It is suspected from theoretical considerations that the rate of water flow from a firehouse is proportional to some power of the nozzle pressure. Assume pressure data is more accurate. You are transforming the data. F 96 129 135 145 168 235 p 11 17 20 25 40 55 What is the exponent of the nozzle pressure in the regression model F = apb?
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complete answer and solution onlt no need explanation
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It is suspected from theoretical considerations that the rate of water flow from a firehouse is proportional to some power of the nozzle pressure. Assume pressure data is more accurate. You are transforming the data.
F
96
129
135
145
168
235
p
11
17
20
25
40
55
What is the exponent of the nozzle pressure in the regression model F = apb?
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- Please help with the artificial intelligence question below thanks! Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6How to solve this Use the fastfood.csv file to complete the following assignment. Create a file, fastfood.py, that loads the .csv file and runs a regression predicting calories from total_fat, sat_fat, cholesterol, and sodium, in that order. Add a constant using sm.add_constant(data). Then, print the following to two decimals print(model.mse_total.round(2)) print(model.rsquared.round(2)) print(model.params.round(2)) print(model.pvalues.round(2))Solve the modified task 3.14 from the textbook.Piecewise functions are sometimes useful when the relationship between a dependent and anindependent variable cannot be adequately represented by a single equation. For example, the velocityof a rocket might be described by?(?) ={ 10?2 − 5?, 0 ≤ ? ≤ 8624 − 3?, 8 ≤ ? ≤ 1636? + 12(? − 16)2, 16 ≤ ? ≤ 262136?−0.1(?−26), ? > 260, ??ℎ??????Develop an M-file function to compute ? as a function of ?. Then, develop a script that uses this functionto generate a plot of ? versus ? for ? = −5 to 50
- 1 Implement a function performing gradient descent using numerical solution (STOCHASTIC gradient descent) def myGradientDescentFun(X,y,learning_rate,epoches, batchsize): # Inputs: Training data, training label, learrning rate, number of epoches, batch size # Output: the final Weights # the loss history along batchesIn R, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to near…In Python, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to…
- Answer the following: This problem exercises the basic concepts of game playing, using tic-tac-toe (noughts and crosses) as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X’s and no O’s. Similarly, On is the number of rows, columns, or diagonals with just n O’s. The utility function assigns +1 to any position with X3=1 and −1 to any position with O3=1. All other terminal positions have utility 0. For nonterminal positions, we use a linear evaluation function defined as Eval(s)=3X2(s)+X1(s)−(3O2(s)+O1(s)). a. Show the whole game tree starting from an empty board down to depth 2 (i.e., one X and one O on the board), taking symmetry into account. b. Mark on your tree the evaluations of all the positions at depth 2. c .Using the minimax algorithm, mark on your tree the backed-up values for the positions at depths 1 and 0, and use those values to choose the best starting move. Provide original solutions including original diagram for part a!Answer the following: This problem exercises the basic concepts of game playing, using tic-tac-toe (noughts and crosses) as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X’s and no O’s. Similarly, On is the number of rows, columns, or diagonals with just n O’s. The utility function assigns +1 to any position with X3=1 and −1 to any position with O3=1. All other terminal positions have utility 0. For nonterminal positions, we use a linear evaluation function defined as Eval(s)=3X2(s)+X1(s)−(3O2(s)+O1(s)). a. Show the whole game tree starting from an empty board down to depth 2 (i.e., one X and one O on the board), taking symmetry into account. b. Mark on your tree the evaluations of all the positions at depth 2. c .Using the minimax algorithm, mark on your tree the backed-up values for the positions at depths 1 and 0, and use those values to choose the best starting move. Provide original solution!Simulated annealing is an extension of hill climbing, which uses randomness to avoid getting stuck in local maxima and plateaux. a) As defined in your textbook, simulated annealing returns the current state when the end of the annealing schedule is reached and if the annealing schedule is slow enough. Given that we know the value (measure of goodness) of each state we visit, is there anything smarter we could do? (b) Simulated annealing requires a very small amount of memory, just enough to store two states: the current state and the proposed next state. Suppose we had enough memory to hold two million states. Propose a modification to simulated annealing that makes productive use of the additional memory. In particular, suggest something that will likely perform better than just running simulated annealing a million times consecutively with random restarts. [Note: There are multiple correct answers here.] (c) Gradient ascent search is prone to local optima just like hill climbing.…
- In the language R: Which of the following functions in the broom library can generate a data frame of component-level information (e.g., coefficients, t-statistics) from a linear regression model? Select one: A. glance() B. augment() C. round_df() D. tidy()Hey guys can I get help with the following question: Function 6 Write a function to return the number of false negatives (as an integer) of a logistic regression model Given the training features (X_train), training labels (y_train), testing features (X_test) and testing labels (y_test) ### START FUNCTION 6 def log_reg_fn(X_train, y_train, X_test, y_test): # YOUR CODE HERE ### END FUNCTION 6 log_reg_fn(X_train_c, y_train_c, X_test_c, y_test_c)Q1 The periodic function sin(2x) has multiple roots between x values of -5π and 5π. If xL = -15 and xU = 15, which of the following statements is true using a bracketed method? Select one: a. All roots will be returned b. The middle root will be returned c. The chosen bracket is invalid for bracketed methods d. A single root will be returned e. The algorithm will be stuck in an infinite loop Q2 Consider x and y to represent data points (xi,yi), where i = 1, 2, 3, … n. What is the length of pafter running the following command? p = polyval(x,y) Select one: a. n b. n - 1 c. n + 1 d. Empty variable e. 1 Q3 Consider a system of linear equations in the form of AX = B, where X is the unknown vector. Which of the following can be used to solve for X? Select one: a. X = A\B b. X = B./A c. X = inv(B)*A d. X = inv(A)./B e. X = B\A