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Error output with multiple regression?

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Error Output With Multiple Regression?

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Sad Hi,
I'm new to multiple regression I try it out today with some data and the out was

SUMMARY OUTPUT
Regression Statistics
Multiple R 1
R Square 1
Adjusted R Square 65535
Standard Error 0
Observations 3

ANOVA
df SS MS F Significance F Regression 11 0.19201459 0.017455872 #NUM! #NUM!
 BenWTS
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Shocked
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The results that you have posted come from the regression analysis of the Analysis Toolpak add-in for Excel. 

I suggest that download the multiple regression and analysis template from this site here, and you will be able to intepret the results and also forecast with a user friendly interface.
 Excel Business Forums Administrator
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Confused
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As per your suggestion i downloaded the software and tried to carry out the regression i got run time error 1004 (unable to get TDist property of the worksheetFunction class  )
so can uyou plz help me out for the following.
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Oops
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Which version of Excel are you running?  There maybe an issue with the TDist function in certain versions.
 Excel Business Forums Administrator
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Confused
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We can define a test problem that we can use to demonstrate the different modeling strategies.
We will use the make_regression() function to create a test dataset for multiple-output regression. We will generate 1,000 examples with 10 input features, five of which will be redundant and five that will be informative. The problem will require the prediction of two numeric values.
Problem Input: 10 numeric variables.Problem Output: 2 numeric variables.The example below generates the dataset and summarizes the shape.
# example of multioutput regression test problemfrom sklearn.datasets import make_regression# create datasetsX, y = make_regression(n_samples=1000, n_features=10, n_informative=5, n_targets=2, random_state=1, noise=0.5)# summarize datasetprint(X.shape, y.shape)Running the example creates the dataset and summarizes the shape of the input and output elements of the dataset for modeling, confirming the chosen configuration.
(1000, 10) (1000, 2)Next, let’s look at modeling this problem directly.
Regards,Rachel Gomez
 rachel
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