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FIVE STATISTICAL TOOLS and TIPS YOU NEED to FOCUS ON
STATISTICAL TOOLS are gaining in importance as the volume, variety, and velocity of data increases. But what are the most overlooked or misused statistical tools FP&A practitioners need to be aware of? Here are five from our FP&A Guide to Leveraging Business Statistics, underwritten by Workiva.
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Fitting the Curve + MAPE
Standard Deviation
Logistic Regression
Tips for Using Statistics Well
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3
ARIMA
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5
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DEFINITION “Fitting a model” is creating a relationship between predictors (independent variables) and outcomes (dependent variables) USE Different models or algorithms may be compared to historical data to see which has the best description (smallest variance) from the data
| STANDARD DEVIATION | LOGISTIC REGRESSION | ARIMA | TIPS FOR USING STATISTICS WELL
FITTING THE CURVE + MAPE
FITTING THE CURVE + MAPE | STANDARD DEVIATION | LOGISTIC REGRESSION | ARIMA | TIPS FOR USING STATISTICS WELL
In this video discover the importance of fitting a curve, and how the MAPE can help you figure out which is the best fit curve.
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DEFINTION MAPE, or mean average percentage error, is a method to calculate the average variance USE When comparing the accuracy of various forecasting methods, the one with the lowest MAPE may have the best predictive power
FITTING THE CURVE
MAPE
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USE CASE
STANDARD DEVIATION
DEFINITION Describes the variation or dispersion of data in a set USE A low SD indicates a small dispersion of data around the mean; a high SD indicates the opposite
In this video, you'll get an explanation of standard deviation, and then see it applied to build a control chart.
FITTING THE CURVE + MAPE | | LOGISTIC REGRESSION | ARIMA | TIPS FOR USING STATISTICS WELL
LOGISTIC REGRESSION
DEFINITION Estimating the relationship of independent variables (predictors) on a dependent (test) variable USE You can use a logistic regression model to predict binary outcomes, such as whether a team will win or lose, or whether a customer will default on their loan or not
FITTING THE CURVE + MAPE | STANDARD DEVIATION | | ARIMA | TIPS FOR USING STATISTICS WELL
This use case was created by Ryan Wade to demonstrate both the statistical application and R programming language as a tool for finance.
autoregressive integrated moving average (ARIMA)
DEFINITION A predictive time series modeling approach that looks to fit data that’s provided on a periodic basis USE Regresses past data for forecasts, but does not require advanced statistical knowledge to build, can be built using minimal data, and can be quickly developed, implemented and tested
A mid-market retailer wanted a more reliable approach to forecasting sales for the upcoming six months to better understand, predict, and manage the volatility and fluctuations they experience month to month. Cadilus Inc., a firm that provides FP&A services, assembled 10 years of daily sales from the client, restructured the incoming data, and built a modeling capability to predict sales on a 6-month forward-looking basis. The Cadilus team utilized R packages to complete the project. They kicked off the process by prepping the data for time-series analysis and examining it for significant outliers that could potentially affect model fitting. The team then decomposed the data into the appropriate components: season, trend, cycle [and the residuals]. Once those components were determined, the team ran formal statistical tests to determine stationarity (assuring that the series fluctuates in a consistent pattern)—a key requirement for ARIMA modeling. Once stationarity was determined and addressed, the team evaluated the order of parameters (given the components) for the model. Additional evaluations were completed for correlations, autocorrelations and order parameters. The model was fitted, improved over a few iterations, and the final predictions were made for the upcoming six months. Graphical illustration of forecast is below:
FITTING THE CURVE + MAPE | STANDARD DEVIATION | LOGISTIC REGRESSION | | TIPS FOR USING STATISTICS WELL
When comparing the monthly actuals to the predicted values, they fell within the prediction range. This allowed the client to feel comfortable that the monthly variations they were experiencing were likely due to seasonal and cyclical components vs. unique events. —Dan Shin, Cadilus
TIPS FOR USING STATISTICS WELL
FITTING THE CURVE + MAPE | STANDARD DEVIATION | LOGISTIC REGRESSION | ARIMA |
The power of statistics is the ability to understand the world and make meaning from the increasing piles of data that accumulate around us. The danger is in their misuse, or misunderstanding. Here are some tips on what questions you should be asking when it comes to statistics.
1. Which came first, the data or the question?
TO LEARN MORE ABOUT STATISTICS
2. Are you sure that the sample data represents the population?
3. What assumptions are behind your analysis?
4. How do you know that the correlation is causality?
5. Did someone check (and duplicate) your findings?
Download the Full Guide for a full list of questions to ask
more information on statistics
AFP GUIDE TO: Leveraging Business Statistics
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WEBINAR: Leveraging Business Statistics
WATCH THE REPLAY
READ MORE
ARTICLE: Improve Your Data Analysis Skills With These Three Steps
FITTING THE CURVE + MAPE | STANDARD DEVIATION | LOGISTIC REGRESSION | ARIMA | TIPS FOR USUNG STATICS WELL
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