Heri
10 months & 6 days ago

I want to know why you choose to utilize unstandardized beta rather than standardized beta in order to achieve this good outcome.

Murekatete zawadi
1 year & 2 months ago

thank you very much!
it was very clear and helpful

Riya Jain
1 year & 2 months ago

Thanks Murekatete for appreciation

Reply

M Naveed
1 year & 2 months ago

Please send me details about regression analysis

Suad
1 year & 2 months ago

Thank you for replying. I have two separate data sets. For the first data set the model summary results are: R= .752, R Square=.566, Adjusted R Square=.555 and Std. Error of the Estimate= 9.640.
For the second data set the results are: R= .797, R Square=.635, Adjusted R Square=.623 and Std. Error of the Estimate=8.880.

For both models, I used the same three predictors for one dependent variable (Course Score).

And I have one more question, Should I report the results of checking the linear regression assumptions before start presenting the regression results?

Thank you.

Reply

Riya Jain
1 year & 2 months ago

The 2nd model is more appropriate as the value Adjusted R square is more there and even standard error is less. Further, assumption testings you can do. But majorly its practiced for economic data.

Reply

Suad
1 year & 3 months ago

Hi, I found this article very helpful. I just have one question about the Model Summary table. What is the acceptable value for “Std. Error of the Estimate”? I am doing research on the factors that influence students’ scores in one subject using three different tests as dependent variables. Thank you in advance. The value I have now is 9.640, is this considered acceptable?

Riya Jain
1 year & 2 months ago

Hi Suad,
There is no specific acceptable value for the standard error of the estimate. Generally the values for significance level, R square or adjusted R square are checked

Reply

Vivian
1 year & 3 months ago

Thank you. This is very helpful

Dilshod
1 year & 3 months ago

Thank you. it was very helpful. it was very clear.

Tumabang Emmanuel
1 year & 4 months ago

Thanks for this document.
It is simple and clear. it has been very helpful to me. Now I know what to do with my analysis

Antony gitonga
1 year & 1 month ago

Very satisfying

Reply

Wail
1 year & 5 months ago

Varibles entered / removed
Model Varibles entered Varibles
Removed method
1 Event,
Allowance . Enter
a. All requested variables enterd dependent variable: motivation level
b. Depent variable:mdv

interpret ALL the SPSS outputs below as detail as possible Regression Analysis

Model summary
Model r R Square Adjusted R
Square Std Error of the Estimate
1 .669
Allowance ..447 .440 .39184
a. Predictors ( constant, events,allowance

Asaph
1 year & 5 months ago

Great post. Thank you

Shem Ngetha
1 year & 8 months ago

Source | SS df MS Number of obs = 13
————-+———————————- F(3, 9) = 0.62
Model | 2.47305128 3 .824350427 Prob > F = 0.6188
Residual | 11.9433999 9 1.32704444 R-squared = 0.1715
————-+———————————- Adj R-squared = -0.1046
Total | 14.4164512 12 1.20137094 Root MSE = 1.152

———————————————————————————-
Dperformance | Coef. Std. Err. t P>|t| [95% Conf. Interval]
—————–+—————————————————————-
Dcapitaladequacy | .0011134 .0048725 0.23 0.824 -.0099089 .0121357
Ddeposits | -.0143361 .0113395 -1.26 0.238 -.0399878 .0113155
Dquality | .0224043 .0182389 1.23 0.250 -.0188548 .0636635

soemone help me interpret research variables