Daily DAX : Day 349 CHISO.DIST.RT

CHISQ.DIST.RT: Power BI DAX Function Explained

What is CHISQ.DIST.RT?

The CHISQ.DIST.RT function in DAX (Data Analysis Expressions) for Power BI calculates the right-tailed probability of the chi-squared distribution. This is the probability that a chi-squared random variable exceeds a given value, essentially 1 minus the cumulative distribution function (CDF) up to that point.

It's part of the statistical functions in Power BI, used for hypothesis testing and analyzing variances in data.

Syntax

CHISQ.DIST.RT(x, deg_freedom)
        

• x: The value at which to evaluate the distribution (must be ≥ 0).
• deg_freedom: The degrees of freedom (integer ≥ 1, up to 10^10).

Returns: A decimal number between 0 and 1 representing the right-tail probability.

How It Works

The chi-squared distribution models the sum of squares of independent standard normal variables. The right-tailed probability helps determine how unusual an observed chi-squared statistic is under the null hypothesis.

• If x is negative: Returns #NUM! error.
• If deg_freedom < 1 or > 10^10: Returns #NUM! error.
• If arguments are non-numeric: Returns #VALUE! error.

Relationship to other functions: CHISQ.DIST.RT(x, deg_freedom) = 1 - CHISQ.DIST(x, deg_freedom, TRUE)

Use Cases

This function is ideal for chi-squared goodness-of-fit tests and tests of independence in categorical data analysis. Common scenarios include:

• Hypothesis Testing: Compare observed vs. expected frequencies (e.g., in genetics or surveys) to check if differences are due to chance.
• Quality Control: Test if product defect rates match expected distributions across categories.
• Market Research: Analyze if customer preferences (e.g., by region) are independent of variables like age group.
• Clinical Trials: Assess if treatment outcomes differ significantly from expected ratios between groups.

Typically, compute a chi-squared statistic from your data, then use this function to get the p-value. If p-value < 0.05, reject the null hypothesis.

Example

Suppose you're testing if observed plant color frequencies (Red: 10, Blue: 20, Green: 30) match expected equal ratios (20 each) with 2 degrees of freedom.

1. Chi-squared statistic = Σ[(Observed - Expected)² / Expected] ≈ 5.0
2. DAX Measure: P-Value = CHISQ.DIST.RT(5.0, 2)
3. Result: ≈ 0.082 (p > 0.05, so no significant difference).

In Power BI, add this as a measure to visualize p-values in reports for quick statistical insights.