0
$\begingroup$

I have a water quality data frame of 6 monitoring stations that collected data in wet and dry season from 9 quality parameters.

I would like to know if there is a difference between the monitoring seasons and also if there is a difference between the dry and rainy seasons of the 9 quality parameters.

I would like to know if it is possible to use ANOVA to do this in R or if ANOVA is not the most suitable test for this.

independent variables = station and season

dependent variables =pH,OD,DBO,N_Total,P_Total,Coli_Termo,Sol_Totais,Turbidez,IQA.

dput out

structure(list(station = c("F02", "F06", "F05", "F04", "F01", 
"F05", "F04", "F06", "F01", "F02", "F04", "F04", "F03", "F04", 
"F02", "F05", "F04", "F04", "F01", "F06", "F03", "F04", "F04", 
"F04", "F06", "F01", "F05", "F02", "F04", "F04", "F03", "F04", 
"F05", "F02", "F06", "F04", "F01", "F04", "F04", "F04", "F03", 
"F04", "F04", "F04", "F03", "F04", "F02", "F01", "F05", "F06", 
"F04", "F04", "F06", "F01", "F02", "F04", "F05", "F03", "F04", 
"F04", "F04", "F06", "F05", "F01", "F02", "F04", "F03", "F02", 
"F06", "F05", "F04", "F04", "F04", "F04", "F03", "F06", "F05", 
"F02", "F04", "F04", "F04", "F04", "F03", "F04", "F01", "F05", 
"F02", "F04", "F06", "F04", "F04", "F04", "F03", "F06", "F05", 
"F02", "F01", "F04", "F04", "F04", "F03", "F04", "F01", "F06", 
"F05", "F02", "F04", "F04", "F03", "F04", "F04", "F04", "F06", 
"F01", "F05", "F02", "F04", "F04", "F04", "F03", "F04", "F04", 
"F03", "F04", "F04", "F04", "F01", "F05", "F04", "F06", "F02", 
"F03", "F04", "F02", "F06", "F05", "F04", "F04", "F01", "F04", 
"F04", "F04", "F03", "F06", "F05", "F01", "F04", "F02", "F04", 
"F04", "F04", "F03", "F04", "F06", "F01", "F02", "F05", "F04", 
"F04", "F04", "F04", "F03", "F04", "F04", "F04", "F04", "F06", 
"F05", "F01", "F02", "F04", "F02", "F04", "F01", "F04", "F04", 
"F05", "F04", "F03", "F04", "F06", "F04", "F04", "F04", "F04", 
"F03", "F06", "F04", "F05", "F01", "F02", "F04", "F04", "F05", 
"F06", "F02", "F04", "F01", "F03", "F04"), year = c("2005", "2005", 
"2005", "2005", "2005", "2005", "2005", "2005", "2005", "2005", 
"2005", "2005", "2005", "2005", "2005", "2005", "2005", "2005", 
"2005", "2005", "2005", "2005", "2005", "2005", "2005", "2005", 
"2005", "2005", "2005", "2005", "2005", "2005", "2006", "2006", 
"2006", "2006", "2006", "2006", "2006", "2006", "2006", "2006", 
"2006", "2006", "2006", "2006", "2006", "2006", "2006", "2006", 
"2007", "2007", "2007", "2007", "2007", "2007", "2007", "2007", 
"2007", "2007", "2007", "2007", "2007", "2007", "2007", "2007", 
"2007", "2007", "2007", "2007", "2007", "2007", "2007", "2007", 
"2007", "2007", "2007", "2007", "2007", "2007", "2007", "2007", 
"2007", "2008", "2008", "2008", "2008", "2008", "2008", "2008", 
"2008", "2008", "2008", "2008", "2008", "2008", "2008", "2008", 
"2008", "2008", "2008", "2008", "2008", "2008", "2008", "2008", 
"2008", "2008", "2008", "2008", "2008", "2008", "2008", "2008", 
"2008", "2008", "2008", "2008", "2008", "2008", "2008", "2008", 
"2008", "2008", "2008", "2008", "2008", "2008", "2008", "2008", 
"2008", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2009", "2009", "2009", 
"2009", "2009", "2009", "2009", "2009", "2010", "2010", "2010", 
"2010", "2010", "2010", "2010", "2010", "2010"), season = c("wet", 
"wet", "wet", "wet", "wet", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "wet", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "wet", 
"wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", "wet", 
"wet", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", 
"dry"), pH = c(8.1, 7.8, 8, 7.9, 8.2, 8, 7.9, 7.8, 8.2, 8.2, 
8.2, 7.9, 8.1, 7.9, 8.2, 8, 6.7, 8.1, 8.2, 7.9, 7.8, 7.8, 7.5, 
7.9, 7.8, 8.1, 8, 8.1, 8, 7.6, 7.8, 7.9, 7.9, 8, 7.8, 7.9, 8.9, 
8, 7.4, 7.9, 7.6, 8, 7.7, 7.6, 8, 8.2, 8.5, 8.7, 8.4, 8.2, 8.1, 
7.5, 7.9, 8.3, 8.2, 7.4, 8, 7.9, 7.8, 7.5, 8, 7.9, 8.1, 8.4, 
8.2, 6.9, 6.8, 7.4, 7.4, 8, 7, 7, 7.2, 7.1, 7, 7.3, 7.7, 7.8, 
7.5, 6.5, 7.8, 7.4, NA, 7.9, 8.2, 7.6, 8.3, 7.8, 7.7, 7.7, 8.4, 
8.4, 8.4, 8.1, 8.4, 8.5, 8.5, 8, 7.9, 8.4, 8.3, 8.4, 8.1, 7, 
7.8, 8.1, 7.2, 7.5, 8, 7.6, 7.8, 7.4, 7.3, 8.2, 6.8, 7.8, 7.4, 
7.5, 7.5, 7.6, 7.2, 85, 8.2, 6.4, 7.9, 8.6, 7.9, 7.8, 7.5, 7.5, 
7.9, 8, 7.4, 7, 6.3, 6.5, 8.1, 7.8, 6.9, 7.9, 6.9, 8.1, 8.7, 
8, 8.3, 8.5, 8.1, 8.4, 8.1, 7.6, 7.8, 8, 8.1, 7.4, 8, 8, 7.8, 
7.6, 8.4, 7.6, 7.6, 8, 7.6, 8.1, 8.2, 7.7, 7.4, 7.7, 8.1, 8, 
7.9, 7.8, 8, 7.6, 7.6, 8, 8, 7.8, 7.8, 7.3, NA, 8.2, 8.2, 7.6, 
8.2, 8, 7.4, 7.8, 7.7, 8, 7.9, 7.9, 8.1, 7.7, 7.7, 7.8, 8.1, 
7.9, 8, 7.9), OD = c(10.2, 7.3, 7.5, 6.8, 8.7, 7.5, 6.8, 7, 8.2, 
8.6, 9.8, 6.4, 8.9, 8.5, 7, 7.6, 4.2, 6.8, 7.6, 6.8, 4.8, 3, 
6.6, 6.6, 8.2, 8.4, 8.6, 8.8, 3.8, 4.6, 4.2, 5.2, 8.8, 6.8, 8.5, 
5.7, 6.2, 5.6, 4.2, 5.4, 3.3, 4.3, 1.7, 2.7, 3.9, 6.7, 7.6, 7.9, 
8.6, 5.6, 3.5, 3.5, 7.1, 8.8, 8, 3.8, 9.5, 1.5, 0.3, 2.6, 5.7, 
7, 8.2, 6.9, 8.2, 3.2, 5, 5.9, 6.8, 7.4, 1.6, 2.3, 6.2, 0.5, 
4.2, 6.3, 7.8, 7.7, 5.2, 1.8, 3.5, 3.7, NA, 5.6, 6, 7.1, 2.7, 
4.8, 4.9, 2.1, 3.3, 4, 3.5, 7, 8.4, 9.2, 5.9, 5.6, 5.2, 6.5, 
6.1, 6.4, 7.2, 4.6, 7.3, 7.6, 3, 3.1, 4.8, 6.6, 1.6, 4, 4.6, 
6.2, 6.9, 7.1, 2.7, 1.5, 3.5, 4.5, 4.1, 5.7, 5.7, 2.8, 5.8, 5.8, 
6.1, 6.9, 6, 6.5, 6.3, 5.1, 5.5, 5, 5.5, 7.3, 5.7, 5.3, 6.1, 
5.4, 4.3, 5.8, 5.5, 5, 5.4, 6, 5.2, 6, 5.5, 4.6, 5.1, 6.9, 6.4, 
7.3, 6.1, 6.8, 7.6, 6, 6.2, 4.7, 6.7, 5.1, 4.4, 6.5, 6.6, 4.8, 
4.2, 6.2, 5.2, 8.2, 5.3, 7, 4.4, 5.7, 5.1, 4.3, 7.1, 4, 4.1, 
5.7, NA, 6, 6, 6.2, 6.2, 5.8, 6.5, 5.2, 7.2, 5.8, 7.1, 6.9, 6.7, 
6.2, 6.2, 6.8, 6.6, 6.9, 6.5, 6.6), DBO = c(0, 0, 0, 1, 1, 0, 
1, 1, 1, 1, 1, 1, 1, 4, 0, 0, 1, 1, 1, 2, 6, 7, 3, 1, 1, 1, 1, 
1, 0, 1, 2, 3, 0, 0, 0, 1, 1, 1, 1, 6, 7, 0, 2, 2, 8, 1, 1, 1, 
1, 2, 0, 0, 0, 0, 0, 1, 2, 4, 9, 0, 0, 0, 1, 1, 1, 2, 4, 0, 0, 
0, 1, 3, 3, 10, 19, 0, 0, 0, 1, 1, 3, 4, NA, 4, 1, 1, 2, 3, 12, 
0, 0, 0, 0, 0, 1, 1, 1, 3, 0, 1, 1, 1, 0, 0, 0, 1, 2, 1, 1, 1, 
2, 9, 1, 1, 1, 1, 2, 1, 1, 1, 15, 1, 2, 4, 5, NA, 1, 1, 2, 2, 
2, 0, 0, 0, 0, 0, 1, 1, 1, 3, 5, 1, 1, 1, 1, 1, 2, 2, 3, 4, 5, 
0, 1, 1, 1, 1, 1, 2, 2, 4, 6, 1, 1, 1, 1, 6, 1, 2, 2, 2, 3, 1, 
1, 2, 2, 3, 3, 4, 6, 6, NA, 0, 0, 0, 1, 2, 1, 2, 2, 3, 3, 0, 
0, 1, 1, 1, 2, 2, 5, 7), Coli_Termo = c(500, 500, 110, 30000, 
800, 300, 1300, 500, 80, 130, 50000, 800, 220, 5000, 130, 70, 
5000, 230, 170, 1300, 30000, 80000, 1100, 3000, 500, 300, 110, 
20, 5000, 500, 11000, 700, 2400, 1700, 230, 24000, 800, 30000, 
500, 2200, 22000, 3000, 1700, 2200, 50000, 500, 80, 70, 40, 800, 
300, 230, 2200, 230, 40, 80, 40, 8000, 1100, 80, 500, 230, 130, 
70, 40, 230, 900, 220, 500, 170, 170, 260, 300, 70, 110000, 300, 
270, 230, 14000, 2400, 22000, 170000, NA, 14000, 230, 220, 300, 
13000, 500, 5000, 14000, 1700, 3000, 700, 80, 800, 500, 5000, 
1100, 7000, 800, 1300, 900, 120, 110, 300, 5000, 1300, 1700, 
2400, 5000, 3e+05, 1100, 1300, 900, 1700, 1100, 11000, 90, 700, 
2200000, 1100, 35000, 5000, 280000, 5000, 20, 110, 340, 2400, 
20, 16000, 9200, 16000, 1300, 170, 160000, 2400, 1100, 540000, 
16000, 22000, 4900, 790, 230, 130, 2200, 140, 490, 9200, 130000, 
1300, 630, 490, 110, 45, 45, 270, 780, 2200, 49000, 1300, 490, 
3300, 330, 79000, 1300, 210, 78, 130, 3500, 9200, 170000, 1300, 
1300, 16000, 700, 240000, 16000, 16000, NA, 13000, 13000, 140, 
1300, 54000, 3500, 2300, 45, 700, 4600, 16000, 17000, 5400, 5400, 
1700, 160000, 1700, 1400000, 160000), P_Total = c(0.027, 0.013, 
0.01, 0.2, 0.071, 0.028, 0.081, 0, 0.049, 0, 0.013, 0.073, 0.084, 
1.79, 0.101, 0.18, 0.081, 0.318, 0.07, 0.028, 2.41, 8.401, 0.076, 
0.321, 0.011, 0.016, 0.091, 0.038, 0.109, 0.076, 1.367, 0.127, 
0.094, 0.058, 0.086, 0.527, 0.085, 0.401, 0.019, 0.492, 0.776, 
0.104, 0.018, 0.058, 2.86, 0.331, 0.105, 0.041, 0.036, 0.036, 
0.15, 0.082, 0.244, 0.021, 0.008, 1.17, 0.058, 2.748, 1.094, 
0.24, 0.265, 0.118, 0.09, 0.09, 0.05, 0.416, 0.41, 0.31, 0.059, 
0.037, 0.385, 0.183, 0.239, 1.995, 2.529, 0.045, 0.149, 0.064, 
0.333, 0.132, 0.818, 0.516, NA, 0.356, 0.073, 0.73, 0.067, 0.548, 
0.059, 0.107, 0.072, 0.469, 0.112, 0.171, 0.189, 0.502, 0.189, 
0.327, 0.313, 0.25, 0.315, 0.28, 0.129, 0.095, 0.05, 0.129, 0.64, 
0.07, 0.244, 0.071, 0.374, 7.6, 0.246, 0.465, 0.109, 0.034, 0.482, 
0.39, 0.459, 0.363, 5.2, 0.933, 0.223, 0.665, 3.02, 0.571, 0.445, 
0.135, 0.732, 0.049, 0.097, 0.528, 1.041, 0.285, 0.144, 0.226, 
0.908, 0.768, 0.007, 0.58, 0.839, 0.458, 0.507, 0.424, 0.419, 
0.404, 0.423, 0.409, 0.549, 0.48, 1.915, 0.1, 0.13, 0.15, 0.09, 
0.093, 0.098, 0.5, 0.098, 0.104, 1.8, 0.062, 0.03, 0.029, 0.039, 
7, 0.029, 0.025, 0.022, 0.043, 0.305, 0.064, 0.131, 0.168, 0.093, 
0.265, 0.04, 0.2, 0.415, 0.603, NA, 0.044, 0.044, 0.049, 0.049, 
0.141, 0.06, 0.306, 0.088, 0.094, 0.049, 0.066, 0.039, 0.039, 
0.039, 0.061, 0.332, 0.028, 0.647, 0.153), N_Total = c(0.7, 0.62, 
0.6, 5.02, 0.68, 0.22, 1.33, 0.79, 0.68, 0.64, 0.71, 1.15, 1.18, 
8.44, 0.31, 0.23, 0.49, 1.38, 0.21, 0.48, 22.07, 40.06, 0.66, 
1.64, 1.1, 0.49, 0.44, 0.79, 0.55, 0.1, 67.78, 0.45, 0.4, 0.54, 
0.44, 2.89, 0.4, 0.57, 0.32, 14.27, 80.7, 0.34, 0.55, 0.77, 21.78, 
3.44, 0.25, 0.27, 0.4, 0.45, 0.91, 0.16, 0.6, 0.39, 0.33, 3.51, 
0.82, 22.96, 10.74, 0.21, 2.31, 0.56, 0.52, 0.97, 0.5, 84.13, 
95.17, 0.49, 0.27, 0.03, 0.19, 0.28, 1.52, 14.7, 61.59, 0.38, 
0.18, 0.2, 0.4, 1.24, 5.02, 1.4, NA, 9.47, 0.28, 0.23, 0.41, 
19.93, 0.27, 0.39, 0.35, 26, 0.73, 0.33, 0.28, 0.37, 0.46, 3.71, 
0.54, 0.51, 0.27, 0.25, 0.42, 0.61, 0.34, 0.51, 7.05, 0.24, 0.99, 
0.49, 1.94, 45.14, 0.98, 0.71, 0.53, 0.72, 23.51, 0.77, 0.73, 
0.61, 88.41, 1.26, 2.5, 0.8, 24.29, 1.24, 0.64, 0.53, 7.63, 0.06, 
0.79, 1.27, 0.85, 1.44, 2, 0.91, 0.99, 0.7, 0.53, 7.03, 6.93, 
0.23, 0.56, 0.23, 0.09, 1.52, 0.16, 0.13, 2.29, 0.51, 15.4, 0.75, 
0.24, 0.24, 0.34, 0.44, 0.2, 1.9, 0.53, 0.21, 14.92, 0.72, 0.32, 
0.41, 0.26, 16.28, 0.47, 0.38, 0.48, 0.44, 0.92, 0.41, 0.59, 
0.81, 0.81, 0.68, 0.24, 1.84, 1.82, 1.43, NA, 0.42, 0.42, 0.59, 
0.24, 4.24, 0.59, 2.6, 0.11, 0.51, 0.29, 0.43, 0.3, 0.45, 0.45, 
0.19, 1.3, 0.16, 2.14, 2.04), Sol_Totais = c(226, 252, 203, 301, 
244, 214, 282, 241, 161, 231, 293, 326, 273, 331, 247, 238, 270, 
161, 127, 219, 336, 534, 140, 152, 212, 218, 203, 212, 356, 252, 
267, 287, 174, 207, 167, 231, 179, 272, 189, 91, 823, 319, 356, 
190, 499, 282, 215, 204, 208, 219, 504, 553, 362, 307, 394, 492, 
368, 794, 621, 587, 449, 277, 381, 345, 372, 1532, 1516, 381, 
445, 329, 517, 567, 442, 389, 1338, 236, 219, 225, 255, 307, 
238, 232, NA, 369, 240, 226, 242, 317, 248, 400, 319, 297, 328, 
226, 214, 224, 238, 294, 390, 334, 293, 307, 386, 265, 231, 226, 
299, 389, 357, 320, 313, 470, 265, 264, 232, 227, 308, 466, 389, 
381, 849, 284, 10370, 426, 414, 334, 211, 222, 312, 233, 223, 
346, 415, 255, 235, 212, 311, 318, 235, 352, 313, 323, 350, 236, 
180, 229, 311, 233, 309, 400, 365, 349, 283, 198, 275, 233, 210, 
306, 305, 404, 382, 328, 377, 287, 275, 354, 215, 233, 249, 247, 
326, 233, 290, 219, 356, 289, 142, 290, 297, 374, NA, 335, 335, 
372, 280, 370, 246, 324, 231, 307, 247, 402, 334, 271, 271, 221, 
790, 200, 536, 314), Turbidez = c(9.7, 11.4, 10, 9.8, 8.23, 0.9, 
1.2, 3.01, 3.8, 1.62, 5.68, 2, 4.9, 4.41, 1.35, 0.99, 2.5, 4.14, 
4.94, 2.84, 4.95, 4.4, 5.5, 7.09, 2.67, 6.08, 1.08, 1.07, 2.02, 
1.81, 4.44, 2.93, 1.12, 2.37, 3.84, 5.29, 6.35, 3.34, 3.14, 5.9, 
5.47, 2, 4, 4, 3, 2.21, 1.58, 2.57, 1.11, 5.29, 1.99, 4.64, 2.32, 
4.87, 0.99, 1.07, 0.81, 3.59, 10, 4.6, 1.39, 2.54, 1.45, 2.75, 
1.13, 205, 189, 3.15, 2.15, 2.54, 9.67, 16.8, 4.8, 94.4, 13.8, 
3.2, 2.89, 2.89, 232, 5.83, 67, 43.3, NA, 5.2, 9.15, 2.35, 4.49, 
6.47, 3.07, 12.2, 6.3, 7.8, 5.05, 1.73, 0.51, 1.29, 15.96, 8.2, 
1.94, 11.5, 4.83, 3.27, 5.41, 0.84, 0.98, 112, 1.94, 2.09, 8.05, 
1.36, 4.31, 10.78, 0.54, 3.1, 0.71, 1.08, 0.83, 7.69, 2.22, 1.66, 
10.37, 7.04, 7.13, 14.38, 6.13, 3.06, 8.42, 0.93, 3.22, 3.35, 
1.32, 3.14, 2.77, 51.7, 1.13, 1.43, 1.88, 3.58, 20.7, 3.89, NA, 
2.1, 2.21, 3.69, 0.77, 4.93, 2.99, 1.47, 2.82, 5.35, 5.4, 3.11, 
2.66, 3.64, 1.91, 0.89, 0.86, 1.67, 2.89, 3.2, 8.47, 4.25, 3.51, 
2.82, 3.05, 8.79, 18.1, 4.68, 3.53, 2.48, 4.57, 15.2, 44.8, 86.3, 
18.1, 148, 5.01, 138, 128, 367, NA, 3.93, 3.93, 3.33, 8.79, 5.57, 
7.01, 5.78, 1.68, 24.9, 5.62, 9.07, 12.3, 3.89, 3.89, 3.99, 238, 
4.68, 583, 5.64), IQA = c(73, 75, 80, 54, 71, 77, 69, 75, 79, 
80, 58, 69, 76, 47, 76, 78, 56, 70, 76, 70, 37, 29, 70, 62, 77, 
78, 79, 86, 55, 67, 40, 65, 70, 70, 78, 51, 66, 53, 68, 54, 29, 
58, 51, 53, 29, 65, 79, 79, 83, 69, 59, 60, 64, 77, 81, 52, 79, 
28, 28, 53, 63, 74, 76, 77, 80, 30, 28, 69, 71, 78, 48, 51, 66, 
28, 21, 67, 76, 78, 43, 47, 38, 38, NA, 47, 73, 75, 74, 45, 62, 
47, 50, 55, 56, 71, 60, 65, 66, 54, 60, 57, 64, 64, 68, 70, 79, 
75, 44, 55, 57, 66, 41, 20, 60, 62, 69, 70, 44, 37, 57, 59, 18, 
55, 47, 41, 31, 54, 75, 78, 59, 66, 80, 44, 46, 53, 64, 74, 43, 
56, 69, 41, 37, 51, 53, 61, 66, 68, 57, 68, 60, 48, 33, 69, 68, 
72, 75, 79, 81, 64, 66, 56, 36, 62, 62, 65, 72, 21, 56, 72, 72, 
77, 54, 60, 73, 58, 61, 39, 71, 33, 31, 38, NA, 59, 59, 75, 67, 
50, 66, 57, 80, 64, 64, 58, 59, 64, 64, 69, 36, 69, 32, 44)), row.names = c(NA, 
200L), class = "data.frame")
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  • $\begingroup$ Welcome to CV! Since you are new here, you may want to take a tour. In order to increase the chance to get a response, please make a reproducible example for your dataset: copy and paste the output of dput(dataset) instead of dataset. $\endgroup$ Commented Dec 28, 2021 at 8:09

1 Answer 1

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With multiple continuous dependent variables, ANOVA is referred to as MANOVA. Suppose you save your dataset to the variable $X$. Then the following R code will run the desired MANOVA model

manova(cbind(pH,OD,DBO,N_Total, P_Total, Coli_Termo, Sol_Totais,Turbidez,IQA)~as.factor(station)+as.factor(season),data=X)
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