**Linear Regression Analysis Exercise**Use the following linear regression equation to answer the questions:\[ x_1 = 1.2 + 3.6x_2 - 7.6x_3 + 2.5x_4 \](a) **Identify Variables**1. Which variable is the response variable? - ○ \( x_1 \) - ○ \( x_4 \) - ○ \( x_2 \) - ○ \( x_3 \)2. Which variables are the explanatory variables? (Select all that apply.) - □ \( x_1 \) - □ \( x_4 \) - □ \( x_2 \) - □ \( x_3 \)(b) **Constant and Coefficients**1. Which number is the constant term? List the coefficients with their corresponding explanatory variables. - constant: [ ] - \( x_2 \) coefficient: [ ] - \( x_3 \) coefficient: [ ] - \( x_4 \) coefficient: [ ](c) **Prediction Equation**1. If \( x_2 = 8 \), \( x_3 = 6 \), and \( x_4 = 4 \), what is the predicted value for \( x_1 \)? (Use 1 decimal place.) - [ ](d) **Understanding Coefficients**1. Explain how each coefficient can be thought of as a "slope" under certain conditions. - ○ If we look at all coefficients together, each one can be thought of as a "slope." - ○ If we hold all explanatory variables as fixed constants, the intercept can be thought of as a "slope." - ○ If we look at all coefficients together, the sum of them can be thought of as the overall "slope" of the regression line. - ○ If we hold all other explanatory variables as fixed constants, then we can look at one coefficient as a "slope."(e) **Changes in Variables**1. Suppose \( x_3 \) and \( x_4 \) were held at fixed but arbitrary values and \( x_2 \) increased by 1 unit. What would be the corresponding change in \( x_1 \)? - [ ]2. Suppose \( x_2 \) increased by 2 units. What would be the

Use the following linear regression equation to answer the questions. X- 1.2+ 3.6x - 7.6x + 2.5x4 (a) Which variable is the response variable? O Which variables are the explanatory variables? (Select all that apply.) (b) Which number is the constant term? List the coefficients with their corresponding explanatory variables. constant 2 coefficient Xy coefficient

Use the following linear regression equation to answer the questions. X- 1.2+ 3.6x - 7.6x + 2.5x4 (a) Which variable is the response variable? O Which variables are the explanatory variables? (Select all that apply.) (b) Which number is the constant term? List the coefficients with their corresponding explanatory variables. constant 2 coefficient Xy coefficient

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

How do I go about these two questions followin this asnwer above: B) Ater removing the point with coordinates (2,9), use the pairs of values for the remaining 9 points and find the equation of the regression line. c. Compare the results from parts (a) and (b).
Suppose Ms. Carr is interested in the relationship between her students' 9th grade math final exam scores and the number of hours they spent studying during the year. She collects information for 15 ninth grade students and uses it to obtain the regression equation, where ?x is the number of hours spent studying during the year and ?̂ is the predicted final exam score. ?̂ =1.48?+75.64 The scatter plot displays her results. What is the predicted ?̂ value when a student spends 13 hours studying? Round your answer to one decimal place. Select the correct interpretation. The above predicted value of ?̂ is the predicted final math test score if a student studies 13 hours during the year. the predicted score of 13 out of 15 students. what any student will score on the final exam if they study at least 13 hours. the math final test score that 13 students beat.
The table shows the number of goals allowed and the total points earned (2 points for a win, and 1 point for an overtime or shootout loss) by 14 ice hockey teams over the course of a season. The equation of the regression line is y=−0.532x+211.813. Use the data to answer the following questions. (a) Find the coefficient of determination, r2, and interpret the result. (b) Find the standard error of the estimate, se, and interpret the result Goals Allowed, x Points, y215 112210 104217 103219 96259 85267 77281 51201 102214 99206 103216 94200 89263 70243 72
Please help ))()()
Use the following linear regression equation to answer the questions. X1 = 1.5 + 3.6x2 - 7.7x3 + 2.3x4 (a) Which variable is the response variable? O X4 O X2 O 3 Which variables are the explanatory variables? (Select all that apply.) O 3 O X4 O X1 O ×2 (b) Which number is the constant term? List the coefficients with their corresponding explanatory variables. constant X2 coefficient X, coefficient X4 coefficient (c) If x2 = 4, x3 = 9, and x4 = 10, what is the predicted value for x,? (Use 1 decimal place.) (d) Explain how each coefficient can be thought of as a "slope" under certain conditions. O If we look at all coefficients together, each one can be thought of as a "slope." O If we hold all explanatory variables as fixed constants, the intercept can be thought of as a "slope." O If we hold all other explanatory variables as fixed constants, then we can look at one coefficient as a "slope." O If we look at all coefficients together, the sum of them can be thought of as the overall…
Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.
a. Using the pairs of values for all 10 points, find the equation of the regression line. b. After removing the point with coordinates (2,3), use the pairs of values for the remaining 9 points and find the equation of the regression line. c. Compare the results from parts (a) and (b).
A sports statistician was interested in the relationship between game attendance (in thousands) and the number of wins for baseball teams. Information was collected on several teams and was used to obtain the regression equation ŷ = 4.9x + 15.2, where x represents the attendance (in thousands) and ŷ is the predicted number of wins. What is the predicted number of wins for a team that has an attendance of 17,000? 83.3 wins 98.5 wins 258.4 wins 263.3 wins
How is the Polynomial Regression model different from the Simple Linear Regression and Multiple Linear Regression models? Select an answer: a. The independent variables are categorical instead of numerical. b. There are multiple dependent variables instead of just one. c. The independent variable is raised to a higher power (squared, cubed, etc.). d. The dependent variable is raised to a higher power (squared, cubed, etc.).