Consider the regression model CEOSAL = 2.5 + 0.5 * sales – 0.1 * sales 2 Note that salesl 2 = sales*sales. What is the effect of an increase from 1 to 2 in sales on CEOSAL
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Consider the regression model CEOSAL = 2.5 + 0.5 * sales – 0.1 * sales 2
Note that salesl 2 = sales*sales. What is the effect of an increase from 1 to 2 in sales on CEOSAL?
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- Write code to define a K-NN regression model with K=5 and the neighbors are weighted by the inverse of their distance. [ ] # Write your code herePython Regression Model 1: train MSE = 0.423, test MSE = 0.978 Model 2: train MSE = 0.572, test MSE = 0.644 Model 3: train MSE = 0.218, test MSE = 1.103 Based on this information, which of these models generalises the best to unseen data?Write the objective function that can be used to determine the regression model parameters. How is this objective function will be used to find model parameters?
- For evaluation of regression models, typically, the higher the [ Select ] ["Adjusted R Squared", "Residual Standard Error"] the better, and the lower the [ Select ] ["Adjusted R Squared", "Residual Standard Error"] , the better.Take a look at the confusion matrix below. How many values did the logistic regression model incorrectly predict as positive? Predicted Values 55 1112 98 150 No Yes Actual Values No 1112 98 Yes 55 150In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535
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- A regression result of y = Bo + B1x1 + ɛ is given in the below plot, where y is Sales, and x, is TV. What are estimated Bo, ß1 values? 50 100 200 300 TV Sales 25If we add more independent variables into the model: A. The adjusted R2 value will increase. B. The R2 value will increase. C. The R2 value will decrease if the variables we are adding into the model should not be there. D. The R2 will be biased.What is the name given to an issue which arises in multiple regression when there is high correlation among two or more independent variables? Answer Choices: a) Heteroscedasticity b) Multicollinearity c) Autocorrelation d) Serial correlation