![]() ![]() \r2\) Notic\e in the output above, the t-statistic for this t-test is given in the row for newspaper copies and under T it is rounded to two decimal places in the output. As has been mentioned several times, when performing a t-test in regression, the degrees of freedom is ALWAYS DFE. \rSome notes: \r1\) the degrees of freedom for the t-test is DFE = n v 1 = 6. Such a model would be considered our “final model”. Whenever all explanatory variables in a model have p-values from the t-test less than 0.05 \(or so\), we\ stop the backwards selection process. ![]() Such a model would be considered our final model. Whenever all explanatory variables in a model have p-values from the t-test less than 0.05 \(or so\), we stop the backwards selection process. Note that B is not correct – “keeping the number of radios and TV sets the same” is used i\n the interpretation of the coefficient of newspaper copies and is different than the phrase “after accounting for the effects of the number of radios and number of TV sets in the country.”)/Rect/Subj(Revealed:)/Subtype/Text/T(The Answer Is)/Type/Annot>endobj665 0 objendobj666 0 obj/C/Contents(False. Note that B is not correct keeping the number of radios and TV sets the same is used in the interpretation of the coefficient of newspaper copies and is different than the phrase after accounting for the effects of the number of radios and number \of TV sets in the country.)/CreationDate(D:20091015144626-07'00')/F 28/M(D:20091015150302-07'00')/NM(fcd9ab47-f370-4756-8226-0f8a0633d196)/Name/Help/P 21 0 R/Popup 665 0 R/RC(Ĭ. )/Rect/Subj(Revealed:)/Subtype/Text/T(The Answer Is)/Type/Annot>endobj661 0 objendobj664 0 obj/C/Contents(C. But\, remember that we’ll stop eliminating variables once all remaining variables have p-values less than 0.05, which is the case here. A is not correct because it is possible that a backwards selection process will eliminate all variables. But, remember that well stop eliminating variables once all remaining variables have p-values less than 0.05, which is the case here. There are only 2 explanatory variables left in the model, so the degrees of freedom for the t-tests = 10 – 2 – 1 = 7. Right now we are driving each metric individually without accounting for their interactions and dependencies which seems inefficient for predicting potential performance.Recall, the degrees of freedom for the t-test is DFE = n – v – 1. How can I account for the relationship between these 3 unit type metrics and my output metric? I'd want to be able to say if X1 is 0.75 and X2 is 1.0 and X3 is 0.25 then my Y will be a specific value. Obviously, the more units sold per interaction (3 types of units) the more revenue would be earned per interaction. The input metrics are the number of sales per unit type out of 100 interactions. The output metric is the amount of revenue per interaction each person has (this can be negative if a customer cancels a sale within 90 days). Here is a sample of the data which is for 100 random sales personnel. ![]() I have some data which I want to run multiple regression on.ġ- is multiple regression the right analysis for this problemĢ- can someone guide me on how to do this in pandas or Minitab using the data set below ![]()
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