Please solve using SPSS y1118302611145394379107y1212261418145X₁9.910.110.08.710.310.511.011.212.913.216.215.816.215.816.016.2X₁17.117.517.517.417.417.818.0X₂1.20.50.90.81.50.31.20.61.80.20.71.60.41.20.81.2X20.70.81.20.81.10.51.6X34444443333333333X33333333X4172318913109710an6ovana121012X4101218913810X512030201604015050100905080120506090160(continued)X590110130603016040 14.61 ● This exercise requires use of a computer package.The accompanying n = 25 observations on y = catch atintake (number of fish), x₁ = water temperature (°C),x₂ = minimum tide height (m), x3 = number of pumpsrunning, x4 = speed (knots), and x5 = wind-range ofdirection (degrees) constitute a subset of the data thatappeared in the article "Multiple Regression Analysisfor Forecasting Critical Fish Influxes at Power StationIntakes" (Journal of Applied Ecology [1983]: 33-42).Use the variable selection procedures discussed in thissection to formulate a model.y1742X₁6.77.8X20.51.0X344ХА1024X55030(continued)

A Multiple regression model is used for analyzing the relationship between a single dependent variable and several independent variables. A multiple regression model is given by: yi=β0+β1x1+β2x2+....+βpxp+εi for i=1,2,....,n where y= dependent variablex's=independent variablesβi=ith parameter of the model.ε=error term ~ N(0,σ2)In the given question we have following variables: y= Catch at intake (number of fish) x1= Water temperature x2= minimum tide height x3= number of pumps running x4= speed x5= wide range of direction. The procedure to carry out multiple regression model building and model utility test in SPSS is as follows: 1. Enter the data in SPSS, define the variables and select the measure as scale in the variable view. 2. Go to Analyze>Regression>Linear. Enter 'y' in Dependent and x1,x2,x3,x4,x5 in Independent. Click OK. The SPSS output is as follows: The multiple linear regression model is: y^=101.16-2.84x1+6.29x2-23.56x3+1.44x4+0.09x5 At 10% level of significance, a variable is significant if the p-value for that variable is less than 0.10 From the coefficients table, we can see that the p-values for x1, x3, and x4 is less than 0.10, so we can say that these independent variables are significant. The variables x2 and x5 are not significant.We will remove the insignificant variables from the model, and will again perform the regression building exercise following the same steps. The regression output with the variables x1, x3, and x4 is as follows: From the coefficients table, we can see that the p-values for x1, x3, and x4 is less than 0.10, so we can say that these independent variables are significant at 10% level of significance. Hence, the multiple linear regression model is: y^=100.14-2.36x1-19.91x3+1.03x4 Hence, the model has been obtained using the variable selection method in a stepwise manner. Required answer: y^=100.14-2.36x1-19.91x3+1.03x4