FDA QB Answers PDF
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Summary
This document provides answers to questions about data analysis, including key steps in the data analysis process, predefined constants in R, machine-generated unstructured data, basic arithmetic operations in R, and definitions of continuous variables, arrays, etc. It's likely a study guide or exam material focused on data manipulation.
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1.Lable the key steps in data analysis process A. Key Steps in the Data Analysis Process: * Data Collection: Gathering raw data from various sources. * Data Cleaning: Identifying and handling missing values, outliers, and inconsistencies. * Data Transformation: Converting data into a suitable format...
1.Lable the key steps in data analysis process A. Key Steps in the Data Analysis Process: * Data Collection: Gathering raw data from various sources. * Data Cleaning: Identifying and handling missing values, outliers, and inconsistencies. * Data Transformation: Converting data into a suitable format for analysis (e.g., scaling, normalization). * Exploratory Data Analysis (EDA): Summarizing and visualizing data to gain insights. * Model Building: Developing and selecting appropriate statistical or machine learning models. * Model Evaluation: Assessing the performance of the model using appropriate metrics. * Interpretation and Communication: Drawing meaningful conclusions and communicating findings effectively. 2. what are the predefined constrains in R A.Predefined Constants in R: * pi: Represents the mathematical constant pi (approximately 3.14159). * LETTERS: A character vector containing all uppercase letters of the alphabet. * letters: A character vector containing all lowercase letters of the alphabet. * Inf: Represents positive infinity. * NA: Represents missing values. * NaN: Represents "Not a Number" (e.g., result of 0/0). 3. A. Machine-Generated Unstructured Data: * Social Media Posts: Tweets, Facebook posts, Instagram comments. * Sensor Data: Data from IoT devices, weather stations, medical equipment. * Log Files: Records of system events, user activity, and application errors. * Audio/Video Recordings: Speech, music, videos. 4. Basic Arithmetic Operations in R A. * +: Addition * -: Subtraction * *: Multiplication * /: Division * ^: Exponentiation * %%: Modulus (remainder after division) * %/%: Integer division 5.DEFINE Continuous Variable: A. A continuous variable can take on any value within a given range. * Example: Height, weight, temperature, time. 6.DEFINE Array in R: A. An array is a multi-dimensional data structure in R. * Example: my_array
