In the following, the parameters a, b, and care constants. For every item, create superimposed (i.e. on the same xy-axes) graphs of the given functions when the constants are varied. Take note that this entails multiple plots, one for each of the varied constant (see explanation in item 1 below). You may choose the representative values you want to use for plotting. You may want to explore the effect of the magnitude (very large vs. intermediate vs. very small) and the signs (positive, negative, or possibly zero) of the constants. Place all your graphs and the accompanying explanations in a single PDF file and upload in this file submission bin.
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.
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