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Week 5 Regression Team Paper (Baseball)

by: smartwriter Notetaker

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Week 5 Regression Team Paper (Baseball)

Marketplace > Week 5 Regression Team Paper Baseball
smartwriter Notetaker
CSU - Dominguez hills
GPA 3.0

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Week 5 Regression Team Paper (Baseball)
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This 6 page Study Guide was uploaded by smartwriter Notetaker on Monday November 16, 2015. The Study Guide belongs to a course at a university taught by a professor in Fall. Since its upload, it has received 12 views.

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Date Created: 11/16/15
Team [INSERT HERE] will conduct a hypothesis test to test whether there is a  significant relationship between the wins of a baseball team (regardless of league) and the  attendance of the team. Team [INSERT HERE] will use a regression hypothesis test for  significance between two variables. Step 1: State the hypothesis Null hypothesis:  Alternative hypothesis: Step 2: Decision rule  A t­distribution will be used for this test. Reject the null hypothesis if the test statistic exceeds 2.048 or is less than 2.048; reject the null  hypothesis if the p­value is less than 0.05, otherwise fail to reject. Step 3: State name of test and test statistic The test team {} will use is the hypothesis test for the correlation coefficient. The test statistic is  equal to: Or Step 4: Calculate the test statistic Wins Attendance 95.0 2,847,798 95.0 4,090,440 88.0 2,108,818 74.0 2,623,904 95.0 3,404,636 93.0 2,014,220 99.0 2,342,804 80.0 2,014,995 83.0 2,034,243 67.0 1,141,915 79.0 2,525,259 71.0 2,024,505 69.0 2,724,859 56.0 1,371,181 90.0 2,520,904 77.0 2,059,327 89.0 2,805,060 73.0 1,923,254 83.0 2,827,549 67.0 1,817,245 71.0 3,603,680 82.0 2,869,787 81.0 2,730,352 75.0 3,181,020 100.0 3,542,271 83.0 1,852,608 88.0 2,665,304 81.0 2,211,323 79.0 3,100,092 67.0 1,914,385 R^2 R Sample  Size 0.258358 0.508289 30               Regression Analysis r²  0.258  Adjusted r²  0.232  r   0.508  Std. Error   589731.685  n   30  k   1  Dep. Var.  Attendance ANOVA  table Source SS   df   MS F p­value Regression  3,392,298,543,204.3300  1    3,392,298,543,204.3300 9.75 .0041 Residual  9,737,936,881,109.5400  28    347,783,460,039.6260   13,130,235,424,313.900 Total 0  29          Regression output confidence interval variables  coefficients std. error     t (df=28) p­value 95% lower ­ Intercept ­60,580.6201  825,786.8646  ­0.073  .9420 1,752,128.3312 1,630,967.0909 Wins 31,568.3772  10,107.8709   3.123  .0041 10,863.3423  52,273.4121  Studentized Studentized Leverag Observation Attendance Predicted   Residual e Residual 1 2,847,798.0  2,938,415.2  ­90,617.2  0.091 ­0.161 2 4,090,440.0  2,938,415.2  1,152,024.8  0.091 2.049 3 2,108,818.0  2,717,436.6  ­608,618.6  0.048 ­1.058 4 2,623,904.0  2,275,479.3  348,424.7  0.048 0.605 5 3,404,636.0  2,938,415.2  466,220.8  0.091 0.829 6 2,014,220.0  2,875,278.5  ­861,058.5  0.076 ­1.519 7 2,342,804.0  3,064,688.7  ­721,884.7  0.129 ­1.311 8 2,014,995.0  2,464,889.6  ­449,894.6  0.034 ­0.776 9 2,034,243.0  2,559,594.7  ­525,351.7  0.035 ­0.907 10 1,141,915.0  2,054,500.7  ­912,585.7  0.091 ­1.623 11 2,525,259.0  2,433,321.2  91,937.8  0.035 0.159 12 2,024,505.0  2,180,774.2  ­156,269.2  0.063 ­0.274 13 2,724,859.0  2,117,637.4  607,221.6  0.076 1.071 14 1,371,181.0  1,707,248.5  ­336,067.5  0.217 ­0.644 15 2,520,904.0  2,780,573.3  ­259,669.3  0.057 ­0.453 16 2,059,327.0  2,370,184.4  ­310,857.4  0.038 ­0.537 17 2,805,060.0  2,749,005.0  56,055.0  0.052 0.098 18 1,923,254.0  2,243,910.9  ­320,656.9  0.052 ­0.558 19 2,827,549.0  2,559,594.7  267,954.3  0.035 0.462 20 1,817,245.0  2,054,500.7  ­237,255.7  0.091 ­0.422 21 3,603,680.0  2,180,774.2  1,422,905.8  0.063 2.492 22 2,869,787.0  2,528,026.3  341,760.7  0.034 0.590 23 2,730,352.0  2,496,457.9  233,894.1  0.033 0.403 24 3,181,020.0  2,307,047.7  873,972.3  0.044 1.516 25 3,542,271.0  3,096,257.1  446,013.9  0.139 0.815 26 1,852,608.0  2,559,594.7  ­706,986.7  0.035 ­1.220 27 2,665,304.0  2,717,436.6  ­52,132.6  0.048 ­0.091 28 2,211,323.0  2,496,457.9  ­285,134.9  0.033 ­0.492 29 3,100,092.0  2,433,321.2  666,770.8  0.035 1.151 30 1,914,385.0  2,054,500.7  ­140,115.7  0.091 ­0.249 Durbin­Watson =1.94 Regression output variables  coefficients std. error     t (df=28) p­value Intercept ­60,580.6201  825,786.8646  ­0.073  .9420 Wins 31,568.3772  10,107.8709   3.123  .0041 This table shows the test statistic which is bold in RED. Step 5: Use the decision rule and communicate results Reject the null hypothesis if the test statistic exceeds 2.048 or is less than 2.048; reject the null  hypothesis if the p­value is less than 0.05, otherwise fail to reject. Since the test statistic exceeds one of the critical values and the p­value is less than 0.05, team  {INSERT HERE} can reject the null hypothesis. Since the null hypothesis is rejected, it can be concluded that the wins of a team are related to the attendance.

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