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Complete Linear Regression Analysis in Python

In this free online course, learn about the methods and techniques involved in Linear Regression Analysis in Python.

Free Course
Building linear regression models from a business perspective will make you an expert in solving business-related problems. In this free online course, learn the components of a comprehensive data dictionary, the steps in handling and interpreting qualitative variables in a linear model, and the methods of treating outliers. Boost your linear regression analysis in Python knowledge and skills by studying this comprehensive course.





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Do you want to be an expert in solving business problems using linear regression in Python? This course covers all the steps you should take when solving business problems using linear regression in Python. The importance of what happens before and after running the analysis. You will have to ensure that you have the correct data before running the analysis and the ability to judge the efficacy of a model after running the analysis. This course also gives you the ability to interpret the results in a way that helps the business. Discover that qualitative data refers to a variable that cannot assume a numerical value, but that can be classified into two or more non-numeric categories. In contrast, quantitative data refers to a variable that can be measured numerically. The course will discuss the essential libraries in Python and the different types of string functions that you will use in Python.

At first, you will be introduced to the concept of parenthesis, which is always recommended when using multiple arithmetic operators in Python. Then, you will discuss Tuples in Python, which are similar to lists. Their difference is that you can not modify them once they have been created, which is why they are often classified as immutable. You will gain insight into the fact that Numpy packages almost always use an all-numerical computation using Python. It provides high-performance vectors, matrix and higher-dimensional data structures for Python. Discover that descriptive statistics refers to the methods used to organise, display, and describe data through tables, summary measures, and graphs. You will then understand that inferential statistics consists of methods that use sample results to help make decisions or predictions about a population. The course will discuss the different types of Seaborn plot functions and various examples of Python’s descriptive statistics tools.

Next, you will learn that the frequency distribution of qualitative data lists all categories and the number of elements belonging to each category. Gain insight into machine learning, which refers to computers’ programming to optimise a specific performance criterion using example data or past experience. It can automatically detect patterns in data and use them to predict future outcomes of interest. You will then understand that for a distribution curve skewed to the right, the value of the mean is the largest, the value of the mode is the smallest, and the value of the median lies between these two. Discover that if a continuous random variable has a distribution with a symmetric and bell-shaped graph, it is classified as having a normal distribution. Finally, you will study data analysis, linear methods, linear regression, and multiple linear regression. This course will be of interest to business managers, executives or students interested in learning about linear regression. Why wait? Start this course today and become a linear regression and problem-solving expert.

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