Data is a driving force in business. More information is being collected than ever before, which professionals continually seek to leverage for success. Across all business functions, it’s essential to have analytical skills to interpret data and put it to use.
Statistical analysis is the basis for many business analytics approaches. Gaining a firm understanding of different statistical analysis methods is one of the first steps to unlocking the power of business analytics. With this knowledge, you can make sense of data, project future outcomes, and make more informed decisions.
Below are three helpful statistical analysis methods that lead to better business decisions.
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Hypothesis testing is a statistical method used to substantiate a claim about a population. This is done by formulating and testing two hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis (denoted by H₀) is a statement about the issue at hand, generally based on historical data and conventional wisdom. A hypothesis test always starts by assuming the null hypothesis is true and then testing to see if it can be nullified.
The alternative hypothesis (denoted by H₁) represents the theory or assumption being tested and is the opposite of the null hypothesis. If the data effectively nullifies the null hypothesis, then the alternative hypothesis can be substantiated.
In business, hypothesis testing is an effective means of assessing theories and assumptions before acting on them. For managers, leaders, and those looking to become more data-driven, this method of statistical analysis is a helpful decision-making tool. Putting this practice into action can lead to better foresight and positive outcomes when planning a business’s future.
For example, you might conduct a hypothesis test to substantiate that if your company launches a new product line, sales and revenue will increase as a result. Since this initiative would be expensive, your company might launch the product in a small test market and use the data it collects to justify rolling it out on a larger scale.
Hypothesis testing is a complex yet highly valuable statistical method for business. If you want to learn about hypothesis testing in more detail, taking an online statistics or business analytics course can be worthwhile.
Linear regression analysis is used for two main purposes: to identify and evaluate the relationship between two variables and forecast a variable based on its relationship to another one.
In single variable linear regression analysis, the relationship between a dependent variable and an independent variable is evaluated by identifying the line of best fit.
To find the line of best fit, use the following equation:
Here, ŷ represents the expected value of the dependent variable for a given value of X, which represents the independent variable. α is equal to the Y-intercept, or the point at which the regression line crosses the Y-axis, when X is equal to zero. β is the slope that equals the average change of the dependent variable (Y) as the independent variable (X) increases by one. Finally, ε is the error term that equals Y – ŷ, or the difference between the actual value of the dependent variable and its expected value.
Using this method, you can forecast a defined variable based on known data.
Consider the relationship between advertising spend and revenue, for example. A business can use historical data relating the advertising dollars spent to the amount of revenue generated for various campaigns or time periods. Using a single variable linear regression analysis, it can use that information to find the line of best fit and subsequently use the slope to forecast revenue for future campaigns.
Whereas single variable linear regression analysis studies the relationship between two variables—a dependent variable and an independent variable—multiple regression analysis investigates the relationship between a dependent variable and multiple independent variables.
Forecasting with multiple regression analysis is similar to using single variable linear regression. However, instead of entering only one value for an independent variable, a value is input for each independent variable. Using the same notation as the single variable linear regression equation, the following equation applies to multiple regression:
In business, multiple regression analysis is helpful for predicting the outcomes of complicated scenarios. For example, think back to the relationship between advertising spend and revenue. Instead of looking at total advertising expenditures, you can use multiple regression analysis to evaluate how different types of campaigns, such as television, radio, and social media ads, impact revenue.
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An analytical mindset is essential to business success. After all, data is one of the most valuable resources in today’s world, and knowing how to leverage it can lead to better decision-making and outcomes.
Depending on your current knowledge of statistics and business analytics and long-term goals, there are many options you can pursue to develop your skills. Taking an online course dedicated to honing and applying analytical skills in a professional setting is a great way to get started.
Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics—one of three courses comprising our Credential of Readiness (CORe) program—to learn how to use data analysis to solve business problems.
