NGUYENC M3 A2
LASA 1 : Linear Regression
Christine Nguyen
Module 3 Assignment 2
LASA 1: Linear Regression 1
In this assignment, I am to graph, report, and giving an explanation of the correlation between the data that was given. The data that I am to graph are between the variables of the number of hours study and the exam scores. The linear correlation coefficient and the linear regression equation produced from the Excel spreadsheet are f(x) = 1.5607693309x + 55.769550074 with the Pearson 's r value of .7850471558. With the scatter graph that I have made from the data given, I can say that Pearson 's r in this particular assignment is positive and implying a strong relationship between the two. There 's no negative sign in front of the value .785 let us know that in this study the Pearson ' r is positive. Since the Pearson 's r is positive, we can conclude that when the study hours increases, the exam scores increases as well, as when study hours decreases, the exam scores will also decreases. With the line connecting most of the scatter points on the graph show the strong relationship between them. The dots seem to come together to form a straight line rather then scatter all over the place where it is hard to form a straight line. When looking at the graph, the implication of the correlation found between the variables are to be of a positive correlation because the slopes are upward from zero.
LASA 1: Linear Regression 2
The increase in the number of hours study are correlated with the increase in the exam scores. Similarly, decreases in the number of hours study are correlated with the decrease in the exam scores. The correlation imply a casual relation between the two exist if the occurrence of the first cause the
Christine Nguyen
Module 3 Assignment 2
LASA 1: Linear Regression 1
In this assignment, I am to graph, report, and giving an explanation of the correlation between the data that was given. The data that I am to graph are between the variables of the number of hours study and the exam scores. The linear correlation coefficient and the linear regression equation produced from the Excel spreadsheet are f(x) = 1.5607693309x + 55.769550074 with the Pearson 's r value of .7850471558. With the scatter graph that I have made from the data given, I can say that Pearson 's r in this particular assignment is positive and implying a strong relationship between the two. There 's no negative sign in front of the value .785 let us know that in this study the Pearson ' r is positive. Since the Pearson 's r is positive, we can conclude that when the study hours increases, the exam scores increases as well, as when study hours decreases, the exam scores will also decreases. With the line connecting most of the scatter points on the graph show the strong relationship between them. The dots seem to come together to form a straight line rather then scatter all over the place where it is hard to form a straight line. When looking at the graph, the implication of the correlation found between the variables are to be of a positive correlation because the slopes are upward from zero.
LASA 1: Linear Regression 2
The increase in the number of hours study are correlated with the increase in the exam scores. Similarly, decreases in the number of hours study are correlated with the decrease in the exam scores. The correlation imply a casual relation between the two exist if the occurrence of the first cause the
References: 1 Wikipedia: Free Encyclopedia, September 2013. http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation 2 Pearson 's r Correlation Coefficient Explanation. February 22, 2011, Patrick JMT 3 Interpreting the Correlation coefficient. July 13, 2012., Lawrence Academy Math