Given the pairs (time, price) (1,3), (2,5), (3,8) use linear regression to find the line ax+b that approximate these values. Use that line to calculate the price when the time is 4.
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Given the pairs (time, price) (1,3), (2,5), (3,8) use linear regression to find the line ax+b that approximate these values. Use that line to calculate the price when the time is 4.
- Let’s say “x” and “y” are “2” variables and assume that “x” is a controlled variable and “y” is a dependent variable.
- It is required to evaluate the value of “y”, when “x” is given.
- The formula to produce the line is as follows,
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- When and why do we use the Proportional, Binomial, and Poisson regression modelsHow and why do each of these models include degrees of freedom?When and why do we employ the Binomial, Poisson, and Proportional models of regression?USING R ONLY! Generate a sample from a theoretical linear regression: Z = 1.5X – 2.8Y – 4.3 N(0, 2.5^2). Then use the sample data to create a linear regression model to predict Z by X and Y. Interpret the regression results one by one and create residual plots to describe how to use each residual plot.
- The following data give the starting salary for students who recently graduated from a local university and accepted jobs soon after graduation. The starting salary, grade-point average (GPA), and major (business or other) are provided. SALARY $29,500 $46,000 $39,800 $36,500 GPA 3.1 3.5 3.8 2.9 Major Other Business Business Other SALARY $42,000 $31,500 $36,200 GPA 3.4 2.1 2.5 Major Business Other Business Using a computer, develop a regression model that could be used to predict a starting salary based on GPA and major. Use this model to predict the starting salary for a business major with a GPA of 3.0. What does the model say about the starting salary for a business major compared to a non-business major? Do you believe this model is useful in predicting the starting salary? Justify your answer, using the information provided in the computer output.Make a time series analysis about the data in table below. Make 2 or more regression models by using stata!Develop a simple linear regression model (univariate model) using gradient descent method for experience-salary datasets as it is shown on the following table. Once you got the model, check how close the predicted values against the ground truth and calculate the total error (mean square error) and the accuracy R^2.
- If you are implementing regularised linear regression and when you tested your hypothesis in a new dataset you found that it suffer from high variance. How can you rectify the model ? Give 3 SolutionsWhy and why do we use the Binomial, Poisson, and Proportional models of regression? Explain how and why degrees of freedom are included into each of these models.Question 91. Consider the following training set of m=4 training examples: x y 0.1 0.6 1 1.5 0 0.5 3 3.5 Consider the linear regression model hθ(x)=θ0+θ1x. What are the values of θ0 and θ1 that you would expect to obtain upon running gradient descent on this model? (Linear regression will be able to fit this data perfectly.)
- ) Develop a simple linear regression model (univariate model) using gradient descent methodfor experience-salary datasets as it is shown on the following table. Once you got the model, checkhow close the predicted values against the ground truth and calculate the total error (mean squareerror) and the accuracy R2.No Experience Salary ($) PredictedDevelop a simple linear regression model (univariate model) using gradient descent methodfor experience-salary datasets as it is shown on the following table. Once you got the model, checkhow close the predicted values against the ground truth and calculate the total error (mean squareerror) and the accuracy R2.Using R perform linear regression for the following data set and derive the equation. If the age is 12, predict what is the weight? Age 1 3 10 16 26 36Weight 22 30 50 60 70 75