You are given data to analyze for a new chemotherapeutic to eliminate malignant tumors and must determine if a significant relationship of any kind exists for the latest drug that was proposed. This new drug, XA98 has shown promising results across multiple repeated cross-sectional studies and now combined evidence was gathered to see if any relationships stand out among the multiple tests that were conducted in the past. The data for XA98 to combat specific cancer tumors can be found below, establish whether or not any significant relationships exist among the variables and describe how strong these relationships are; hint: think about using linear regression to establish any relationships that can be described with a simple model and pinpoint which variables are critical for understanding these relationships. Feel free to use any resources from the web or from your notes. The dataset consists of four variables, in vivo fluorescence for XA98 (photon arrival time in picoseconds); tumor size (in volume mm3); ultrasonography for XA98 (in hertz); excitation light for XA98 (in nm) Vivo fluor: 123, 34, 56, 78, 29, 19, 101, 283, 98, 76 Tumor size: 23, 43, 51, 56, 72, 34, 98, 12, 34, 23 Ultrasono: 34, 44, 54, 44, 34, 44, 45, 54, 67, 88 excitation: 110, 112, 114, 112, 113, 114, 224, 112, 115, 111
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.
You are given data to analyze for a new chemotherapeutic to eliminate malignant tumors and must determine if a significant relationship of any kind exists for the latest drug that was proposed. This new drug, XA98 has shown promising results across multiple repeated cross-sectional studies and now combined evidence was gathered to see if any relationships stand out among the multiple tests that were conducted in the past. The data for XA98 to combat specific cancer tumors can be found below, establish whether or not any significant relationships exist among the variables and describe how strong these relationships are; hint: think about using linear regression to establish any relationships that can be described with a simple model and pinpoint which variables are critical for understanding these relationships. Feel free to use any resources from the web or from your notes. The dataset consists of four variables, in vivo fluorescence for XA98 (photon arrival time in picoseconds); tumor size (in volume mm3); ultrasonography for XA98 (in hertz); excitation light for XA98 (in nm)
Vivo fluor: 123, 34, 56, 78, 29, 19, 101, 283, 98, 76
Tumor size: 23, 43, 51, 56, 72, 34, 98, 12, 34, 23
Ultrasono: 34, 44, 54, 44, 34, 44, 45, 54, 67, 88
excitation: 110, 112, 114, 112, 113, 114, 224, 112, 115, 111
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