Math221

The use of a scatterplot was also used to provide a visual representation of the correlations explained above, which can be seen in figure 1 this type of “association” would be described as more curvilinear because it does not go in a straight line or

The Barbie Bungee lab was conducted in order to find the association between the amount of rubber bands and the distance the Barbie bungeed. Before performing the final experiment, the group conducted an initial investigation to get data that could be analyzed to examine the comparison from the amount of rubber bands to the length Barbie was able to bungee. In the investigation rubber bands would gradually be added one by one starting at two rubber bands. Each time a rubber band was added, three trial bungees were done and the lengths the barbie dropped were recorded. Using data collected from our background investigation, the group used excel to create a sheet displaying the data in a table, a graph showing the correlation constant, the line of best fit. The line of best fit was in slope-intercept form (y=mx+b) where y represents the length of the trial average; m represents the slope

Researchers have studied the correlation between birth defects and tobacco. Correlation is not about cause and effect but rather how a relationship between two variables works

However, a correlation between two variables does not necessarily imply causation but for a causal relationship to exist between two variables there must be a correlation between the variables (Solomon W. Golomb, 2005). When predicting the Grade Point Averages, correlation might not be a good test for its prediction. This is because there is no GPA is not only influenced by intelligent quotient but it is also influenced by other external factors like Education background, family background, social and political environment among other factors. Other statistical tests may include the use of rating scales to rate qualities that cannot be directly rated through correlation by use of variables like good, fair, and excellent among others. Coefficient of correlation might also be used as a technique of predicting the Grade Point Averages. This refers to the main result of a correlation whereby it predicts significant and smaller changes among variables by use of scale r that ranges from +1.0 to -1.0.

Answer: A positive correlation means that increases in the value of one variable are associated

At a glance scatter plots show whether a relationship exists between two sets of data. This data will determine correlations between students taking the SAT and ACT. Because this scatter plot is falling from left to right it has a negative slope, so therefore there is a negative correlation between these two sets of data. Although these points are falling, it is not a clear negative relationship since the clustered points are not in a straight line. Therefore, this relationship is a weak, negative relationship.

The textbook define the correlation coefficient as "a measure that is designed to indicate the strength of the relationship between two variables" (UBC Real Estate Division, 2009). The textbook also states "the correlation coefficient may be positive, negative, or zero" (UBC Real Estate Division, 2009). A strong relationship implies that there is a relationship between the two variables and "as one variable increases (decreases), the other the variable will increase (decrease)" (Estate Division, 2009). A strong relationship would have a correlation coefficient value of +1 or -1.

There seems to be a general tendency to attribute causation with correlation. People often conclude that causation exist when the association between variables is seemingly obvious but this is a common mistakes that people makes. It is important to note that correlation does not prove causation. Correlation just simply implies that a relationship between variables exists and that there is a possibility of a cause and effect relationship; however, it does not prove causation actually exist. Correlation do not indicate in which direction the relationship works such as Variable A could cause Variable B, or vice versa, or may a third (or fourth, or even fifth) variable may be the cause of both of the other related variables.

the correlation is 0.668; the equation of regression is CREDITS=11.7475*AGE-174.356; the slope is 11.7475 which is positive; when the predictor variable AGE increase, the response variable CREDITS also strongly increase; for instance, when AGE increase by 1, the CREDITS will increase 11.7475. There are some outliers may affect the correlation. Based on the graphs and data above, we can find out a student who is older with a litter lower GPA, but has very higher credits; the student with higher credits also has high GPA.

139). It is an assumed linear association between two variables that is quantified by a single statistical number. The correlation coefficient measures the strength of the association between the two variables, 0 means there is no correlation, 1 means there is a perfect positive correlation, -1 means there is a perfect negative correlation. "The sample correlation coefficient, denoted r, ranges between -1 and +1 and quantifies the direction and strength of the linear association between the two variables. The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other) (bumc.bu.edu, 2013)." Correlation is the most appropriate because it is easy to calculate and easy to

"A correlation is a statistical to determine the tendency or pattern for two (or more) variables or two sets of data to very consistently" (Creswell, (2012). any

An example of a positive correlation is the use of manners and age. The use will increase as age does. They both increase in a consistent way.

The data is presented in such a way as to highlight correlations that support this Jen science. The graphs

The R2 value is a statistical measurement of how close the data is from the linear regression line and indicates the variation in the values from the relationship of two variables. In graph 1, the R2 value is 0.60497, which indicates a moderate positive correlation. This suggests that around 60% of the variation in the mean age among different football teams can be predicted from the relationship between the number of wins and the mean age of the team. Contrarily, 40% of the variation in mean team ages cannot be

The scatter plot of Credit balance ($) versus Size show that the slope of the 'best fit' line is upward (positive); this indicates that Credit balance varies directly with Size. As Size increases, Credit Balance also increases vice versa.