Chapter 5. Linear Regression as a Fundamental Descriptive Tool
Chapter 6. Correlation vs. Causality in Regression Analysis
Initial Postings: Read and reflect on the assigned readings for the week. Then post what you thought was the most important concept(s), method(s), term(s), and/or any other thing that you felt was worthy of your understanding in each assigned textbook chapter.Your initial post should be based upon the assigned reading for the week, so the textbook should be a source listed in your reference section and cited within the body of the text. Other sources are not required but feel free to use them if they aid in your discussion.
Also, provide a graduate-level response to each of the following questions:
[Your post must be substantive and demonstrate insight gained from the course material. Postings must be in the student’s own words – do not provide quotes!]
[Your initial post should be at least 450+ words and in APA format (including Times New Roman with font size 12 and double spaced). Post the actual body of your paper in the discussion thread then attach a Word version of the paper for APA review]
We have discussed different types of numerical regressions that can be done to solve problems in economics and business.
Linear regression is used to find the relationship between two variables. Linear regression can be used to predict the future values of a variable based on its past values. Linear regression is also known as ordinary least square (OLS) or ordinary least square estimation (OLSE).
Linear regression can be used in many situations, such as predicting demand for a product based on its price, or estimating the cost of building a house by measuring how many rooms it needs and adding up all those costs into one number.
Polynomial regression is a type of regression that uses a polynomial function of degree one or more. It’s used to model the trend in data, and can be applied to time series or cross-sectional data (i.e., collected at different times).
Polynomial regression is one of two types of parametric models in which you can use an ordinary least squares method to fit your regressor with values for each observation (x) from your dataset as well as for their mean and variance:
`y=ax+b` where `a` represents the intercept term and `b` represents all other terms in order from smallest to largest with respect to y value at x=0.
Logarithmic regression is used for data that is growing exponentially. For example, if you have a dataset and want to find the best fitting model, logarithmic regression can be used to find out how fast your data changes over time. This type of regression is useful when there are not many observations in your dataset and there isn’t enough information about each observation (such as age).
Exponential regression is a method that is used to find the relationship between two variables. In this type of regression, you have a dependent variable and an independent variable. The independent variable can be anything that you want it to be (e.g., age). The dependent variable will be whatever value your model predicts when it’s given input from your independent variable(s).
Exponential regressions are useful because they allow you to estimate relationships between time and other factors without needing large samples or expensive equipment (like supercomputers). In most cases, exponential regressions can be done on desktop computers with simple statistical software like R or SPSS; however there may be cases where this isn’t possible due either poor internet connections or slow processors/hard drives in laptops/desktops–for example if you’re doing research in remote areas where there aren’t many people who can help set up your experiment properly before hand!
Nonlinear regression is used to model nonlinear relationships. For example, if you want to predict the sales volume of a product based on its price and other factors, nonlinear regression can help you do this.
Let’s say that we have two data sets: one in which there are two variables (product and price), and another where there are three variables (product, price, and time). We want to find out how much revenue each product will generate over time given its price point relative to other products at similar prices or even lower ones. In order for us to do this using linear methods like ordinary least squares (OLS), we must choose some values from our dataset so that the equation looks valid when plugged into OLS:
In this section we have discussed different types of numerical regressions that can be done to solve problems in economics and business. We have discussed linear regression, polynomial regression, logarithmic regression and exponential regression.
Linear Regression: In linear regression we use straight lines with a constant intercept and slope to model data that follow a straight line. For example if you want to model the economic growth rate of your country over time then you would use a linear function for this purpose. The formula for this type of regression is given below:
The next section will discuss further applications of regression, how to model and analyse the data, and some tricks that can be used to get the most out of your model. A linear regression is a simple way to explore relationships between two variables but it does not allow for much insight into other characteristics about those variables such as their distribution or trend.
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