Database Systems: Design, Implementation, & Management
Database Systems: Design, Implementation, & Management
12th Edition
ISBN: 9781305627482
Author: Carlos Coronel, Steven Morris
Publisher: Cengage Learning
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Chapter 14, Problem 19RQ
Program Plan Intro

Data mining:

Data mining is a tool for analyzing massive amount of data to uncover hidden trends, patterns, and relationships to form computer model.

  • • It is used for analyzing the data from different views and shortened into the useful information.
  • • In other words, it can be said data mining is a process that helps in finding patterns or correlations between multiple fields in severe relational databases.
  • • For example, a data mining tool could be used to examine customer purchase past data.

Predictive analytics:

  • • Predictive analysis is focuses on predicting outcomes of future data with a high degree of accuracy.
  • • It creates advanced model for end user using statistical tool. The tool answers questions about future data occurrences.
  • • For example, a predictive model could be used to forecast future customer behavior, such as a customer response to a target marketing campaign.

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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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