Question

I'm doing a project on Wild Horse Population, versus the amount of adoptions each year.

What are some ways that the data could relate to each other statistically? working on some H0 and H1 hpotheses

Answer 1

In order to obtain statistical evidence about the Statistical relationship between the Wild Horse Population versus the amount of adoptions each year consider the following steps :-

Since, we have to to come at some statistically valid conclusions which will talk about the relation between these two variables, we can use Regression Analysis.

1. Using the given data on two variables, obtain the Karl Pearson's Correlation Coefficient.

The value of r and its sign will give you an idea about the extent of linear association between the two variables and the direction respectively.

2. Also, obtain the Scatterplot for the given two variables.

And see what it tries to indicate.

A Scatterplot gives us a rough idea about the type of relationship between the two variables. If it's linear, we can move ahead with Linear Regression Analysis. If it's curvilinear then we have to consider some other Regression Models to explain the relationship better.

3. The correlation coefficient r obtained in step 1 and the relationship obtained through Scatterplot, if these are fair enough and the value of r is high enough then we can move for Linear Regression Analysis.

4. Now, using various Statistical Softwares, or using the Least Squares Method , we can easily obtain the Regression Model for the given two variables.

Here, X: the wild life Population and Y : Amount of adoption each year.

Here, X is independent Variable or Regressor Variable and Y is dependent variable or Response Variable.

Using the Regression Model, we can also predict the amount of adoptions in other ( previous or upcoming) years if we have the values of wild horse Population.

Further, on obtaining the Regression model and the estimates for slope and Intercept, we can carry out hypothesis testing for checking the significance of our estimates as well as our model.

This answers your question.

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Best wishes for your project.