Using the major League Baseball Data set available through the "Data Sets" lin in the materials section, answer the research question of whether the number of wins of a team can be explained linearly by the size of stadium, ERA, and stolen bases. Prepare a 750-1,050 word paper describing the use of the multiple regression process to answer this research question. Be sure to include, in an appendix, your raw data table and the results of you computations in your paper, using both graphical and tabular methods of displaying dat and results.

I need a regression analysis for this.

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Team League Built Size Surface Salary Salary -mil Wins Attendance Batting ERA HR Error SB Year Average
Boston 1 1912 33871 0 123505125.0 123.5 95.0 2,847,798 0.281 4.74 199 109 45 1989 512,930
New York Yankees 1 1923 57746 0 208306817.0 208.3 95.0 4,090,440 0.276 4.52 229 95 84 1990 578,930
Oakland 1 1966 43662 0 55425762.0 55.4 88.0 2,108,818 0.262 3.69 155 88 31 1991 891,188
Baltimore 1 1992 48262 0 73914333.0 73.9 74.0 2,623,904 0.269 4.56 189 107 83 1992 1,084,408
Los Angles Angels 1 1966 45050 0 97725322.0 97.7 95.0 3,404,636 0.270 3.68 147 87 161 1993 1,120,254
Cleveland 1 1994 43368 0 41502500.0 41.5 93.0 2,014,220 0.271 3.61 207 106 62 1994 1,188,679
Chicago White Sox 1 1991 44321 0 75178000.0 75.2 99.0 2,342,804 0.262 3.61 200 94 137 1995 1,071,029
Toronto 1 1989 50516 1 45719500.0 45.7 80.0 2,014,995 0.265 4.06 136 95 72 1996 1,176,967
Minnesota 1 1982 48678 1 56186000.0 56.2 83.0 2,034,243 0.259 3.71 134 102 102 1997 1,383,578
Tampa Bay 1 1990 44027 1 29679067.0 29.7 67.0 1,141,915 0.274 5.39 157 124 151 1998 1,441,406
Texas 1 1994 52000 0 55849000.0 55.8 79.0 2,525,259 0.267 4.96 260 108 67 1999 1,720,050
Detroit 1 2000 40000 0 69092000.0 69.1 71.0 2,024,505 0.272 4.51 168 110 66 2000 1,988,034
Seattle 1 1999 45611 0 87754334.0 87.8 69.0 2,724,859 0.256 4.49 130 86 102 2001 2,264,403
Kansas City 1 1973 40529 0 36881000.0 36.9 56.0 1,371,181 0.263 5.49 126 125 53 2002 2,383,235
Atlanta 0 1993 50062 0 86457302.0 86.5 90.0 2,520,904 0.265 3.98 184 86 92 2003 2,555,476
Arizona 0 1998 49075 0 62329166.0 62.3 77.0 2,059,327 0.256 4.84 191 94 67 2004 2,486,609
Houston 0 2000 42000 0 76799000.0 76.8 89.0 2,805,060 0.256 3.51 161 89 115 2005 2,632,655
Cincinnati 0 2003 42,059 0 61892583.0 61.9 73.0 1,923,254 0.261 5.15 222 104 72
New York Mets 0 1964 55775 0 101305821.0 101.3 83.0 2,827,549 0.258 3.76 175 106 153
Pittsburgh 0 2001 38127 0 38133000.0 38.1 67.0 1,817,245 0.259 4.42 139 117 73
Los Angeles Dodgers 0 1962 56000 0 83039000.0 83.0 71.0 3,603,680 0.253 4.38 149 106 58
San Diego 0 2004 42,445 0 63290833.0 63.3 82.0 2,869,787 0.257 4.13 130 109 99
Washington 0 1961 56000 0 48581500.0 48.6 81.0 2,730,352 0.252 3.87 117 92 45
San Francisco 0 2000 40800 0 90199500.0 90.2 75.0 3,181,020 0.261 4.33 128 90 71
St Louis 0 1966 49625 0 92106833.0 92.1 100.0 3,542,271 0.270 3.49 170 100 83
Florida 0 1987 42531 0 60408834.0 60.4 83.0 1,852,608 0.272 4.16 128 103 96
Philadelphia 0 2004 43500 0 95522000.0 95.5 88.0 2,665,304 0.270 4.21 167 90 116
Milwaukee 0 2001 42400 0 39934833.0 39.9 81.0 2,211,323 0.259 3.97 175 119 79
Chicago Cubs 0 1914 38957 0 87032933.0 87.0 79.0 3,100,092 0.270 4.19 194 101 65
Colorado 0 1995 50381 0 48155000.0 48.2 67.0 1,914,385 0.267 5.13 150 118 65

To answer the research question of whether the number of wins of a team can be explained linearly by the size of the stadium, ERA, and stolen bases, we can use multiple regression analysis. This analysis will help us determine the relationship between these independent variables and the dependent variable (number of wins).

Here's how you can conduct the multiple regression analysis:

1. Import the dataset into a statistical analysis software or spreadsheet software like Excel. Make sure to include all the variables mentioned in the research question: size of stadium, ERA, stolen bases, and number of wins.

2. Clean the data and remove any unnecessary variables or rows. Ensure that the variables are in the correct format for analysis (numeric instead of text).

3. Conduct exploratory data analysis to identify any outliers or missing values. Handle these outliers and missing values appropriately, either by removing them or imputing values.

4. Once the data is cleaned and ready, perform multiple regression analysis. This can be done using the regression analysis functionality available in your software.

5. Specify the dependent variable (number of wins) and the independent variables (size of stadium, ERA, stolen bases).

6. Run the regression analysis and examine the results. Look for the regression equation and the coefficients of the independent variables, as well as the p-values.

7. Interpret the results. The regression equation will show how the independent variables collectively explain the variation in the dependent variable. The coefficients of the independent variables will indicate the direction and strength of their relationship with the dependent variable. The p-values will help determine if the relationships are statistically significant.

8. Use graphical methods to display the data and results. Consider creating scatter plots to visualize the relationships between each independent variable and the dependent variable. Additionally, create a residual plot to evaluate the assumptions of linearity, homoscedasticity, and normality.

9. Write a research paper summarizing the findings. Include a table with the raw data, the results of the regression analysis, and the graphical representations. In the paper, describe the methodology used, the interpretation of the results, and any limitations or potential sources of error.

10. In the appendix of the paper, include the complete dataset along with the computed results and any additional analysis.

By following these steps, you can conduct a multiple regression analysis to answer the research question of whether the number of wins of a team can be explained linearly by the size of the stadium, ERA, and stolen bases. Remember to thoroughly analyze the results and provide appropriate interpretations in your research paper.