Learn how to apply statistical information and quantitative methods to the workplace by examining techniques for gathering, analyzing, and interpreting data that is applicable to many fields, from anthropology to hedge fund management.
This course will introduce you to business statistics: the application of statistics in the workplace. Statistics is how we gather, analyze, and interpret data. If you have taken a statistics course in the past, you may find some of the topics in this course familiar. You can apply statistics to any number of fields, from anthropology to hedge fund management, because many of us interpret data best when it is presented in an organized fashion. Here, we will look at summary statistics, which give an overview of a data set, such as the average score on an exam. However, the average does not always tell the entire story since half of the students could have gotten 100 on the exam and the other half 60. Using statistics, we can learn a lot more about how data is organized. To do that, we will use statistical tools to analyze data, draw conclusions, and make predictions about the future. The course will begin with data distributions, followed by probability analysis, sampling, hypothesis testing, inferential statistics, and regression.
- Unit 1: Introduction to Statistical Analysis
- Unit 2: Counting, Probability, and Probability Distributions
- Unit 3: The Normal Distribution
- Unit 4: Sampling and Sampling Distributions
- Unit 5: Estimation and Hypothesis Testing
- Unit 6: Correlation and Regression
- Explain the importance of statistics and statistical analysis for applicability to business scenarios;
- Explain the differences between data types (quantitative and qualitative data) and their application to real-world situations;
- Apply the elements of descriptive statistics to solve problems and understand datasets;
- Create graphs and visual representations of data;
- Calculate metrics based on the properties of various data distributions;
- Apply the concept of a random variable by differentiating a population from a sample;
- Relate the central limit theorem to sample size and normal distribution;
- Describe different sampling methods;
- Estimate intervals over which the population parameter could exist using sample data;
- Use hypotheses to test population parameters using one or two samples;
- Explain components and values of the linear regression model; and
- Explain regression lines and the coefficient that shapes the line.