[{"id":"b6e21c9d-a40b-4362-ac41-fb652256e69a","public":true,"title":"Linear Regression","description":"A quick recap","created_at":"2025-11-26T15:02:26.705958","updated_at":"2025-11-27T00:22:55.926207","user_id":{"id":"53b2630f-c51e-41c1-a20c-1bb55d33d1d4","quizs":[{"id":"b6e21c9d-a40b-4362-ac41-fb652256e69a","public":true,"title":"Linear Regression","description":"A quick recap","created_at":"2025-11-26T15:02:26.705958","updated_at":"2025-11-27T00:22:55.926207","user_id":{"id":"53b2630f-c51e-41c1-a20c-1bb55d33d1d4"},"questions":[{"question":"Which residual plot shows homoscedasticity?","time":"30","type":"ABCD","answers":[{"right":true,"answer":"Plot A","color":"#76cadb"},{"right":false,"answer":"Plot B","color":"#dda036"}],"image":"6252b078-7ec0-4df2-af76-b2ad0364e0fb","hide_results":false},{"question":"Which plot shows LOWER OLS variance?","time":"30","type":"ABCD","answers":[{"right":false,"answer":"Plot A","color":"#31a6eb"},{"right":true,"answer":"Plot B","color":"#76eb31"},{"right":false,"answer":"Both show the SAME OLS variance","color":"#eb9e31"},{"right":false,"answer":"Can't be answered with this plots","color":"#e831eb"}],"image":"90611c52-8ed7-48d7-93c7-3657d7f86f24","hide_results":false},{"question":"Which variables is not significant? (Hint: alpha-value=0.05)","time":"30","type":"ABCD","answers":[{"right":false,"answer":"x1","color":"#31a6eb"},{"right":false,"answer":"x2","color":"#76eb31"},{"right":false,"answer":"x3","color":"#eb9e31"},{"right":true,"answer":"x4","color":"#e831eb"}],"image":"425050d2-179a-4727-9a6b-9112e1190c4f","hide_results":false},{"question":"What is the correct interpretation of R-squared = 0.9637?","time":"30","type":"ABCD","answers":[{"right":false,"answer":"96.37% of the data points fall on the regression line","color":"#31a6eb"},{"right":false,"answer":"There is a 96.37% probability that X influences Y","color":"#76eb31"},{"right":true,"answer":"96.37% of the variance in Y is explained by the regression model","color":"#eb9e31"},{"right":false,"answer":"The model makes correct predictions 96.37% of the time","color":"#e831eb"}],"image":"16dd2824-b4d3-4d91-bb05-3c099c8aaefb","hide_results":false},{"question":"Comparing the two regression models below, which model is BETTER?","time":"35","type":"ABCD","answers":[{"right":false,"answer":"Model 1","color":"#31a6eb"},{"right":true,"answer":"Model 2","color":"#76eb31"}],"image":"9669af17-ddb5-46d2-be4c-5b2d68188835","hide_results":false},{"question":"What is t hedifference between R-squared and ADJUSTED R-squared?","time":"30","type":"ABCD","answers":[{"right":false,"answer":"-squared measures linearity, Adjusted R-squared measures non-linearity","color":"#31a6eb"},{"right":false,"answer":"R-squared measures prediction accuracy, Adjusted R-squared measures causality","color":"#76eb31"},{"right":true,"answer":"R-squared always increases with more variables, Adjusted R-squared penalizes unnecessary complexity","color":"#eb9e31"},{"right":false,"answer":"R-squared is for small samples, Adjusted R-squared is for large samples","color":"#e831eb"}],"image":"ce2fd80e-4895-4136-9562-6c0ff7b09569","hide_results":false},{"question":"What is heteroskedasticity?","time":"30","type":"ABCD","answers":[{"right":false,"answer":"When residuals are not normally distributed","color":"#31a6eb"},{"right":false,"answer":"When predictors are correlated with each other","color":"#76eb31"},{"right":true,"answer":"When the variance of residuals is not constant","color":"#eb9e31"},{"right":false,"answer":"When the regression line is curved","color":"#e831eb"}],"image":"1d7d015b-f45e-4239-addd-7ff5919d7b29","hide_results":false},{"question":"What is the opposite of heteroskedasticity?","time":"30","type":"ABCD","answers":[{"right":false,"answer":"Multicollinearity","color":"#31a6eb"},{"right":true,"answer":"Homoskedasticity","color":"#76eb31"},{"right":false,"answer":"Autocorrelation","color":"#eb9e31"},{"right":false,"answer":"Normality","color":"#e831eb"}],"image":"1e0f99cd-984c-427b-aba3-2292ddb7d7d3","hide_results":false},{"question":"Based on the regression output below, which is the correct final model description? 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@classy

Linear Regression