪙

November 19, 2025

Circus.Nl Casino Aussie Cassino On-Line No More Deposit Incentive Atlantic Canada 💷

November 16, 2025

Casino Rocket No Deposit Bonus Codes 2025 Australia ✱ Grand Rush Casino No Deposit Bonus Codes Northern Canada 💸

November 16, 2025
Facebook X (Twitter) Instagram
Centurysoftwares.com
  • Home
  • News
  • Business
  • Technology
  • Software
  • Microsoft
  • Mobiles
  • Security
  • Web Design
Centurysoftwares.com
News

REGRESSION AND CORRELATION

AlanBy AlanOctober 19, 2020Updated:December 13, 2021No Comments2 Mins Read

This chapter examines the relationship between two variables using linear regression and correlation. Linear regression estimates a linear equation that describes the relationship, whereas correlation measures the strength of that linear relationship.

Simple Linear Regression

When you plot two variables against each other in a scatter plot, the values usually don’t fall exactly in a perfectly straight line. When you perform a linear regression analysis, you attempt to fi nd the line that best estimates the relationship between two variables (the y, or dependent, variable, and the x, or independent, variable). The line you fi nd is called the fi tted regression line, and the equation that specifi es the line is called the regression equation.

The Regression Equation

If the data in a scatter plot fall approximately in a straight line, you can use linear regression to fi nd an equation for the regression line drawn over the data. Usually, you will not be able to fi t the data perfectly, so some points will lie above and some below the fi tted regression line. The regression line that Excel fits will have an equation of the form y 5 a 1 bx. Here y is the dependent variable, the one you are trying to predict, and x is the independent, or predictor, variable, the one that is doing the predicting

Checking the Regression Model

As in any statistical procedure, for statistical inference on a regression, you are making some important assumptions. There are four:

  1. The straight-line model is correct.
  2. The error term e is normally distributed with mean 0.
  3. The errors have constant variance.
  4. The errors are independent of each other.

Conclusion

Whenever you use regression to fi t a line to data, you should consider these assumptions. Fortunately, regression is somewhat robust, so the assumptions do not need to be perfectly satisfi ed. One point that cannot be emphasized too strongly is that a signifi cant regression is not proof that these assumptions haven’t been violated. To verify that your data do not violate these assumptions is to go through a series of tests, called diagnostics.

crunknews.com The Modern Coffer of Information

Alan
  • Website

Recent Post

Building Mobile Apps with WebAssembly: Performance and Portability

February 26, 2024

Die Cut Sticker Marketing in 2024: Things you need to Know

February 1, 2024

Social Media and Mental Health

June 27, 2023

Top commercial kitchen equipment items that you must have while opening your commercial kitchen 

April 17, 2023
Categories
  • Android
  • Apps
  • Business
  • Computing
  • Gear
  • Insights
  • Law
  • Legal
  • Linux
  • Microsoft
  • Mobiles
  • News
  • Security
  • Software
  • Software Development
  • Spotlight
  • Technology
  • Web Design
  • Windows
  • Contact Us
  • Privacy Policy
Centurysoftwares.com © 2026, All Rights Reserved

Type above and press Enter to search. Press Esc to cancel.