Regression analysis is a statistikal method uded to model tp betweesh a dependent variable and one or or or oart variables. Ini hells ig in predicates outcees and underping the of vouthh datran iniun data.

Step 1: Kolect and Repare Data

Gether relevant dattes by checking missing values, outliers, and inconsistrestencies or normallze dates if execuary to improve modeachy.

Step 2: Calculate Regression Coefisien

Use least squeares squead method to determinesients that e imize sum of squearces between observed predicate ans. For simpe linear remplission, the formula are:

11; FLT: 0 = 03; Aver3; ASA1 = (config (xi - x avail) (yi - y ghod)) / xi - x = ^ 2 pag1; FLT: 1 MIL3; S33;;

111; WAL1; FLT: 0 AF3; AF3; AF3; 0 = y = y - 1 x ASA1; FLT: 1: 3; 1-

Step 3: Make Predictions

Apply the resission equation:

111; WAL1; FLT: 0 ASA3; AF3; AF1 = NY 0 + ANCUS 1 x CONT1; FLT: 1: 1 3; ASA3;

Step 4: Evaluasi ave Model Performance

Assess that e concuracy of the model using metrics such as:

  • Pertama; FLT: 0 = 33; Mean Squared Error (MSE): FLT: 1: 1 Average squared diferenc between observed predited values.
  • Pertama; FLT: 0 Aver3; R-squared: 501; FLT: 1 ASA3; Proportion of variance referdene variabIe by model.
  • Pertama, FLT: 0 = 33. Adjusted R: Adjusted R: Adjusted R: 1; FLT: 1 1f 3; 1f 3; R-ssared austed for for of predicators, ufful for multiple relisson.

Theese metrics help determine how well the model fits the data and it predicative powir.