Python Programming: An Introduction to Computer Science
Python Programming: An Introduction to Computer Science
3rd Edition
ISBN: 9781590282779
Author: John Zelle
Publisher: Franklin Beedle & Associates
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Chapter 7, Problem 1D

a)

Explanation of Solution

Simple decision:

  • In python, decisions can be implemented using “if” statements.
  • Python handles the following type of decisions,
    • Simple decision
    • Two-way decision
    • Multi-way decision
  • Simple decisions can be implemented by a simple “if” statement.
  • Example:

    #Initialize the variable

    a=1

    #Check the condition using “if” statement

    if (

b)

Explanation of Solution

Two-way decision:

  • In python, decisions can be implemented using “if” statements.
  • Python handles the following type of decisions,
    • Simple decision
    • Two-way decision
    • Multi-way decision
  • Two-way decisions can be implemented by “if-else” statement.
  • Example:

#Initialize the variables

a=5

b=3

#Check the condition using “if” statement

if(a<b):

�...

c)

Explanation of Solution

Multi-way decision:

  • In python, decisions can be implemented using “if” statements.
  • Python handles the following type of decisions,
    • Simple decision
    • Two-way decision
    • Multi-way decision
  • Multi-way decision can be implemented by “if-elif-else”, and nested “if-else” statements.
  • Example:

#Initialize the variables

a=5

b=3

c=6

#Check the condition using “if” statement

if(a<b && a<c):

  #Print “a” value

  print(a)

#Check the condition using “elif” statement

elif(b<c):

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Can you help me solve this problem using Master's Theorem:Solve the recurrence relation f(n) = 3af(n/a) + (n + a)2 with f(1) = 1 and a > 1 byfinding an expression for f(n) in big-Oh notation.
here is example 7.6## Example 7.6 Suppose the sample population is χ 2 (2), which is non-normal but with same variance 4. ▶ Repeat the simulation, but replacing the N(0, 4) samples with χ 2 (2) samples. ▶ Calculate the empirical confidence level.(Empirical confidence level) n <- 20 alpha <- 0.05 UCL <- replicate(1000, expr = { x <- rchisq(n,df=2) (n-1)*var(x)/qchisq(alpha,df=n-1) }) sum(UCL >4) mean(UCL > 4) ## t.test function n <- 20 x <- rnorm(n,mean=2) result <- t.test(x,mu=1) result$statistic result$parameter result$p.value result$conf.int result$estimate
using r language
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