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This is a Multiple Regression Analysis Project – There Are 3 X variables and 1 Y variable …

          This is a Multiple Regression Analysis Project – There Are  3 X variables and 1 Y variable:     X1: # of   Players Who Were “All-Stars” During A Given Year On The Detriot   Pistons     X2: Points   Per Game (In Regular Season) Each Season      X3: (Dummy   Variable) Whether The Detroit Pistons Made The Playoffs The Previous Season   (Dummy Variable Because It Is Either A Yes Or No Statistic.)   Y: Regular   Season Winning Percentage               – The goal   is to analyze the regression model, prepare a write-up to explain the idea   and the results. THE GOAL: Create a Multiple Regression   Model to help a Company, Government, or in our case, a Sports   Organization understand somthing more about their data.           As Seen In   The Table Below: The  DETROIT PISTONS  (A National   Basketball Association Team/Organization) STATISTICS FOR THE GIVEN VARIABLES   FOR EACH SEASON FROM 1984 – 2016:   Year All Stars (X1) Points Per Game (X2) Playoffs Previous Season (X3) Win Percentage (Y)    2016 1 102 0 0.537    2015 0 98.5 0 0.39    2014 0 101 0 0.345    2013 0 94.9 0 0.345    2012 0 73.1 0 0.379    2011 0 97 0 0.366    2010 0 94 1 0.329    2009 1 94.2 1 0.476    2008 3 97.5 1 0.72    2007 2 96 1 0.646    2006 4 96.8 1 0.78    2005 1 93.3 1 0.659    2004 1 90.1 1 0.659    2003 1 91.4 1 0.61    2002 0 94.3 0 0.61    2001 1 95.6 1 0.39    2000 2 103.5 1 0.512    1999 0 55.1 0 0.58    1998 1 94.2 1 0.451    1997 2 94.2 1 0.659    1996 1 95.4 0 0.561    1995 2 98.2 0 0.341    1994 0 96.9 0 0.244    1993 2 100.6 1 0.488    1992 3 98.9 1 0.585    1991 2 100.1 1 0.61    1990 2 104.3 1 0.72    1989 1 106.6 1 0.768    1988 1 109.2 1 0.659    1987 2 111.2 1 0.634    1986 1 114.2 1 0.561    1985 2 116 1 0.561            With The   Given Table Filled With Statistics of The Last 30-32 Seasons Of The Detroit   Pistons, Answer The Following:            1) Include   the three tables from the Excel regression including adjusted R2, R2,   standard error for the model, ANOVA results, as well as the variable names,   estimated intercept, estimated coefficients, t-test statistics, p-values, and   95% confidence intervals. (No more than four decimal places should be   displayed.)    
     2) Write out   the regression model using the variable names.      Y = 0.5633 + 0.0602 * X1 –   0.0017 * X2 + 0.1035 * X3   Win Percentage = 0.5633 +   0.0602 * All Stars – 0.0017 * Points Per Game + 0.1035 * Playoffs Previous   Season      3) Create a scatter diagram for each   independent variable (except the dummy variable) and the dependent variable.   In other words, plot X1 with Y on a scatter diagram. On a second graph, plot   X2 with Y. If you have more independent variables, also plot them   individually with Y. Write about what you observe in these scatter plots.                                                      Both scatter plots show that   the points are randomly distributed. This may indicate a low degree of   correlation between X1 and Y, and X2 and Y.      4) Comment on the strength of the   model using adjusted R2 and F-test (use the p-value approach) to assess the   fit. Show how you would calculate R2 and F if they weren’t provided in the   tables.