Trayce Gray Essays - Covariance And Correlation, Data Analysis

Trayce Gray Intro to Stats 11-5-17 Andy Garza Critical Thinking Unit 2 The main purpose of this module is to teach the reader to determi ne whether a correlation exists , and if there is one , how strong the correlation between two variables is. Another purpose of this unit is to teach the reader how to utilize data to make the best possible prediction in response to the variables relationship. The key question at the heart of this module is: How do two given variables correspond t o each other and how can the reader express the strength and accuracy of that given relationship. The most important information in this module is teaching the reader how to find the correlation coefficient of r . This knowledge is critical in determining how two given variables coordinate to one another . Additionally, This module teaches the reader how to accurately portray the results of correlation regarding the the relationship between two given variables . The most important inferences in this module are that correlation coefficient is used to determine how well two variables corre spond to each other, and with that measurement of correlation the reader can make data based predictions. . The key concepts we need to consider are correlation and causation. Correlation does not always mean two variables are connected . determining this is a key concept of correlational statistics. Although many times a strong correlation can point to a connection, in the real world things aren't as black and white. The main assumptions underlying the authors thinking are that the reader will understand the concept of correlation and causation completely . I t is easy for a person to falsely conclude that correlation naturally means a relationship is existent between two variables . The main point of view presented in this article is that the correlation coefficient should be used to find the strength of a relationship between two given variables . If people take seriously what the author is saying, some of the important implications are that we will be able to better use and understand the correlation coefficient. And from the data found with it we will be able to better interpret the relationships between variables. If we fail to accept what the author is saying, some of the important implications are that we will fail to determine the strength of relationship (if it exists) between two variables . If people take seriously what the author is saying, some of the important implications are that the reader will be better equipped to utilize data and determine the strength of relationship between two given variables .