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 11, Problem 1PE
Program Plan Intro

Modified statistics program

Program plan:

  • Import the necessary modules.
  • Define the “mean()” function,
    • Returns mean value to the caller.
  • Define the “StdDev()” function,
    • Returns standard deviation value to the caller.
  • Define the “meanStdDev()” function,
    • Returns average standard deviation value to the caller.
  • Define the “median()” function,
    • Returns median value to the caller.
  • Define the “main()” function,
    • Get comma separated values from the user.
    • Separate the value based on “,” using “split()”.
    • Create a “for” loop to iterate over comma separated values.
      • Assign the value.
        • Print the options.
        • Create sentinel loop to get numbers.
        • Prompts the user for the type of operation to perform.
        • Check whether the operation is “Avg” using “if” .
          • If it is true, print mean value return from “mean()” function.
        • Otherwise, check whether the operation is “Med” using “elif”.
          • If it is true, print median value return from “median()” function.
        • Otherwise, check whether the operation is “Std” using “elif”.
          • If it is true, print standard deviation return from “stdDev()” function.
        • Otherwise, check whether the operation is “AvgStd” using “elif”.
          • If it is true, print average standard deviation value return from “meanStdDev()” function.
        • Otherwise, check whether the operation is empty (‘ ‘)using “elif”.
          • If it is true, use “break” to exit.
          • Otherwise print “Please try again”.
  • Call the “main()” function.

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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
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