data <- read.csv("Student_Performance.csv")model <- lm(`Performance.Index` ~ `Hours.Studied` + `Previous.Scores` + `Extracurricular.Activities` + `Sleep.Hours` + `Sample.Question.Papers.Practiced`, data = data)summary(model)##
## Call:
## lm(formula = Performance.Index ~ Hours.Studied + Previous.Scores +
## Extracurricular.Activities + Sleep.Hours + Sample.Question.Papers.Practiced,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.6333 -1.3684 -0.0311 1.3556 8.7932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -34.075588 0.127143 -268.01 <2e-16 ***
## Hours.Studied 2.852982 0.007873 362.35 <2e-16 ***
## Previous.Scores 1.018434 0.001175 866.45 <2e-16 ***
## Extracurricular.ActivitiesYes 0.612898 0.040781 15.03 <2e-16 ***
## Sleep.Hours 0.480560 0.012022 39.97 <2e-16 ***
## Sample.Question.Papers.Practiced 0.193802 0.007110 27.26 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.038 on 9994 degrees of freedom
## Multiple R-squared: 0.9888, Adjusted R-squared: 0.9887
## F-statistic: 1.757e+05 on 5 and 9994 DF, p-value: < 2.2e-16```