While it is extremely common to hear the word "data" in business today, what is less common is an understanding of how to collect the right data and then apply it to solving business problems. In this course, you will learn foundational concepts in statistics and how to collect and interpret data while applying statistics and statistical thinking to business problems. Additionally, in the practice of business statistics, it is essential to capture accurate data but also to communicate that data clearly and effectively. You will then explore methods of presenting this type of data and try it for yourself. Lastly, it may seem far-fetched to describe numeric values collected during a business day as a story, but when quantitative data is compiled into a visual tool such as a table or graph, it can indeed tell a story about that day’s business activity. In this course you will examine how to display quantitative data through tables as well as best practices you should follow to determine which method is the best choice for communicating the data at hand.
In order to uncover insights in data, it is important to draw conclusions about the population that is being studied using numerical measures. In this course, you will identify various numerical measures including percentiles, range, variance, and standard deviation. You will then see how to visualize and draw conclusions on quantitative or qualitative variables. This course uses tables and charts to compare combinations of variables, identify the means of finding relationships between variables, and teaches you to interpret results and make predictions between variables.
In order to use data from a sample group to make judgments about an entire population, you will explore probability in order to move toward the area of inferential statistics in this course. You will identify the role of discrete variables, use them in determining probability, find the expected value, and define variance. Additionally, the normal distribution, often called the bell curve, is a practical model for many business measurements, including financial decision making, process variations, and salaries. In this course you will examine the normal distribution and identify how to determine probabilities and percentiles from each of these distributions.
It is often not feasible to capture parameters for an entire population; however, it’s necessary to gather statistics to estimate population parameters. In this course, you will walk through the multiple methods of collecting samples and examining margin of error and confidence intervals, including how they are calculated. You will then explore another area of inferential statistics called hypothesis testing to start with a hypothesized value. One of the most important measures to calculate is the p-value, which helps gauge the significance of your findings. You will observe the role that p-values play in hypothesis testing and the way in which they are calculated.

An ever-present need in business is to compare two populations, such as sales of related products, different customer segments, or productivity of factory work shifts, to name a few. In this course, you will examine how to compare two population means. Just as there is a need to look at two populations, the same is true for larger groups. However, the process of comparing three or more population means is significantly different. You will investigate the comparison of multiple means, including the experiment designs to choose from and the three-step process to follow. Additionally, you will explore how hypothesis testing is used to make judgments about a population.

Many times, however, comparisons are needed on more than one variable, such as a survey given to two different audiences or a defect caused by different pieces of equipment. Lastly, in this course you will examine tests on two variables, having either two options or multiple options and identify the formulas used in these comparisons.

Forecasting can be found in every corner of the business world today. When done in tandem with accurate time series analysis, it enables sound prediction of future values. In this course, you will explore the use of time series analysis and the four components of time series data. Consider, there are a number of time series that may require forecasting but do not have any discernible trend, such as a stable product environment or a very short timeframe. In this course you will continue exploring forecasting by examining stationary time series and the situations in which they most often occur and practice forecasting techniques and stationary time series analysis. You will then examine stationary data where no substantial change is taking place. Lastly, you will move to data that is changing. A layer of complexity can be added to forecasting in the form of seasonality, where the time series being studied regularly changes with each season. This added element must be considered in any prediction of future periods.
A field in which statistics can play a vital role is quality control. Statistical tools assist in the monitoring and maintenance of product quality. In this course you will explore quality control and how statistical methods are utilized within quality control. You will practice preparation and analysis of charts and determine some additional quality control methods. Additionally, organizations are constantly faced with major strategic decisions. These critical choices are best made using decision analysis tools. Analysis may involve a large number of variables for each item or individual being studied. This type of study, known as multivariate analysis, seeks to shed light on the relationships between all the variables. You will examine several techniques to choose from when undertaking multivariate analysis.

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