Estimate the mean of the independent variable (regression)Testing for a random walk with driftInterpreting...
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Estimate the mean of the independent variable (regression)
Testing for a random walk with driftInterpreting high p value and low correlation valueMultiple regression - low F-statistics and multiple R-squared. What should i do/concludePrediction Interval from Markov ChainsDegrees of freedom of t-test in multiple regression .Finding range of p-values from linear regression modelLinear regression where the error is modifiedthe predicted value of Y , when $x=x_0$Finding $n$ given the margin of error $1$%Problem on the Interpretation of Specfic Regression Equation Variables
$begingroup$
If I have the simple linear model: $y=alpha+beta x+epsilon$, and I know that
$hat{alpha}= hat{overline{y}}-hat{beta}hat{overline{x}}$.
Then my estimate og the mean is actually: $hat{overline{x}}=frac{hat{overline{y}}-hat{alpha}}{hat{beta}}$
I'm trying to do this in R, with the lm(), but I don't know if this is correct.
Call:
lm(formula = ult ~ vale)
Residuals:
Min 1Q Median 3Q Max
-278701 -34922 -13235 21227 940124
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.834e+04 7.337e+02 65.89 <2e-16 ***
vale 2.307e-04 5.874e-06 39.27 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 64480 on 13065 degrees of freedom
Multiple R-squared: 0.1056, Adjusted R-squared: 0.1055
F-statistic: 1542 on 1 and 13065 DF, p-value: < 2.2e-16
statistics regression math-software
edited 2 days ago
callculus
18.3k31427
asked 2 days ago
Lexie WalkerLexie Walker
1597
$endgroup$
add a comment |
$begingroup$
If I have the simple linear model: $y=alpha+beta x+epsilon$, and I know that
$hat{alpha}= hat{overline{y}}-hat{beta}hat{overline{x}}$.
Then my estimate og the mean is actually: $hat{overline{x}}=frac{hat{overline{y}}-hat{alpha}}{hat{beta}}$
I'm trying to do this in R, with the lm(), but I don't know if this is correct.
Call:
lm(formula = ult ~ vale)
Residuals:
Min 1Q Median 3Q Max
-278701 -34922 -13235 21227 940124
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.834e+04 7.337e+02 65.89 <2e-16 ***
vale 2.307e-04 5.874e-06 39.27 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 64480 on 13065 degrees of freedom
Multiple R-squared: 0.1056, Adjusted R-squared: 0.1055
F-statistic: 1542 on 1 and 13065 DF, p-value: < 2.2e-16
statistics regression math-software
edited 2 days ago
callculus
18.3k31427
asked 2 days ago
Lexie WalkerLexie Walker
1597
$endgroup$
$begingroup$
The lm()-function seems correct for linear models.
$endgroup$
– callculus
2 days ago
$begingroup$
@callculus, I know but the question is with that summary how do i get the estimated mean of my independent variable.
$endgroup$
– Lexie Walker
2 days ago
$begingroup$
Try $texttt{mean(ult)}$. This command should work.
$endgroup$
– callculus
2 days ago
add a comment |
$begingroup$
If I have the simple linear model: $y=alpha+beta x+epsilon$, and I know that
$hat{alpha}= hat{overline{y}}-hat{beta}hat{overline{x}}$.
Then my estimate og the mean is actually: $hat{overline{x}}=frac{hat{overline{y}}-hat{alpha}}{hat{beta}}$
I'm trying to do this in R, with the lm(), but I don't know if this is correct.
Call:
lm(formula = ult ~ vale)
Residuals:
Min 1Q Median 3Q Max
-278701 -34922 -13235 21227 940124
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.834e+04 7.337e+02 65.89 <2e-16 ***
vale 2.307e-04 5.874e-06 39.27 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 64480 on 13065 degrees of freedom
Multiple R-squared: 0.1056, Adjusted R-squared: 0.1055
F-statistic: 1542 on 1 and 13065 DF, p-value: < 2.2e-16
statistics regression math-software
edited 2 days ago
callculus
18.3k31427
asked 2 days ago
Lexie WalkerLexie Walker
1597
$endgroup$
If I have the simple linear model: $y=alpha+beta x+epsilon$, and I know that
$hat{alpha}= hat{overline{y}}-hat{beta}hat{overline{x}}$.
Then my estimate og the mean is actually: $hat{overline{x}}=frac{hat{overline{y}}-hat{alpha}}{hat{beta}}$
I'm trying to do this in R, with the lm(), but I don't know if this is correct.
Call:
lm(formula = ult ~ vale)
Residuals:
Min 1Q Median 3Q Max
-278701 -34922 -13235 21227 940124
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.834e+04 7.337e+02 65.89 <2e-16 ***
vale 2.307e-04 5.874e-06 39.27 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 64480 on 13065 degrees of freedom
Multiple R-squared: 0.1056, Adjusted R-squared: 0.1055
F-statistic: 1542 on 1 and 13065 DF, p-value: < 2.2e-16
statistics regression math-software
statistics regression math-software
edited 2 days ago
callculus
18.3k31427
asked 2 days ago
Lexie WalkerLexie Walker
1597
edited 2 days ago
callculus
18.3k31427
asked 2 days ago
Lexie WalkerLexie Walker
1597
edited 2 days ago
callculus
18.3k31427
edited 2 days ago
callculus
18.3k31427
edited 2 days ago
callculus
18.3k31427
18.3k31427
asked 2 days ago
Lexie WalkerLexie Walker
1597
asked 2 days ago
Lexie WalkerLexie Walker
1597
asked 2 days ago
Lexie WalkerLexie Walker
1597
1597
$begingroup$
The lm()-function seems correct for linear models.
$endgroup$
– callculus
2 days ago
$begingroup$
@callculus, I know but the question is with that summary how do i get the estimated mean of my independent variable.
$endgroup$
– Lexie Walker
2 days ago
$begingroup$
Try $texttt{mean(ult)}$. This command should work.
$endgroup$
– callculus
2 days ago
add a comment |
$begingroup$
The lm()-function seems correct for linear models.
$endgroup$
– callculus
2 days ago
$begingroup$
@callculus, I know but the question is with that summary how do i get the estimated mean of my independent variable.
$endgroup$
– Lexie Walker
2 days ago
$begingroup$
Try $texttt{mean(ult)}$. This command should work.
$endgroup$
– callculus
2 days ago
$begingroup$
The lm()-function seems correct for linear models.
$endgroup$
– callculus
2 days ago
$begingroup$
The lm()-function seems correct for linear models.
$endgroup$
– callculus
2 days ago
$begingroup$
@callculus, I know but the question is with that summary how do i get the estimated mean of my independent variable.
$endgroup$
– Lexie Walker
2 days ago
$begingroup$
@callculus, I know but the question is with that summary how do i get the estimated mean of my independent variable.
$endgroup$
– Lexie Walker
2 days ago
$begingroup$
Try $texttt{mean(ult)}$. This command should work.
$endgroup$
– callculus
2 days ago
$begingroup$
Try $texttt{mean(ult)}$. This command should work.
$endgroup$
– callculus
2 days ago
add a comment |
1 Answer
1
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oldest
votes
$begingroup$
( mean(fitted(lm(formula = ult ~ vale))) - coef( lm(formula = ult ~ vale) )[1] ) / coef( lm(formula = ult ~ vale) )[2]
answered 2 days ago
V. VancakV. Vancak
11.3k3926
$endgroup$
add a comment |
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1 Answer
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$begingroup$
( mean(fitted(lm(formula = ult ~ vale))) - coef( lm(formula = ult ~ vale) )[1] ) / coef( lm(formula = ult ~ vale) )[2]
answered 2 days ago
V. VancakV. Vancak
11.3k3926
$endgroup$
add a comment |
$begingroup$
( mean(fitted(lm(formula = ult ~ vale))) - coef( lm(formula = ult ~ vale) )[1] ) / coef( lm(formula = ult ~ vale) )[2]
answered 2 days ago
V. VancakV. Vancak
11.3k3926
$endgroup$
add a comment |
$begingroup$
( mean(fitted(lm(formula = ult ~ vale))) - coef( lm(formula = ult ~ vale) )[1] ) / coef( lm(formula = ult ~ vale) )[2]
answered 2 days ago
V. VancakV. Vancak
11.3k3926
$endgroup$
( mean(fitted(lm(formula = ult ~ vale))) - coef( lm(formula = ult ~ vale) )[1] ) / coef( lm(formula = ult ~ vale) )[2]
answered 2 days ago
V. VancakV. Vancak
11.3k3926
answered 2 days ago
V. VancakV. Vancak
11.3k3926
answered 2 days ago
V. VancakV. Vancak
11.3k3926
answered 2 days ago
V. VancakV. Vancak
11.3k3926
11.3k3926
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$begingroup$
The lm()-function seems correct for linear models.
$endgroup$
– callculus
2 days ago
$begingroup$
@callculus, I know but the question is with that summary how do i get the estimated mean of my independent variable.
$endgroup$
– Lexie Walker
2 days ago
$begingroup$
Try $texttt{mean(ult)}$. This command should work.
$endgroup$
– callculus
2 days ago