{"id":50801,"date":"2018-02-20T23:50:30","date_gmt":"2018-02-20T23:50:30","guid":{"rendered":"https:\/\/writemyessayfree.com\/regression-analysis-3"},"modified":"2017-08-16T12:30:54","modified_gmt":"2017-08-16T12:30:54","slug":"regression-analysis-3","status":"publish","type":"post","link":"https:\/\/www.benedictsol.com\/blogs\/regression-analysis-3\/","title":{"rendered":"Regression Analysis"},"content":{"rendered":"

QUESTION 1<\/b><\/p>\n

    \n
  1. \n

    Please look at the following output from the regression. <\/p>\n

    What does the value of R-square tell us about our model? <\/b><\/p>\n

    Note: It is not sufficient to just provide some general answers. Use the numbers from the output, and write your answers specific to our regression model.<\/b><\/p>\n

    Model Summary<\/b><\/p>\n

    Model<\/p>\n

    R<\/p>\n

    R Square<\/p>\n

    Adjusted R Square<\/p>\n

    Std. Error of the Estimate<\/p>\n

    1<\/p>\n

    .771a<\/p>\n

    .594<\/p>\n

    .580<\/p>\n

    21.741<\/p>\n

    a. Predictors: (Constant), DENSITY<\/p>\n

         — Font family –Andale MonoArialArial BlackBook AntiquaComic Sans MSCourier NewGeorgiaHelveticaImpactSymbolTahomaTerminalTimes New RomanTrebuchet MSVerdanaWebdingsWingdings — Font size –1 (8pt)2 (10pt)3 (12pt)4 (14pt)5 (18pt)6 (24pt)7 (36pt)                      — Format –HeadingSub Heading 1Sub Heading 2ParagraphFormatted Code — Font family — — Font size —                                                 \"mashuplogo.png\"                             <\/p>\n

    Path: p<\/p>\n

    Words:0<\/p>\n<\/li>\n<\/ol>\n

    12.5 points   <\/p>\n

    QUESTION 2<\/b><\/p>\n

      \n
    1. \n

      Please look at the following output from the regression. <\/p>\n

      What do the value of F-test and its P-value tell us about our model? <\/b><\/p>\n

      Note: It is not sufficient to just provide some general answers. Use the numbers from the output, and write your answers specific to our regression model.<\/b><\/p>\n

      ANOVAa<\/b><\/p>\n

      Model<\/p>\n

      Sum of Squares<\/p>\n

      df<\/p>\n

      Mean Square<\/p>\n

      F<\/p>\n

      Sig.<\/p>\n

      1<\/p>\n

      Regression<\/p>\n

      20747.246<\/p>\n

      1<\/p>\n

      20747.246<\/p>\n

      43.895<\/p>\n

      .000b<\/p>\n

      Residual<\/p>\n

      14179.629<\/p>\n

      30<\/p>\n

      472.654<\/p>\n

      Total<\/p>\n

      34926.875<\/p>\n

      31<\/p>\n

      a. Dependent Variable: SALES<\/p>\n

      b. Predictors: (Constant), DENSITY<\/p>\n

           — Font family –Andale MonoArialArial BlackBook AntiquaComic Sans MSCourier NewGeorgiaHelveticaImpactSymbolTahomaTerminalTimes New RomanTrebuchet MSVerdanaWebdingsWingdings — Font size –1 (8pt)2 (10pt)3 (12pt)4 (14pt)5 (18pt)6 (24pt)7 (36pt)                      — Format –HeadingSub Heading 1Sub Heading 2ParagraphFormatted Code — Font family — — Font size —                                                 \"mashuplogo.png\"                             <\/p>\n

      Path: p<\/p>\n

      Words:0<\/p>\n<\/li>\n<\/ol>\n

      12.5 points   <\/p>\n

      QUESTION 3<\/b><\/p>\n

        \n
      1. \n

        The regression coefficient output is shown below. <\/p>\n

        Does Density matter in terms of explaining sales? Can you provide an explanation of the coefficient estimate for Density?<\/b> (Note: the unit of Density is number of homes per acre, and the unit of Sales is dollars per thousand homes ). <\/p>\n

        Coefficientsa<\/b><\/p>\n

        Model<\/p>\n

        Unstandardized Coefficients<\/p>\n

        Standardized Coefficients<\/p>\n

        t<\/p>\n

        Sig.<\/p>\n

        B<\/p>\n

        Std. Error<\/p>\n

        Beta<\/p>\n

        1<\/p>\n

        (Constant)<\/p>\n

        141.525<\/p>\n

        9.109<\/p>\n

        15.538<\/p>\n

        .000<\/p>\n

        DENSITY<\/p>\n

        -12.893<\/p>\n

        1.946<\/p>\n

        -.771<\/p>\n

        -6.625<\/p>\n

        .000<\/p>\n

        a. Dependent Variable: SALES<\/p>\n

        For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).     — Font family –Andale MonoArialArial BlackBook AntiquaComic Sans MSCourier NewGeorgiaHelveticaImpactSymbolTahomaTerminalTimes New RomanTrebuchet MSVerdanaWebdingsWingdings — Font size –1 (8pt)2 (10pt)3 (12pt)4 (14pt)5 (18pt)6 (24pt)7 (36pt)                      — Format –HeadingSub Heading 1Sub Heading 2ParagraphFormatted Code — Font family — — Font size —                                                 \"mashuplogo.png\"                             <\/p>\n

        5<\/p>\n

        Path: p<\/p>\n

        Words:28<\/p>\n<\/li>\n<\/ol>\n

        12.5 points   <\/p>\n

        QUESTION 4<\/b><\/p>\n

          \n
        1. \n

          Managers suspect that the effect of Density on Sales can be nonlinear; in other words, as density increases, there will a decreasing marginal effect on density. To test this idea, they ran an additional regression, with Density and Density_Squared (i.e. Density*Density) as the independent variables (again, Sales as the dependant variable), and the output of the new regression shows below.<\/p>\n

          Can you explain what the R_square and F-test tell us about the new model? Is the new model better than the model with only Density as the independent variable?<\/b><\/p>\n

          Model Summary<\/b><\/p>\n

          Model<\/p>\n

          R<\/p>\n

          R Square<\/p>\n

          Adjusted R Square<\/p>\n

          Std. Error of the Estimate<\/p>\n

          1<\/p>\n

          .910a<\/p>\n

          .829<\/p>\n

          .817<\/p>\n

          14.354<\/p>\n

          a. Predictors: (Constant), Density2, DENSITY<\/p>\n

          ANOVAa<\/b><\/p>\n

          Model<\/p>\n

          Sum of Squares<\/p>\n

          df<\/p>\n

          Mean Square<\/p>\n

          F<\/p>\n

          Sig.<\/p>\n

          1<\/p>\n

          Regression<\/p>\n

          28951.384<\/p>\n

          2<\/p>\n

          14475.692<\/p>\n

          70.253<\/p>\n

          .000b<\/p>\n

          Residual<\/p>\n

          5975.491<\/p>\n

          29<\/p>\n

          206.051<\/p>\n

          Total<\/p>\n

          34926.875<\/p>\n

          31<\/p>\n

          a. Dependent Variable: SALES<\/p>\n

          b. Predictors: (Constant), Density2, DENSITY<\/p>\n

          Coefficientsa<\/b><\/p>\n

          Model<\/p>\n

          Unstandardized Coefficients<\/p>\n

          Standardized Coefficients<\/p>\n

          t<\/p>\n

          Sig.<\/p>\n

          B<\/p>\n

          Std. Error<\/p>\n

          Beta<\/p>\n

          1<\/p>\n

          (Constant)<\/p>\n

          212.595<\/p>\n

          12.768<\/p>\n

          16.650<\/p>\n

          .000<\/p>\n

          DENSITY<\/p>\n

          -47.293<\/p>\n

          5.601<\/p>\n

          -2.827<\/p>\n

          -8.444<\/p>\n

          .000<\/p>\n

          Density2<\/p>\n

          3.419<\/p>\n

          .542<\/p>\n

          2.113<\/p>\n

          6.310<\/p>\n

          .000<\/p>\n

          a. Dependent Variable: SALES<\/p>\n

               — Font family –Andale MonoArialArial BlackBook AntiquaComic Sans MSCourier NewGeorgiaHelveticaImpactSymbolTahomaTerminalTimes New RomanTrebuchet MSVerdanaWebdingsWingdings — Font size –1 (8pt)2 (10pt)3 (12pt)4 (14pt)5 (18pt)6 (24pt)7 (36pt)                      — Format –HeadingSub Heading 1Sub Heading 2ParagraphFormatted Code — Font family — — Font size —                                                 \"mashuplogo.png\"                             <\/p>\n

          Path: p<\/p>\n

          Words:0<\/p>\n<\/li>\n<\/ol>\n

          12.5 points   <\/p>\n

          QUESTION 5<\/b><\/p>\n

            \n
          1. \n

            (This is a Bonus Question)<\/p>\n

            Continuing from Question 4, can you explain the meaning of the coefficient estimate of Density_Squared (i.e. Density*Density)?<\/b> (Hint: the effect of Density on Sales is negative, while the effect of Density_Squared on Sales is positive). <\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

            QUESTION 1 Please look at the following output from the regression.  What does the value of R-square tell us about our model?  Note: It is not sufficient to just provide some general answers. Use the numbers from the output, and write your answers specific to Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-50801","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/50801","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/comments?post=50801"}],"version-history":[{"count":0,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/50801\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/media?parent=50801"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/categories?post=50801"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/tags?post=50801"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}