Pick a reasonable value for your independent variable to make a prediction of the dependent variable. How do you know this is a reasonable value?Using the excel equation and your chosen value, make a prediction for your dependent variable – MAKE SURE TO SHOW WORK. Using your chosen value and the predicted value, explain what information this tells you. Based on all of the previous tasks, do you feel that your prediction is reliable – why or why not
Pick a reasonable value for your independent variable to make a prediction of the dependent variable. How do you know this is a reasonable value?Using the excel equation and your chosen value, make a prediction for your dependent variable – MAKE SURE TO SHOW WORK. Using your chosen value and the predicted value, explain what information this tells you. Based on all of the previous tasks, do you feel that your prediction is reliable – why or why not
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Pick a reasonable value for your independent variable to make a prediction of the dependent variable. How do you know this is a reasonable value?Using the excel equation and your chosen value, make a prediction for your dependent variable – MAKE SURE TO SHOW WORK. Using your chosen value and the predicted value, explain what information this tells you. Based on all of the previous tasks, do you feel that your prediction is reliable – why or why not?

Transcribed Image Text:35
30
25
20
15
10
5
0
At Bats
51
67
77
44
55
39
45
0
10
SUMMARY OUTPUT
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
20
30
Regression Statistics
Hits
19
25
30
20
23
16
18
Hits
40
SLOPE
0.340215
y = 0.3402x + 3.1998..
60
70
80
YIntercept
3.199795
90
Correlation
Regression
0.967625
0.954306
Field: At Bats and Field: Hits appear highly correlated.
35
30
25
20
15
10
5
0
20
30
40
50
60
70
80
Hits
At Bats
50
0.976885985
0.954306228
0.942882786
1.218625281
6
df
SS
MS
F
ignificance F
1 124.0598 124.0598 83.53928 0.000795
4
5.94019 1.485048
5
130
Coefficients andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0%
3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581
0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715
51
0
10
90

Transcribed Image Text:ANOVA
Regression
Residual
Total
Intercept
RESIDUAL OUTPUT
Observation
51
1
2
3
4
5
6
df
SS
MS
F
ignificance F
1 124.0598 124.0598 83.53928 0.000795
4
5.94019 1.485048
5
130
Coefficients
andard Erro t Stat P-value Lower 95% Upper 95% ower 95.0%Jpper 95.0%
3.726325329 2.06028 1.80865 0.144774 -1.99393 9.446581 -1.99393 9.446581
0.335296783 0.036685 9.139983 0.000795 0.233444 0.43715 0.233444 0.43715
Predicted 19
Residuals
26.19120979 -1.19121
29.54417762 0.455822
18.47938378 1.520616
22.16764839 0.832352
16.80289986 -0.8029
18.81468056 -0.81468
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