It contains data on brain mass in different species versus glia-neuron ratio, the latter being a measurement of brain metabolism as the glia provides the metabolic needs of the neurons. The relationship between THE LOGARITHM of the brain mass (in the third column) and Glia-neuron ratio (fourth column) appears linear and it is these two variables that we wish to analyze via linear regression. We would like to know if the human brain fits the trend from the other species. Towards this end we will perform the regression on all species EXCEPT humans (Homo sapiens). Again, throw out the human data from your analysis. You will however need the human numbers for some of the questions. What is the predicted glia-neuron ratio for species with ln(brainmass) equal to that of humans (Homo sapiens)? What is the standard error of the predicted mean glia-neuron ratio for all species with ln(brain mass) equal to that of humans? What is the lower & upper bound of the 95% confidence interval for the predicted mean glia-neuron ratio for all species with ln(brain mass) equal to that of humans
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.
It contains data on brain mass in different species versus glia-neuron ratio, the latter being a measurement of brain metabolism as the glia provides the metabolic needs of the neurons. The relationship between THE LOGARITHM of the brain mass (in the third column) and Glia-neuron ratio (fourth column) appears linear and it is these two variables that we wish to analyze via linear regression. We would like to know if the human brain fits the trend from the other species. Towards this end we will perform the regression on all species EXCEPT humans (Homo sapiens). Again, throw out the human data from your analysis. You will however need the human numbers for some of the questions.
What is the predicted glia-neuron ratio for species with ln(brainmass) equal to that of humans (Homo sapiens)?
What is the standard error of the predicted mean glia-neuron ratio for all species with ln(brain mass) equal to that of humans?
What is the lower & upper bound of the 95% confidence interval for the predicted mean glia-neuron ratio for all species with ln(brain mass) equal to that of humans?
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 21 images