Homework Help is Here – Start Your Trial Now!
Math
Statistics
Obs. 1 2 3 4 6 7 8 9 10 00 Xu X 5 s LD 00 6 14 55 12 15 16 16 19 Y 23 16 25 ~~ 28 13 51 37 51 51 788 56 60 Y-hat 0 0 0 OO 0 0 0 0 23 16 25 ~~ 28 10 m 51 37 51 788 51 56 60 e² 529 256 625 784 2601 1369 2601 2601 3136 3600 Your Model Bo B₁ Sum of Error SSE SSX S.E. of the Estimate S.E. of B₁ 398 18102 I I 1 I

Obs. 1 2 3 4 6 7 8 9 10 00 Xu X 5 s LD 00 6 14 55 12 15 16 16 19 Y 23 16 25 ~~ 28 13 51 37 51 51 788 56 60 Y-hat 0 0 0 OO 0 0 0 0 23 16 25 ~~ 28 10 m 51 37 51 788 51 56 60 e² 529 256 625 784 2601 1369 2601 2601 3136 3600 Your Model Bo B₁ Sum of Error SSE SSX S.E. of the Estimate S.E. of B₁ 398 18102 I I 1 I

MATLAB: An Introduction with Applications
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
icon
Related questions
Question

first image is the assignment, second image is the data table

A B Obs. 1 2 3 st 4 5 01 6 7 5600 10 U X 5 5 LO 6 8405 1615 19 14 12 15 D Y 23 16 25 28 in m 51 37 51 51 56 60 E Y-hat 0 0 0 0 0 0 0 0 OO 0 0 F e 3 23 16 25 28 51 37 51 51 56 60 G 529 256 625 784 2601 1369 2601 2601 3136 3600 H Your Model Bo B₁ Sum of Error SSE SSX S.E. of the Estimate S.E. of B₁ K 398 18102 I I i I I L
Transcribed Image Text:A B Obs. 1 2 3 st 4 5 01 6 7 5600 10 U X 5 5 LO 6 8405 1615 19 14 12 15 D Y 23 16 25 28 in m 51 37 51 51 56 60 E Y-hat 0 0 0 0 0 0 0 0 OO 0 0 F e 3 23 16 25 28 51 37 51 51 56 60 G 529 256 625 784 2601 1369 2601 2601 3136 3600 H Your Model Bo B₁ Sum of Error SSE SSX S.E. of the Estimate S.E. of B₁ K 398 18102 I I i I I L
regression predictions that depend on the values found in the table called "Your Model". Here is the breakdown of the data in the first table: >> Obs. is the observation number. It runs from 1 to 10. >> X is the set of values for an independent variable being used to predict the dependent variable. Y is the set of values for the dependent variable being predicted by the regression. >> >> Y-hat is the set of predicted values found by multiplying X by the coefficient for B₁ and then adding the value for B₂. This is what the regression line predicts for Y for a given value of X. >>e is the set of residuals for the regression. It is the difference between Y and Y-hat. >> e² is the residuals squared. That simple. Investigate the table of data and familiarize yourself with the equations used to calculate the regression predictions. Then start entering different values for B₁ and B₂ in the "Your Model" table. You'll notice things change when you do this. "Sum of Error" adds up the values of column F in the data table. "SSE" adds up the values in column G in the data table, and is otherwise known as the "Sum of Squares Error". Below this is the SSX or "Sum of Squares for X" which you will need to calculate the S.E. of the Estimate, or "Standard Error of the Estimate" below. And in the last row, you have to calculate the S.E. of B₁ or the "Standard Error of the Slope". Sxy cov(x,y) var (x) B₁ = 53/2= = SZ Ba =ỳ - B Se (S. E. of the Estimate) : SB, (S.E.of B₁) = √SSX SSE n-2 You need to calculate values for each of the tan cells in the spreadsheet. For B₁ and 3₂ you will need to write your own formulas which reference the data for X and Y in the data table. For SSX, remember that the SSX is the numerator of the variance of X, which you can find by multiplying the variance by n-1. For S.E. of the Estimate and the S.E. of B₁ you will again need to write your own formula which uses the SSE, SSX, and the count of observations.
Transcribed Image Text:regression predictions that depend on the values found in the table called "Your Model". Here is the breakdown of the data in the first table: >> Obs. is the observation number. It runs from 1 to 10. >> X is the set of values for an independent variable being used to predict the dependent variable. Y is the set of values for the dependent variable being predicted by the regression. >> >> Y-hat is the set of predicted values found by multiplying X by the coefficient for B₁ and then adding the value for B₂. This is what the regression line predicts for Y for a given value of X. >>e is the set of residuals for the regression. It is the difference between Y and Y-hat. >> e² is the residuals squared. That simple. Investigate the table of data and familiarize yourself with the equations used to calculate the regression predictions. Then start entering different values for B₁ and B₂ in the "Your Model" table. You'll notice things change when you do this. "Sum of Error" adds up the values of column F in the data table. "SSE" adds up the values in column G in the data table, and is otherwise known as the "Sum of Squares Error". Below this is the SSX or "Sum of Squares for X" which you will need to calculate the S.E. of the Estimate, or "Standard Error of the Estimate" below. And in the last row, you have to calculate the S.E. of B₁ or the "Standard Error of the Slope". Sxy cov(x,y) var (x) B₁ = 53/2= = SZ Ba =ỳ - B Se (S. E. of the Estimate) : SB, (S.E.of B₁) = √SSX SSE n-2 You need to calculate values for each of the tan cells in the spreadsheet. For B₁ and 3₂ you will need to write your own formulas which reference the data for X and Y in the data table. For SSX, remember that the SSX is the numerator of the variance of X, which you can find by multiplying the variance by n-1. For S.E. of the Estimate and the S.E. of B₁ you will again need to write your own formula which uses the SSE, SSX, and the count of observations.
Expert Solution
steps

Step by step

Solved in 4 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman
SEE MORE TEXTBOOKS