I need help with these questions if you could provide explanations that would be great.

Question 1:

A horticulturist is studying the relationship between tomato plant height and fertilizer amount. Thirty tomato plants grown in similar conditions were subjected to various amounts of fertilizer (in ounces) over a four-monll'l period. and then their heights {in inches} were measured. A portion of the data is shown in the accompanying table. Height Partilizar 20.4 1.9 29.1 5.0 36.4 3.1 Click here for the Excel Data File a. Estimate the linear regression model: Height - n + lFartilizer + a. {Negative values should be indicated by a minus sign. Round your answers to 2 decimal places} mm mm h-1. Estimate the quadratic regression model: Height: - 130 + lFartilizer + zl'ertilizarz + .2. {Negative values should be indicated by a minus sign. Round your answers to 2 decimal places} How _I____ \fb-2. Using the quadratic results, find the fertilizer amount at which the height reaches a minimum or maximum. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) Fertilizer amount = ounces c. Use the best-fitting model to predict, after a four-month period, the height of a tomato plant that received 3.0 ounces of fertilizer. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.) Height = inchesThe logistic model cannot be estimated with Excel. Because assembly line work can be tedious and repetitive, it is not suited for everybody. Consequently, a production manager is developing a binary choice regression model to predict whether a newly hired worker will stay in the job for at least one year (Stay equals 1 if a new hire stays for at least one year, 0 otherwise). Three predictor variables will be used: (1) Age; (2) a Female dummy variable that equals 1 if the new hire is female, 0 otherwise; and (3) an Assembly dummy variable that equals 1 if the new hire has worked on an assembly line before, 0 otherwise. The accompanying table shows a portion of data for 32 assembly line workers. Stay Age Female Assembly 35 1 26 38 ASpicture Click here for the Excel Data File a-1. Estimate the linear probability regression model and the logistic regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Predictor Variable Linear Model Logistic Model Constant Age Female AssemblyC E A B D F G Stay Age Female Assembly 35 H 3 26 1 35 D 5 28 6 1 31 7 0 31 1 55 0 LO 23 10 0 22 11 1 43 12 1 25 13 46 14 22 15 1 29 0 16 1 29 17 1 58 18 37 19 44 20 1 55 21 1 32 22 1 38 23 32 24 25 25 1 28 26 1 47 27 1 32 28 28 29 1 52 30 19 31 0 41 1 32 1 40 33 1 38 34 35 36 37a-2. Which of the following statements correctly infer the significance of the Assembly variable on Stay in both models. (You may select more than one answer. Single click the box with the question mark to produce a check mark for a correct answers and double click the box with the question mark to empty the box for a wrong answers. Any boxes left with a question mark will be automatically graded as incorrect.) ? It has a positive and significant influence at the 1% level. ? It has a positive but not significant influence at the 1% level. ? It has a positive and significant influence at the 5% level. ? It has a positive but not significant influence at the 5% level. b. Compute the accuracy rates of both models. (Do not round intermediate calculations and round final answers to 2 decimal places.) Linear Probability Model % Logistic Regression Model %c-1. Use the preferred model to predict the probability that a 45-year-old female who has not worked on an assembly line before will still be in thejob one year later. {Round coefficient estimates to at least 4 decimal places and final answer to 4 decimal places.) Predicted Probability I I c-2. Use the preferred model to predict the probability that a 45-year-old female who has worked on an assembly line before will still be in fhejob one year later. (Round coefficient estimates to at least 4 decimal places and final answer to 4 decimal places.) Predicted Probability I I The accompanying data file contains salary data (in $1,000s) for 30 college-educated men with their respective BMI and a dummy variable that represents 1 for a white man and 0 otherwise. Model 1 predicts Salary using BMI and White as predictor variables. Model 2 includes BMI and White along with an interaction between the two. picture Click here for the Excel Data File a-1. Use the holdout method to compare the predictability of the models using the first 20 observations for training and the remaining 10 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Predictor Variable Model 1 (No interaction) Model 2 (Interaction) Constant BMI White BMI x White a-2. Calculate the RMSE of the two models in the validation set. (Do not round intermediate calculations and round your final answers to 2 decimal places.) Model 1 (No interaction) Model 2 (Interaction) RMSE\fa-3. Which model is better for making predictions? because its RMSE is b-1. Use the k-fold method, with k = 3, to calculate the average RMSE of the two models. (Do not round intermediate calculations and round your final answers to 2 decimal places.) Model 1 (No interaction) Model 2 (Interaction) Average RMSEb-2. Is the preferred model the same as found by the holdout method":l D Yes, because the average RMSE equals the RMSE with the holdout method. 0 No, because the average RMSE does not equal the RMSE with the holdout method. 0 Yes, because the earlier preferred model also has the lowest average RMSE. O No, because the earlier preferred model does not have the lowest average RMSE. a-2. Calculate the RMSE of the two models in the validation set. (Do not round intermediate calculations and round your final answer to 2 decimal places.) Model 1 (Linear) Model 2 (Exponential) RMSE a-3. Which model is better for making predictions? because its RMSE isConsider a regression model for predicting monthly electricity cost (Cost in $) on the basis of average outdoor temperature (Temp in "F), working days per month (Days), and tons of product produced (Tons). The accompanying data file contains data on 80 observations. Apicture Click here for the Excel Data File a-1. Use the holdout method to compare the predictability of the linear and the exponential regression models using the first 60 observations for training and the remaining 20 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Predictor Variable Model 1 (Linear) Model 2 (Exponential) Constant Temp Days TonsA B C D E F G 1 Cost Temp Day's Tons 2 16747 46 22 75 3 7901 31 24 98 13526 67 25 86 5 11743 43 28 86 6 29243 100 29 82 7 8502 57 28 94 E 19775 76 25 74 9 23041 71 30 70 10 15870 23 26 66 11 15804 60 25 73 12 9447 28 29 95 13 23878 78 25 82 14 16998 72 24 80 15 10514 22 24 73 16 23615 50 26 66 17 15616 70 23 74 18 16481 89 25 99 19 14340 33 27 93 20 15148 30 28 67 21 20942 55 24 72 22 14022 71 29 86 23 28922 75 25 71 24 6881 52 22 99 25 7757 30 22 88 26 18452 82 25 74 27 18563 38 29 66 28 14136 83 25 98 29 10100 33 23 76 30 22655 91 29 74 31 11388 41 20 75 32 12480 30 27 73 33 10171 28 22 83 34 8557 29 20 73 35 24286 69 30 74 36 11093 36 22 70 37 32556 65 28 65 38 8968 62 23 95 39 10100 63 27 93 40 10941 79 24 9441 15806 43 26 66 42 15079 40 28 66 43 11274 72 25 84 44 18520 80 23 68 45 17285 45 25 92 46 20670 21 79 47 16460 62 21 62 48 26401 98 27 65 49 15300 41 24 78 50 13603 43 26 75 51 11977 52 23 95 52 29590 89 23 62 53 14798 60 23 81 54 9043 30 28 92 55 12196 60 21 90 56 14164 59 22 79 57 22527 70 28 65 58 17159 89 27 97 59 16634 91 23 73 60 11168 46 29 94 61 19271 44 24 60 62 6722 22 22 98 63 27653 85 28 67 64 12594 76 21 97 65 20876 76 30 67 66 24438 97 26 63 67 14575 23 22 82 68 12796 45 28 82 69 10773 97 24 95 70 10432 52 21 66 71 8131 63 20 95 72 9588 46 24 99 73 14741 51 28 66 74 20740 91 26 88 75 10760 63 23 84 76 17768 81 28 90 77 12295 38 28 73 78 8701 71 21 98 79 20055 96 22 81 80 8009 44 25 9679 20055 96 22 81 80 8009 44 25 96 81 11380 56 28 84 82 83 84 85 86