Benford's law states that the probability distribution of the first digits of many items (e.g. populations and expenses) is not uniform, but has the probabilities shown in this table. Business expenses tend to follow Benford's Law, because there are generally more small expenses than large expenses. Perform a "Goodness of Fit" Chi-Squared hypothesis test (a = 0.05) to see if these values are consistent with Benford's Law. If they are not consistent, it there might be embezzelment. Complete this table. The sum of the observed frequencies is 137 Obranu Expected

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**Understanding Benford's Law in Business Expenses**

Benford's law states that the probability distribution of the first digits of many items (e.g., populations and expenses) is not uniform, but has the probabilities shown in this table.

Businesses tend to follow Benford's Law because there are generally more small expenses than large expenses. 

Let's perform a "Goodness of Fit" Chi-Squared hypothesis test (α = 0.05) to see if these values are consistent with Benford's Law. If they are not consistent, there might be embezzlement.

Complete the table. The sum of the observed frequencies is 137.

| First Digit (X) | Observed Frequency (Counts) | Benford’s Law P(X) | Expected Frequency (Counts) |
|-----------------|------------------------------|---------------------|-----------------------------|
|        1        |             39               |        .301         |                             |
|        2        |             29               |        .176         |                             |
|        3        |             20               |        .125         |                             |
|        4        |             12               |        .097         |                             |
|        5        |              7               |        .079         |                             |
|        6        |             11               |        .067         |                             |
|        7        |             7                |        .058         |                             |
|        8        |             6                |        .051         |                             |
|        9        |             6                |        .046         |                             |


Report all answers accurate to three decimal places.

**Steps to Complete the Table:**

1. Calculate the Expected Frequency (Counts) for each digit by multiplying the total observed frequencies by the probability given by Benford’s Law.
   - For digit 1: Expected Frequency = 137 * 0.301 = 41.237
   - For digit 2: Expected Frequency = 137 * 0.176 = 24.112
   - Continue similarly for all digits.

2. Once the Expected Frequencies are filled, you can proceed to compute the Chi-square test-statistic (χ²).

**Chi-Square Test Computation:**

1. Calculate the Chi-square test-statistic (χ²) using the formula:
   \[
   \chi^2 = \sum \frac{(Observed -
Transcribed Image Text:**Understanding Benford's Law in Business Expenses** Benford's law states that the probability distribution of the first digits of many items (e.g., populations and expenses) is not uniform, but has the probabilities shown in this table. Businesses tend to follow Benford's Law because there are generally more small expenses than large expenses. Let's perform a "Goodness of Fit" Chi-Squared hypothesis test (α = 0.05) to see if these values are consistent with Benford's Law. If they are not consistent, there might be embezzlement. Complete the table. The sum of the observed frequencies is 137. | First Digit (X) | Observed Frequency (Counts) | Benford’s Law P(X) | Expected Frequency (Counts) | |-----------------|------------------------------|---------------------|-----------------------------| | 1 | 39 | .301 | | | 2 | 29 | .176 | | | 3 | 20 | .125 | | | 4 | 12 | .097 | | | 5 | 7 | .079 | | | 6 | 11 | .067 | | | 7 | 7 | .058 | | | 8 | 6 | .051 | | | 9 | 6 | .046 | | Report all answers accurate to three decimal places. **Steps to Complete the Table:** 1. Calculate the Expected Frequency (Counts) for each digit by multiplying the total observed frequencies by the probability given by Benford’s Law. - For digit 1: Expected Frequency = 137 * 0.301 = 41.237 - For digit 2: Expected Frequency = 137 * 0.176 = 24.112 - Continue similarly for all digits. 2. Once the Expected Frequencies are filled, you can proceed to compute the Chi-square test-statistic (χ²). **Chi-Square Test Computation:** 1. Calculate the Chi-square test-statistic (χ²) using the formula: \[ \chi^2 = \sum \frac{(Observed -
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