Please rank these four classification models from the most flexible model to the least flexible model: (assume that the dataset contains 5 variables, but a model doesn't have to use all variables. )• Logistic regression with 1 variable• Logistic regression with 5 variables• KNN with K=1 using all 5 variables• KNN with K=3 using all 5 variablesLogistic regression with 1 variableLogistic regression with 5 variablesKNN with K=1 using all 5 variablesKNN with K=3 using all 5 variables

Answered: • Logistic regression with 1 variable •…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Find the regression line round all decimals to 4 decimal places (5,5)(7,7)(8,9)
Type regression equation (you may omit sub_index i at all variables) or describe the relationship that this model is estimating with no more than 2 sentences.
Due in 3 hours, 41 minutes. Due Sat 04/18/2020 11:59 pm Show Intro/Instructions A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: у-ах+b a=6.704 b=30.59 p2=0.972196 r=0.986 Use this to predict the final exam score of a student who studies 8.5 hours per week, and please round your answer to a whole number. Hint: Help License Points possible: 1 This is attempt 1 of 3. Message instructor about this question Submit
8- According to the summary result of linear regression model between A and B obtained from R given below, we can fit a regression line. Assume that A has any value. If we decrease the value of A by 3, how would Y be affected? Call: Im (formula = B - A) Residuals: Min 10 Median 30 Маx -16.340 -10.793 -9.653 -8.502 58.325 Coefficients: Estimate Std. Error t value Pr (>[t]) (Intercept) 19.6315 20.6457 0.951 0.373 A 9.9609 0.3717 26.800 2.58e-08 *** --- Signif. codes: O *** 0.001 1** 0.01 1** 0.05 '.' 0.1 '' 1 Residual standard error: 26.46 on 7 degrees of freedom Multiple R-squared: 0.9903, Adjusted R-squared: 0.989 F-statistic: 718.2 on 1 and 7 DF, p-value: 2.58le-08 a) 49.5142 decrease b) 29.8827 increase c) 58.8945 decrease 29.8827 decrease 58.8945 increase
I am completely lost please help
the regression model using 240 monthly observation stock return = bo+b,*size+b,*CM+b;+profitability+b,investment +e coefficien standard error bo -0.01 0.03 b1 0.22 0.25 b2 0.02 0.13 b3 0.03 0.08 b4 0.04 0.06 df sum residual squared regression 4 0.095 Which one of the four variable can explain the time variation in stock return given 95% confidence level? Is the four-factor model is better than the random walk model in explain the return given 95% confidence level?
explain the model that given
Define Least Squares Regression Unbiased Estimators α^, β^, σ^²?
For the linear regression model Y = bo + b1(X): The p-value for the intercept is large: about 0.98 The p-value for the slope is very small: less than 2 times 10^(-16) What can we conclude? Since the p-value for the intercept is large, we can conclude that there is not a strong correlation between X and Y. Since the p-value for the intercept is large, we can conclude that there is a very strong correlation between X and Y. Since the p-value for the slope is very small, we can conclude that there is a very weak correlation between X and Y. Since the p-value for the slope is very small, we can conclude that there is a very strong correlation between X and Y. We are not able to assess the strength of the correlation between X and Y with the output provided.
Using the regression model, which is the estimated score in quiz of a student and is the number of hours the student spent in studying before the quiz, interpret the regression constant (bo) 50. None of the above. O The estimated score of a student who had 0 hours spent in studying is 50. The regression constant is not appropriate to interpret. The number of hours needed to have a score of 100 is 50.
For a regression line y^=mx+b, what does the Sum of Squares Due to Error measure? Select the correct answer below: the total variation in x that cannot be explained by variation in y the total variation in y that cannot be explained by the error, or residuals, for x the total variation in y that cannot be explained by the variation in x the total variation in x that cannot be explained by the error, or residuals, for y
Exercise 4 In this regression we presented the results of the consumption fitted to the income in the period 1980-2005. We divide the data in two periods as 1980-1992 and 1993-2005. Decide if there is a structural change in the consumption-income regression in the two periods. The results for all data are as follows cons = 179 + 0.85 Income RSSfull = 265277 The results for 1980-1992(1) and 1993-2005 (2) are as follows 143 - 0.85 Income RSSfull = 13310 (1) cons = cons = 110 + 0.18 Income RSSfui = 163505 (2) fll

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