Homework Answers
Answer.
D. Least square error.
In linear regression, we try to minimize the least square errors of the model to identify the line of best fit.
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Which of the following methods do we use to find the best fit line for data...
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation...
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation Error Training Error Error Error Error Training set size Training set size Model-1 Training set size Model-3 Model-2 A) Model-1 B) Model-2 C) Model-3 Q55) [1 Point] Maximal margin classifiers are sensitive to outliers in training data. A) True B) False Q56) [1 Point] Soft margin classifiers allows for misclassification in training data. A) True B) False Q57) [1 Point] Which of the following...
The “least square regression model” is based on the “best fit” line to the data. This...
The “least square regression model” is based on the “best fit” line to the data. This will determine a line equation for LINEAR data that will minimize “residual” values (difference between actual and “predicted” ) True or False Correlation tells us if there is a relationship between two numeric variables and how strong that relationship is: True or False
The table shows data collected on the relationship between the average daily temperature and coffee sales (in hundreds of dollars) at a coffee shop. The line of best fit for the data is -0...
The table shows data collected on the relationship between the average daily temperature and coffee sales (in hundreds of dollars) at a coffee shop. The line of best fit for the data is -0.68z +85.1. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Temperature (Degrees) Coffee Sales (in hundreds of dollars) 30 40 50 60 65 58 50 45 According to the line of best fit, what would be the...
7. The line of best fit is the 8. The point at which the regression line...
7. The line of best fit is the 8. The point at which the regression line crosses the is referred to as the intercept. 9. The proportionate reduction in error is also called the 10. variables are independent variables that make separate but distinct contributions in the prediction of a dependent variable, as compared with the contributions of other variables. 11. If both the independent and dependent variables are measured as scale variables, then one can use either correlation or...
q We would like to fit a line y = cx + d to the following...
q
We would like to fit a line y = cx + d to the following data х у -1 -3 0 -1 0 2 3 using the method of least squares. (a) Write down the (overdetermined) linear system this problem gives rise to in the form Ax = b, where x = (..) (b) Find the best-fit line by computing the least-squares solution of the system Ax = b.
Linear Algebra Question 25. Suppose you are looking at data which is supposed to fit an...
Linear Algebra Question
25. Suppose you are looking at data which is supposed to fit an exponential equation, i.e. a model of the form y= Cekz where C and k are constants. Suppose your data points are (2, 5), (3,8) and (4, 17). Use least-squares to find (decimal approximations to) the values of C and k which best fit this model How to proceed: First, take the natural log of both sides of the model to obtain what is called...
4. Consider the following data. 16 74 900 29810 (a) Suppose you would like to fit...
4. Consider the following data. 16 74 900 29810 (a) Suppose you would like to fit a model of the form yCes (C and k are constants) to this data. Transform the model in such a way that you can use linear least squares to determine C and k. (b) For this data set, use the following equations to find a best fit line. and (e) Use your calculation from part b) to find the model y - Cek for...
21. Use the least square method to determine the equation of line of best fit for...
21. Use the least square method to determine the equation of line of best fit for the data by completing the following table and then plot the line. 24 120 1 649 100 121 36 2569 33 36 5 8 120 1 848 1나나 81 36 14 7 64 10 528 656 1312 College of WestchUSIU
This is how you import the Carseats data into Rstudio software: library(“ISLR”) data(“Carseats”) ...
This is how you import the Carseats data into Rstudio
software:
library(“ISLR”)
data(“Carseats”)
view(Carseats)
after that, please provide codes for following:
a. Split the data into a training set and a test set. b. Fit a linear model using least squares on the training set to predict Sales using the entire collection of predictors. Report the Cp, BIC, R2, and RSS for this model c. Use the fitted model to predict responses for the test data and report the test...
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the ...
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the best linear least-squares fit to the following data. x2.4 3.6 3.64 4.7 5.3 y| 33.8 34.7 35.5 36.0 37.5 38.1 Plot both the linear function and the data points on the same axis system Solution We can solve the problem with the following MATLAB commands x[2.4;3.6; 3.6;4.1;4.7;5.3]; y-L33.8;34.7;35.5;36.0;37.5;38.1 X [ones ( size (x)),x); % build the matrix X for linear model %...
Q54) [1 Point] Which of the following learning curves represent a good linear regression model? Validation Error Training Error Error Error Error Training set size Training set size Model-1 Training set size Model-3 Model-2 A) Model-1 B) Model-2 C) Model-3 Q55) [1 Point] Maximal margin classifiers are sensitive to outliers in training data. A) True B) False Q56) [1 Point] Soft margin classifiers allows for misclassification in training data. A) True B) False Q57) [1 Point] Which of the following...
The table shows data collected on the relationship between the average daily temperature and coffee sales (in hundreds of dollars) at a coffee shop. The line of best fit for the data is -0.68z +85.1. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Temperature (Degrees) Coffee Sales (in hundreds of dollars) 30 40 50 60 65 58 50 45 According to the line of best fit, what would be the...
7. The line of best fit is the 8. The point at which the regression line crosses the is referred to as the intercept. 9. The proportionate reduction in error is also called the 10. variables are independent variables that make separate but distinct contributions in the prediction of a dependent variable, as compared with the contributions of other variables. 11. If both the independent and dependent variables are measured as scale variables, then one can use either correlation or...
q
We would like to fit a line y = cx + d to the following data х у -1 -3 0 -1 0 2 3 using the method of least squares. (a) Write down the (overdetermined) linear system this problem gives rise to in the form Ax = b, where x = (..) (b) Find the best-fit line by computing the least-squares solution of the system Ax = b.
Linear Algebra Question
25. Suppose you are looking at data which is supposed to fit an exponential equation, i.e. a model of the form y= Cekz where C and k are constants. Suppose your data points are (2, 5), (3,8) and (4, 17). Use least-squares to find (decimal approximations to) the values of C and k which best fit this model How to proceed: First, take the natural log of both sides of the model to obtain what is called...
4. Consider the following data. 16 74 900 29810 (a) Suppose you would like to fit a model of the form yCes (C and k are constants) to this data. Transform the model in such a way that you can use linear least squares to determine C and k. (b) For this data set, use the following equations to find a best fit line. and (e) Use your calculation from part b) to find the model y - Cek for...
21. Use the least square method to determine the equation of line of best fit for the data by completing the following table and then plot the line. 24 120 1 649 100 121 36 2569 33 36 5 8 120 1 848 1나나 81 36 14 7 64 10 528 656 1312 College of WestchUSIU
This is how you import the Carseats data into Rstudio
software:
library(“ISLR”)
data(“Carseats”)
view(Carseats)
after that, please provide codes for following:
a. Split the data into a training set and a test set. b. Fit a linear model using least squares on the training set to predict Sales using the entire collection of predictors. Report the Cp, BIC, R2, and RSS for this model c. Use the fitted model to predict responses for the test data and report the test...
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the best linear least-squares fit to the following data. x2.4 3.6 3.64 4.7 5.3 y| 33.8 34.7 35.5 36.0 37.5 38.1 Plot both the linear function and the data points on the same axis system Solution We can solve the problem with the following MATLAB commands x[2.4;3.6; 3.6;4.1;4.7;5.3]; y-L33.8;34.7;35.5;36.0;37.5;38.1 X [ones ( size (x)),x); % build the matrix X for linear model %...
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