Construct the data using the sample() function: # change n <- your id/100 n <- 12345/100 a <- sample(n, 30, replace = T) b <- sample(n, 30, replace = T) c <- sample(n, 30, replace = T) d <- sample(n, 30, replace = T) mydata <- data.frame(a,b,c,d) head(mydata) Create a loop over the columns and create a histogram of the data in each column. • First, set up a 2 by 2 plotting space with par(mforw()) • Next, write a loop to create four histograms for a,b,c, and d.

Construct the data using the sample() function: # change n <- your id/100 n <- 12345/100 a <- sample(n, 30, replace = T) b <- sample(n, 30, replace = T) c <- sample(n, 30, replace = T) d <- sample(n, 30, replace = T) mydata <- data.frame(a,b,c,d) head(mydata) Create a loop over the columns and create a histogram of the data in each column. • First, set up a 2 by 2 plotting space with par(mforw()) • Next, write a loop to create four histograms for a,b,c, and d.

Np Ms Office 365/Excel 2016 I Ntermed

1st Edition

ISBN:9781337508841

Author:Carey

Publisher:Carey

Chapter3: Performing Calculations With Formulas And Functions

Section: Chapter Questions

Problem 3.9CP

See similar textbooks

Similar questions

What are the advantages of the "Union Find Disjoint" data structure?
get_total_cases() takes the a 2D-list (similar to database) and an integer x from this set {0, 1, 2} as input parameters. Here, 0 represents Case_Reported_Date, 1 represents Age_Group and 2 represents Client_Gender (these are the fields on the header row, the integer value represents the index of each of these fields on that row). This function computes the total number of reported cases for each instance of x in the text file, and it stores this information in a dictionary in this form {an_instance_of_x : total_case}. Finally, it returns the dictionary and the total number of all reported cases saved in this dictionary.
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= Run the function for L=3 and N=7 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= 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
#The Iris Dataset import sklearn.datasetsimport matplotlib.pyplot as plt import numpy as np import scipy iris = sklearn.datasets.load_iris() Write a function that takes in an index i and prints out a verbose desciption of the species and measurements for data point i. For example:Data point 5 is of the species setosaIts sepal length (cm) is 5.4Its sepal width (cm) is 3.9Its petal length (cm) is 1.7Its petal width (cm) is 0.4
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
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 1-Run the function with L=3 and N=1 and upload the plotted graph 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 2-Run the function for L=3 and N=3 and upload the image of the graph plotted use matlap
Problem set4.
Tails Function Purpose: Produce a new column-based ( e.g. dict[str, list[str]] ) table with only the first N (a parameter) rows of data for each column. * Function name: Tails Parameters: 1. dict[str, list[str]] - a column-based table of data that_will not be mutated_ 2. int - The number of "rows" to include in the resulting list * Return type: dict[str, list[str]] Implementation strategy: 1. Establish an empty dictionary that will serve as the returned dictionary this function is building up. 2. Loop through each of the columns in the first row of the table given as a parameter. 1. Inside of the loop, establish an empty list to store each of the first N values in the column. 2. Loop through the first N items of the table's column, 1. Appending each item to the previously list established in step 2.1. 3. Assign the produced list of column values to the dictionary established in step 1. 3. Return the dictionary.
A = [1 1; 1 -1];b = [80; 20]; A_inverse = inv(A);x = A\b; disp('A inverse is');disp(A_inverse);disp(['x1 is ', num2str(x(1)), ' and x2 is ', num2str(x(2))]); if isequal(round(x), [50; 30]) disp('Success');else disp('Incorrect: Please try again');end. Output matlab. .
1. Write a PL/SQL program using an explicit cursor that displays all patients PLCO_ID numbers from the colo_proc_data table on the screen if the patients’ procedure is biopsy, the procedure is staging, interval status of linked cancer is never screened, the procedure type is invasive Scopes, biopsies, & surgeries, the procedure type is Lymphadenectomy/Lymph Node Sampling, and the procedure result is abnormal, confirmed metastasis.