25. Colorectal cancer (CRC) is the third most commonly diagnosed cancer among Americans (with nearly 147,000 new cases), and the third leading cause of cancer death (with over 50,000 deaths annually). Research was done to determine whether there is a link between obesity and CRC mortality rates among African Americans in the United States by county. Below are the results of a least-squares regression analysis from the software StatCrunch. Simple linear regression results: Dependent Variable: Mortality.rate Independent Variable: Obesity.rate Mortality.rate = 13.458199 - 0.21749489 Obesity.rate Sample size: 3098 R (correlation coefficient) = -0.0067 R-sq = 4.5304943E-5 Estimate of error standard deviation: 111.20661 %3! Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-Value Intercept 13.458199 15..9797735 +0 3096 0.84220207 0.3997 Slope -0.21749489 0.5807189 +0 3096 -0.37452698 0.708 Analysis of variance table for regression model: Source DF MS F-stat P-value Model 1734,7122 1734.7122 0.14027046 0.708 Error 3096 3.8287952E7 12366.91 Total 3097 3.8289688E7 What is the equation to predict mortality rates from obesity rates? O Mortality.rate = 13.458199 - 0.21749489 Obesity.rate O Obesity.rate = 13.458199 - 0.21749489 Mortality.rate O Mortality.rate = 13.458199 + 0.21749489 Obesity.rate O Mortality.rate = 13.458199 - 0.0067 Obesity.rate 26. Colorectal cancer (CRC) is the third most commonly diagnosed cancer among Americans (with nearly 147,000 new cases), and the third leading cause of cancer death (with over 50,000 deaths annually). Research was done to determine whether there is a link between obesity and CRC mortality rates among African Americans in the United States by county. Below are the results of a least-squares regression analysis from the software StatCrunch. Simple linear regression results: Denendent Varishla: Martalitu rata Remaining: 48:59 Start: 10:52 PM

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Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.