#2) The Dairyr Herd Improvement Cooperative (DHI) in Upstate New York collects and analyzes data on milk production. One question of interest is how to develop a suitable model to predict current milk production from a. set of measured variables. The response variable is current milk production in pounds (CurrentMilk). The predictor variables are dened below. Samples are taken once a month during milking. The period that a cow gives milk is called lactation. Number of lactations is the number of times a cow has calved or given milk. The recommended management practice is to have the cow produce milk for about 305 days and then allow a 60-day rest period before beginning the next lactation. The data set, consisting of 199 observations, was compiled from the DHI milk production records. Predictor Variables Previous (Previous month milk production in pounds) Fat (Percent of fat in milk) Protein (Percent of protein in milk) Days (Number of days since present lactation) Lactation (Number of lactations) 17 9 (Indicator variable, 0 if Days 79, and 1 if Days > 79) (A) When dealing with the Full Model analysis, what informlition do we learn rm: the R Square value in the SPSS Model Summary Table (the Excel Regression Statistics Table)? How is this value computed? How is the Standard Error of the Estimate value computed? (6 points) Model Summarvb Change Statistics Adjusted R Std. Error of R Square 5 Model R R Square Square the Estimate Change F Change an 5 an Sig. F Change 1 .815' .664 .653 10.674 .664 63.200 6 192 .000 a. Predictors: (Constant). I79, Previous. Fat. Lactation, Protein. Days b. Dependent Variable: CurrentMiIl-c