Imagine I want to estimate the following regression in StataTestScorei=.Bo B₁ Height; + ɛiWhat code would correctly estimate this regressionols height test_scoreols test_score heightreg test_score heightreg height test_score

Given your model equation `TestScorei = βο + β1 * Height + Ei`, the correct Stata command to…

Answered: Imagine I want to estimate the…

COMPREHENSIVE MICROSOFT OFFICE 365 EXCE

1st Edition

ISBN:9780357392676

Author:FREUND, Steven

Publisher:FREUND, Steven

Chapter6: Creating, Sorting, And Querying A Table

Section: Chapter Questions

Problem 10EYK

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Using the linear_predict function that you previously wrote, write a function linear_residuals that given the learned weights in the linear_regression function calculates the residuals vector. Your functions takes the following arguments as input: 1. X:A numpy array of the shape (N,d) where N is the number of data points, and d is the data dimension. Do not assume anything about N or d other than being a positive integer. 2. beta : Anumpy array of the shape (d+1,1) where d is the data dimension Bo Ba 3. Y: A numpy array of the shape (N,) where N is the number of data points. Your function should produce the e numpy array with the shape of (N,), whose i" element is defined as el) = yl) – (Bo + Bix? + Bzx + + Bax) In [ ]: def linear_residuals (X,beta,Y): assert X.ndim==2 N = X.shape[0] х.shape[1] assert beta.shape assert Y.shape d = (d+1,1) (N,) == == #3 your code here e e.reshape(-1) %3D assert e.size return e (np.arange ( 35).reshape ( 7,5) ** 13) % 20 2.** (-np.arange (6).reshape…
The output below estimates the following regression Treati = • Bo B1 Female; + ɛi Based on the results, which statement is most accurate. reg treat st_femalel Source SS df MS Number of obs = 619 F(1, 617) = 0.01 Model Residual .003006351 154.71428 1 .003006351 617 .250752479 Prob > F R-squared = 0.9128 = 0.0000 Total 154.717286 618 250351595 Adj R-squared Root MSE = -0.0016 = .50075 treat Coefficient Std. err. t P>|t| [95% conf. interval] st_female1 _cons .0051549 .5033557 .0470789 0410232 0.11 0.913 12.27 0.000 0872993 0976092 4227937 .5839177 In the treatment group, the fraction female is much higher than in the control group In the treatment group, the fraction female is slightly higher than in the control group In the treatment group, the fraction female is slightly lower than in the control group In the treatment group, the fraction female is much lower than in the control group
Interesting, Intersecting def squares_intersect (s1, s2): An axis-aligned square on the two-dimensional plane can be defined as a tuple (x, y, r) where (x, y) are the coordinates of its bottom left corner and r is the length of the side of the square. Given two squares as tuples (x1, yl, r1) and (x2, y2, r2), this function should determine whether these two squares intersect by having at least one point in common, even if that one point is the shared corner point of two squares placed kitty corner (The intersection of two squares can have zero area, if the intersection consists of parts of the one-dimensional edges.) This function should not contain any loops or list comprehensions of any kind, but should compute the result using only integer comparisons and conditional statements. This problem showcases an idea that comes up with some problems of this nature; it is actually far easier to determine that the two axis-aligned squares do not intersect, and negate that answer! Two squares…
Write a r programming code for random forest use mtcars dataset and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.
You work for an loan analytics organization have been tasked with writing a program that simulates an analysis of loan applications. Code a modular program that uses parallel arrays to store loan application credit scores (values generated between 300 and 900), score ratings (poor, fair, good, very good, or exceptional based on credit score) and loan status (approved or declined applications based on credit score). The program must do the following: The program must first ask the user for the number of loan applications that will be in the simulation. This must be done by calling the getNumLoanApplications() function (definition below). After the input of the number of accounts, the program must use loops and random numbers to generate the credit score, score rating, and application result for each loan application in parallel arrays. These arrays must not be declared globally (major error). The following arrays must be declared and populated: creditScores[] - an array of type int…
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function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= Which one is true? a. When the N increases, the approximated graph tends to be closer to the original square wave b. When the L increases, the approximated graph tends to be closer to the original square wave c. When the L decreases, the approximated graph tends to be closer to the original square wave d. When the N decreases, the approximated graph tends to be closer to the original square wave e. None
function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend 2-Run the function for L=3 and N=3 and upload the image of the graph plotted use matlap
function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= In which case the bandwidth decreases? a. When N decreases b. When L increases c. When L decreases d. When N increases e. None
Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order.You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1. Make sure to add a constant using sm.add_constant(X) Your file should print the results in this way: print(mod.params.round(2)) print(mod.pvalues.round(2)) print('The smallest p-value is for sqft') Please use sm.add_constant(X)! In Jupyter Notebook please! Please use the sys module!
Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order.You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1. Make sure to add a constant using sm.add_constant(X) Your file should print the results in this way: print(mod.params.round(2)) print(mod.pvalues.round(2)) print('The smallest p-value is for sqft')
Interesting, intersecting def squares_intersect(s1, s2): A square on the two-dimensional plane can be defined as a tuple (x, y, r) where (x, y) are the coordinates of its bottom left corner and r is the length of the side of the square. Given two squares as tuples (x1, y1, r1) and (x2, y2, r2), this function should determine whether these two squares intersect, that is, their areas have at least one point in common, even if that one point is merely the shared corner point when these two squares are placed kitty corner. This function should not contain any loops or list comprehensions of any kind, but should compute the result using only integer comparisons and conditional statements.This problem showcases an idea that comes up with some problems of this nature; it is actually far easier to determine that the two squares do not intersect, and negate that answer. Two squares do not intersect if one of them ends in the horizontal direction before the other one begins, or if the same…

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COMPREHENSIVE MICROSOFT OFFICE 365 EXCE

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Imagine I want to estimate the following regression in Stata TestScorei = . Bo B₁ Height; + ɛi What code would correctly estimate this regression ols height test_score ols test_score height reg test_score height reg height test_score