Scenario: 1. Relates to the dataset given. You may use SPSS or R for the analysis. The dependent variable is the number of longnose dace (Rhinichthys cataractae) per 75-meter section of stream. The independent variables are the area (in acres) drained by the stream: the dissolved oxygen in mg/liter): the maximum depth (in cm) of the 75-meter segment of stream: nitrate concentration (mg/liter): sulfate concentration (mg/liter): and the water temperature on the sampling date (in degrees C). Instructions: a) Conduct the collinearity diagnostics by using the xı as the dependent variable against all other independent variables, attach the screenshot and describe the results [3 marks) b) Generate the residual plots and partial residual plots (7 in total) and describe each in terms of linearity, homoscedasticity, and normality. [21 marks] c) One assumption of linear regression is that the residuals of the DV should be normally distributed. Why is this necessary? [2 marks] d) Attach a screenshot of the Model Summary and interpret the adjusted R Square. [3 marks) e) Attach a screenshot of the ANOVA table and interpret the sig-value. [2 marks] f) Write the equation of the estimated regression equation. [3 marks) 9) Interpret the intercept (constant) and the slope for X3, X5 and 36. [4 marks] -variable fish 13 X3 depth 80 83 96 56 43 12 54 19 37 2 72 164 18 X2 do2 9.6 8.5 8.3 9.2 8.1 9.2 9.4 10.2 7.5 8.5 11.9 8.3 8.3 1 53 16 32 51 911 81 120 46 56 37 120 103 105 42 65 51 X1 acre 2528 3333 19611 3570 1722 583 4790 35971 25440 2217 1971 12620 19046 8612 3896 6298 27350 4145 1175 8297 7814 1745 5046 18943 8624 2225 12659 1967 1172 639 X6 temp 15.3 19.4 19.5 17 19.3 12.9 16.7 13.8 13.7 14.3 22.2 16.8 18 15 18.4 18.2 24.1 21 8.2 X4 no3 2.28 5.34 0.99 5.44 5.66 2.26 4.1 3.2 3.53 1.2 3.25 0.61 2.93 1.57 2.77 0.26 6.95 0.34 1.3 5.26 0.44 2.19 0.73 0.25 3.37 2.3 3.3 7.71 2.62 3.53 X5 S04 16.75 7.74 10.92 16.53 5.91 8.81 5.65 17.53 8.2 10.85 11.12 18.87 11.31 16.09 12.79 17.63 14.94 44.93 21.68 6.36 20.24 10.27 7.1 14.21 7.51 9.72 5.98 26.44 4.64 4.46 23 23 18 112 25 5 26 8 15 11 57 10.4 8.6 8.5 8.7 7.7 9.9 6.8 9.4 7.6 9.2 8.6 9.1 9.7 8.6 8.3 9.5 60 160 48 109 50 78 41 65 11 21.8 19.1 22.6 14.3 19 18.5 21.3 20.5 18 16.8 20.5 20.1 87 33 22 98 50 1 73 26 5 1 1381 301 12 24 6 15 381 84 3 18 63 239 234 6 76 25 8 23 16 60 855 133 621 44 67 54 1101 56 85 58 34 1501 931 53 130 7056 1934 6260 424 3488 3330 2227 8115 1600 15305 7121 5794 8636 4803 1097 97651 4266 1507 3836 17419 8735 22550 9961 4706 4011 6949 11405 904 3332 575 29708 25111 6.4 10.5 9.5 8.31 9.3 9.11 6.8 9.6 10.23 9.7 9.5 9.4 8.4 8.5 8.31 9.3 8.9 7.4 8.3 7.4 8.2 8.4 8.6 8.9 8.31 9.33 9.2 9.8 8.4 6.8 7.71 10.2 0.25 2.34 2.41 3.49 2.11 0.81 0.33 3.4 3.54 2.6 0.51 1.19 3.31 5.01 1.71 4.38 2.05 0.84 1.32 0.29 1.56 1.41 1.02 4.06 4.71 4.57 2.17 6.811 2.09 2.47 0.63 4.17 9.82 11.44 13.77 5.82 13.37 8.16 7.6 9.22 5.69 6.96 7.41 12.27 5.95 10.98 15.77 5.74 12.77 16.3 7.36 2.5 13.22 14.45 9.07 99 5.38 17.84 10.17 9.2 5.53 7.61 12.28 10.75 24.5 12 211 20.2 24 14.9 24 20.5 19.5 17.5 16 17.5 18.1 24.3 13.1 16.9 17 21 18.5 18| 20.8 23 21.8 19.7 18.9 18.6 23.6 19.2 17.7 18 21.4 17.7 68 51 121 48 63 107 79 100 80 28 48 18 361 19 321 3 106 62 611 521 100 70 39 73 333 73 60 23 184221 2 6311 26 1450 201 4106 38 10274 19 5101 9.93 7.6 7.9 101 9.3 6.7 45 46 60 96 90 82 1.58 0.64 2.96 2.62 5.45 5.25 8.37 21.16 8.84 5.45 24.76 14.19 20.1 18.5 18.6 15.4 15 26.5