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ANOVA & Multiple Regression Analysis

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4. Because the school board's primary concern is whether or not the experimental curriculum led to better standardized test scores, your next step is to conduct a simple analysis comparing test scores from schools with the old curriculum with the test scores from schools with the new/experimental curriculum.
a) Conduct an ANOVA to evaluate whether or not there is a significant difference in test scores between schools with the old curriculum and schools with the new curriculum.
5. The board member who originally wanted you to include only low income households in the survey is still concerned about the particular effect of the experimental curriculum on schools in low income neighborhoods. To find the answer to this, you need to run a multiple regression model.
a) Create a dummy variable for the experimental curriculum and an interaction variable that interacts with the experimental dummy and the income variable.
b) Estimate a multiple regression model that includes the curriculum dummy, income, and interaction variable as independent variables.
c) Calculate predicted values for the chgtestscores variable for both the new and old curriculum for income levels of \$15,000, \$30,000, \$60,000, and \$120,000.
d) Summarize and interpret the results of this model. What do you tell the board member about the effect of the new curriculum across different income levels?

curriculum chgtestscores income (\$000) school
New 1.31 42 3
Old 1.49 10.4 10
New -0.15 47.2 11
Old -1.12 26.6 25
New 2.5 85.4 28
Old -0.32 96.9 31
New 0.83 73.3 32
New 1.34 38.5 39
Old -1.07 89.5 43
Old 0.98 39.1 55
New 2.87 104.357
Old 0.46 33.5 60
New 2.5 96.2 61
New 0.75 33.9 62
New 1.5 38.7 75
New -0.27 31.6 77
New 3.42 189.886
Old -0.19 7.5 89
New -0.75 25.6 91
Old 0.79 126 106
New 1.32 58.1 108
Old 0.15 39.4 109
Old -0.15 122.5110
Old -1.12 16.7 121
Old 0.39 41.1 123
New 2.41 116 125
Old -1.35 77.7 130
Old 1.34 26.2 137
New -0.5 37.1 138
Old 0.01 21.3 143
New -0.6 19.9 152
Old 0.05 46.1 153
Old 1.72 19.9 158
Old -1.03 51.3 168
Old 0.04 56.9 171
Old -0.04 50.6 174
New 1.9 63.2 186
New -0.07 40.5 189
Old -0.4 38.7 191
Old -0.27 25.8 194
Old 0.62 77.8 201
New -0.35 32.7 202
Old 1.06 48 212
New -0.79 11.3 213
Old 0.7 8.9 223
New 1.26 41 225
New 0.43 58.2 226
New 0.71 34.7 235
Old 1.19 13.8 236
New 1.12 53.4 239
New 1.75 93.9 252
Old 0.06 108.9255
Old 0.19 21.3 257
Old 0.72 18.3 265
New 3.3 143.8267
Old 0.6 25.9 276
New 1.09 37.4 278
New 0.19 48.3 281
Old 1.17 16.2 296
New 1.08 87.5 298
Old 0.66 7.9 300
New 0.59 37.2 303
Old 0.96 42.5 306
Old 0.37 20.4 310
New -0.59 26.3 321
New 1.55 73.7 324
New 1.4 106.2326
New 1.52 57.8 330
Old -0.5 11.8 335
Old 0.42 11 339
New 1.71 83.9 342
New 1.44 48.6 348
New 0.5 27.9 354
Old -0.51 41 360
New 0.44 59.8 364
New 2.35 76.5 376
Old -0.49 21.9 379
Old 1.43 23.2 381
New -0.37 19.1 383
Old -0.88 62.9 397

Solution Summary

The solution provides step by step method for the calculation of ANOVA and regression analysis. Formula for the calculation and Interpretations of the results are also included.

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