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I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 x4 x5 y are the original data, X1 X2 X3 X4 X5 Y are standardized variables with 0 mean and 1 standard deviation. I have missing data on the x4 and y .

sum x1 x2 x3 x4 x5 y

Variable Obs    Mean    Std. Dev.   Min   Max

x1      146 .0584795    .0801943     0      .401
x2      146 .1780685    .1500611    .026    1
x3      146 .8747945    .1389587    .309    1
x4      123 14.24472    2.007943    11.1    22.2
x5      146 .5480685    .3825432    .005    1
y       140 2.991429    .8211665    1.3     4.9


sum X1 X2 X3 X4 X5 Y

Variable Obs     Mean   Std. Dev.  Min       Max

X1      146    1.99e-08     1   -.7292224   4.271135
X2      146    2.50e-08     1   -1.013377   5.477312
X3      146   -1.70e-07     1   -4.071673   .9010261
X4      123   -1.22e-07     1   -1.566137   3.961907
X5      146    7.00e-08     1   -1.419626   1.181387
Y       140   -4.35e-08     1   -2.059788   2.32422


I thought regression (2) will give me same results as regression (3) since Stata manual says:

The results(with actual data) are as follows:

             (2)          (3)         (4)
ELLx1       -0.1964757   -0.2524519  -0.1964757
SPEDx2       -0.1864261   -0.4689916  -0.1864261
FRLx3        0.0219722    0.020811    0.0219722
EXPLORE9x4        0.5246134    0.5251326   0.5246134
AFAMx5       -0.3346567   -0.3290855  -0.3346567


I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 are the original data, X1 X2 X3 are standardized variables with 0 mean and 1 standard deviation. I thought regression (2) will give me same results as regression (3) since Stata manual says:

The results(with actual data) are as follows:

             (2)          (3)         (4)
ELL      -0.1964757   -0.2524519  -0.1964757
SPED     -0.1864261   -0.4689916  -0.1864261
FRL       0.0219722    0.020811    0.0219722
EXPLORE9  0.5246134    0.5251326   0.5246134
AFAM     -0.3346567   -0.3290855  -0.3346567


I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 x4 x5 y are the original data, X1 X2 X3 X4 X5 Y are standardized variables with 0 mean and 1 standard deviation. I have missing data on the x4 and y .

sum x1 x2 x3 x4 x5 y

Variable Obs    Mean    Std. Dev.   Min   Max

x1      146 .0584795    .0801943     0      .401
x2      146 .1780685    .1500611    .026    1
x3      146 .8747945    .1389587    .309    1
x4      123 14.24472    2.007943    11.1    22.2
x5      146 .5480685    .3825432    .005    1
y       140 2.991429    .8211665    1.3     4.9


sum X1 X2 X3 X4 X5 Y

Variable Obs     Mean   Std. Dev.  Min       Max

X1      146    1.99e-08     1   -.7292224   4.271135
X2      146    2.50e-08     1   -1.013377   5.477312
X3      146   -1.70e-07     1   -4.071673   .9010261
X4      123   -1.22e-07     1   -1.566137   3.961907
X5      146    7.00e-08     1   -1.419626   1.181387
Y       140   -4.35e-08     1   -2.059788   2.32422


I thought regression (2) will give me same results as regression (3) since Stata manual says:

The results are as follows:

             (2)          (3)         (4)
x1       -0.1964757   -0.2524519  -0.1964757
x2       -0.1864261   -0.4689916  -0.1864261
x3        0.0219722    0.020811    0.0219722
x4        0.5246134    0.5251326   0.5246134
x5       -0.3346567   -0.3290855  -0.3346567

2 added 327 characters in body

I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 are the original data, X1 X2 X3 are standardized variables with 0 mean and 1 standard deviation. I thought regression (2) will give me same results as regression (3) since Stata manual says:

The beta coefficients are the regression coefficients obtained by first standardizing all variables to have a mean of 0 and a standard deviation of 1.

reg y x1 x2 x3 (1) reg y x1 x2 x3, beta (2) reg Y X1 X2 X3 (3) reg Y X1 X2 X3, beta (4)

The results(with actual data) are as follows:

             (2)          (3)         (4)
ELL      -0.1964757   -0.2524519  -0.1964757
SPED     -0.1864261   -0.4689916  -0.1864261
FRL       0.0219722    0.020811    0.0219722
EXPLORE9  0.5246134    0.5251326   0.5246134
AFAM     -0.3346567   -0.3290855  -0.3346567


And it turns out the regression (4) gives very similarsame results to (2), but all the variables in regression 4 are already standardized. My guess is that since the variables are all proportions, they are not normally distributed and vary in range.For For instance x1 is between 0-0.4 while x2 is binomial distribution between 0-0.99. Would that be a problem? Should I choose (2) over (3) and why?

I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 are the original data, X1 X2 X3 are standardized variables with 0 mean and 1 standard deviation. I thought regression (2) will give me same results as regression (3) since Stata manual says:

The beta coefficients are the regression coefficients obtained by first standardizing all variables to have a mean of 0 and a standard deviation of 1.

reg y x1 x2 x3 (1) reg y x1 x2 x3, beta (2) reg Y X1 X2 X3 (3) reg Y X1 X2 X3, beta (4)

And it turns out the regression (4) gives very similar results to (2), but all the variables in regression 4 are already standardized. My guess is that since the variables are all proportions, they are not normally distributed and vary in range.For instance x1 is between 0-0.4 while x2 is binomial distribution between 0-0.99. Would that be a problem? Should I choose (2) over (3) and why?

I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 are the original data, X1 X2 X3 are standardized variables with 0 mean and 1 standard deviation. I thought regression (2) will give me same results as regression (3) since Stata manual says:

The beta coefficients are the regression coefficients obtained by first standardizing all variables to have a mean of 0 and a standard deviation of 1.

reg y x1 x2 x3 (1) reg y x1 x2 x3, beta (2) reg Y X1 X2 X3 (3) reg Y X1 X2 X3, beta (4)

The results(with actual data) are as follows:

             (2)          (3)         (4)
ELL      -0.1964757   -0.2524519  -0.1964757
SPED     -0.1864261   -0.4689916  -0.1864261
FRL       0.0219722    0.020811    0.0219722
EXPLORE9  0.5246134    0.5251326   0.5246134
AFAM     -0.3346567   -0.3290855  -0.3346567


And it turns out the regression (4) gives same results to (2), but all the variables in regression 4 are already standardized. My guess is that since the variables are all proportions, they are not normally distributed and vary in range. For instance x1 is between 0-0.4 while x2 is binomial distribution between 0-0.99. Would that be a problem? Should I choose (2) over (3) and why?

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# Standardized beta on standardized variables

I am having trouble understanding why the standardized beta option in regress in Stata gives me different results when I manually standardize all variables. Say x1 x2 x3 are the original data, X1 X2 X3 are standardized variables with 0 mean and 1 standard deviation. I thought regression (2) will give me same results as regression (3) since Stata manual says:

The beta coefficients are the regression coefficients obtained by first standardizing all variables to have a mean of 0 and a standard deviation of 1.

reg y x1 x2 x3 (1) reg y x1 x2 x3, beta (2) reg Y X1 X2 X3 (3) reg Y X1 X2 X3, beta (4)

And it turns out the regression (4) gives very similar results to (2), but all the variables in regression 4 are already standardized. My guess is that since the variables are all proportions, they are not normally distributed and vary in range.For instance x1 is between 0-0.4 while x2 is binomial distribution between 0-0.99. Would that be a problem? Should I choose (2) over (3) and why?