Statistics for Business


★★★★★ “This course was very helpful in understanding the different aspects of training and development management. I particularly found the modules on developing and implementing training programs to be very informative.” – Sarah B., Manager of Training and Development

Statistics for Business Online Training Program

This online training program will teach you everything you need to know about statistics for business, from the basics to more advanced concepts. The course is designed for students who have no prior knowledge of the subject, as well as those who want to brush up on their skills.

The program starts with an introduction to statistical methods and data analysis, followed by a more in-depth look at specific topics such as probability, regression analysis, and time series analysis. Throughout the course, you’ll work with real-world data sets and learn how to apply statistical techniques to real-world problems. By the end of the program, you’ll be able to confidently use statistical tools and methods to make sound business decisions.

Learning Objectives:

Upon completing this online training program, you will be able to:

– Understand basic statistical concepts and methods

– Apply statistical techniques to real-world data sets

– Understand and interpret regression analysis results

– Understand and interpret time series analysis results

– Use statistical tools and methods to make sound business decisions.


Program Outline:

Introduction to Statistical Methods and Data Analysis

– Introduction to statistic

– Types of data

– Summarizing data

– Probability concepts

– Sampling methods

An Introduction to Regression Analysis

– Introduction to regression analysis

– Simple linear regression analysis

– Multiple linear regression analysis

Interpreting Regression Analysis Results

– Goodness-of-fit measures in regression analysis

– Interpreting regression coefficients outputted by statistical software packages

Time Series Analysis Concepts and Techniques

– Introduction to time series analysis

– Components of a time series – Forecasting with time series data

Applying Statistical Methods to Business Problems

– Case studies on using statistical methods in business decision-making

– Project: Business Decision Analysis



Course Format:

This online training program is self-paced and can be completed at your own convenience. The course is delivered through online self-study modules, and practical exercises.

Self-Study Modules:

The self-study modules consist of readings, videos, and interactive activities that you can complete at your own pace. These modules provide a deeper understanding of the topics covered in the ILT sessions.

Practical Exercises:

The practical exercises are designed to give you a hands-on opportunity to apply the concepts you’ve learned. You will be given a data set and a business problem, and you will be expected to use statistical techniques to solve the problem.




★★★★★ “This online training program was exactly what I needed to get up to speed on business statistics. It covered all the basics, as well as more advanced topics, and it was very well-organized and easy to follow. The practical exercises were also extremely helpful in solidifying my understanding of the material.” Bradley K.

★★★★ “I really enjoyed this course! It was very informative and gave me a great foundation in business statistics. I would definitely recommend it to anyone who wants to learn more about this topic.” Melinda S.


Sneak Peak & Glossary of Terms from the Program:


Statistical Software Packages:

There are many different statistical software packages available, each with its own strengths and weaknesses. Some of the more popular statistical software packages include SAS, SPSS, and R.

Data Set:

A data set is a collection of data that can be used for statistical analysis. Data sets can be found online, in books, or in other sources.

Business Problem:

A business problem is a real-world problem that can be solved using statistical methods. Business problems can be found in all industries, and they vary in complexity.

Descriptive Statistics:

Descriptive statistics are used to describe the main features of a data set. They are typically used to calculate measures of central tendency, such as the mean and median, and measures of dispersion, such as the standard deviation and range.

Data Science:

Data science is a branch of computer science that deals with extracting knowledge from data. Data science methods can be used for both structured and unstructured data.

Inferential Statistics:

Inferential statistics are used to make predictions or inferences about a population based on a sample. Inferential statistics are used when it is not possible to study the entire population, so a sample is taken and inferences are made about the population based on the sample.

Data Points:

Data points are the individual pieces of data in a data set. Data points can be numeric, categorical, or ordinal.

Hypothesis Testing:

Hypothesis testing is a process used to test a hypothesis about a population. Hypothesis testing consists of four steps: state the null and alternative hypotheses, calculate the test statistic, compare the test statistic to the critical value, and interpret the results.

Statistical Research:

Statistical research is research that uses statistical methods to solve problems. Statistical research can be used to study a variety of topics, such as business trends, social issues, and scientific phenomena.

Sample Data:

Sample data is a subset of a population that is used to represent the population. Sample data can be used to estimate population parameters, such as the mean and standard deviation.

Analyze Data:

Data analysis is the process of organizing, cleaning, and transforming data so that it can be used for statistical analysis. Data analysis involves understanding the data, finding patterns in the data, and making predictions about the data.

Independent Variables:

Independent variables are variables that are not influenced by other variables. Business management, normal distribution, and fixed income analyst are all independent variables.

Dependent Variable:

A dependent variable is a variable that is influenced by other variables. Interpret data, business setting, and qualitative data are all dependent variables.

Sampling Distributions:

Sampling distributions are distributions of values that can be calculated from a sample. Sampling distributions can be used to make inferences about a population.

Business Management:

Business management is the process of running a business. Business managers use statistical methods to make decisions about how to run their businesses.

Normal Distribution:

The normal distribution is a type of probability distribution that is symmetrical and bell-shaped. The normal distribution is often used to model data.

Fixed Income Analyst:

A fixed income analyst is a financial analyst who specializes in analyzing bonds and other fixed-income securities. Fixed income analysts use statistical methods to assess the risk and return of these investments.

Descriptive Statistics:

Descriptive statistics are used to describe the main features of a data set. They are typically used to calculate measures of central tendency, such as the mean and median, and measures of dispersion, such as the standard deviation and range.

Business Setting:

The business setting is the environment in which businesses operate. The business setting can be influenced by economic, political, and social factors.

Qualitative Data:

Qualitative data is data that cannot be quantified. Qualitative data is often categorical or ordinal. Examples of qualitative data include gender, race, and opinion.

Quantitative Data:

Quantitative data is data that can be quantified. Quantitative data is often numeric. Examples of quantitative data include height, weight, and age.

Performance Management:

Performance management is the process of assessing and improving the performance of employees. Performance management includes setting goals, measuring progress, providing feedback, and taking corrective action.

Financial Engineering:

Financial engineering is the application of math and physics to finance. Financial engineers use statistical methods to solve financial problems.

Data Collection:

Data collection is the process of gathering data from sources. Data collection can be done manually or electronically. Common sources of data include surveys, experiments, and observation.

Critical Values:

Critical values are values that are used to assess whether a test statistic is statistically significant. Critical values are based on the alpha level, which is the probability of making a Type I error.

Multiple Regression:

Multiple regression is a type of regression that is used to predict a dependent variable from two or more independent variables. Multiple regression is used to assess the relationship between multiple independent variables and a dependent variable.

Standard Deviation:

The standard deviation is a measure of dispersion. The standard deviation is the square root of the variance. It is used to quantify the amount of variation in a data set.

Risk Manager:

A risk manager is a financial professional who specializes in managing risk. Risk managers use statistical methods to identify, assess, and manage risk.

Central Limit Theorem:

The central limit theorem states that the distribution of a sample mean will be normal if the sample size is large enough. The central limit theorem is used to assess the validity of statistical tests.

Random Sample:

A random sample is a sample that is chosen randomly from a population. A random sample is used to estimate the population parameters.

Hypothesis Tests:

Hypothesis tests are used to test hypotheses about population parameters. Hypothesis tests are based on a comparison of the test statistic and the critical value.

Quality Control:

Quality control is the process of assessing and improving the quality of products or services. Quality control includes setting standards, measuring progress, providing feedback, and taking corrective action.

Past Performance:

Past performance is an assessment of how well an employee has done in the past. Past performance is often used to predict future success.

Future Events:

Future events are events that have not yet occurred. Future events can be predicted using statistical methods.

Business Applications:

Business applications are applications that are used in business. Business applications include accounting, marketing, and human resources.

Drawing Conclusions:

Drawing conclusions is the process of making inferences from data. Drawing conclusions involves analyzing data and making assumptions about the population.

Discrete Distributions:

Discrete distributions are probability distributions that are used to model discrete data. Discrete distributions are often used to model count data. Examples of discrete distributions include the binomial distribution and the Poisson distribution.

Continuous Distributions:

Continuous distributions are probability distributions that are used to model continuous data. Continuous distributions are often used to model measurement data. Examples of continuous distributions include the normal distribution and the uniform distribution.

Alternative Hypothesis:

The alternative hypothesis is the hypothesis that is being tested in a hypothesis test. The alternative hypothesis is usually the opposite of the null hypothesis.

Confidence Intervals:

Confidence intervals are used to estimate population parameters. Confidence intervals are based on sample statistics and contain a margin of error.

Central Tendency:

Central tendency is a measure of the center of a data set. Central tendency can be measured using the mean, median, or mode.

Statistical Problems:

Statistical problems are problems that can be solved using statistical methods. Statistical problems often involve data analysis and interpretation.

P Value:

The p value is the probability of making a Type I error. The p value is used to assess the statistical significance of a result.


Variables are characteristics that can be measured or observed. Variables can be categorical or numerical. Examples of variables include age, gender, and income.