first i send this number to bard chat bot

6020 5603 54830 6750 23000 12370 10000

it responds this.

You have listed 7 numbers.

• 6020
• 5603
• 54830
• 6750
• 23000
• 12370
• 10000

The sum of these numbers is 103573.

The average of these numbers is 14825.

The median of these numbers is 6750.

The mode of these numbers is 10000, which appears twice.

The range of these numbers is 23000 - 10000 = 13000.

The standard deviation of these numbers is 3844.74.

Is there anything else I can help you with?

then i ask it to standardize them.

and the respond was this.

Sure. Standardizing the numbers will mean subtracting the mean and then dividing by the standard deviation. This will transform the numbers so that they have a mean of 0 and a standard deviation of 1.

The standardized numbers are:

• -2.18
• -2.88
• 2.44
• -1.38
• 3.22
• -0.78
• 0.00

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Bing chat respond to the same question what this

I can standardize the numbers you provided. Standardization is the process of transforming data to have a mean of 0 and a standard deviation of 1. This is done by subtracting the mean from each data point and then dividing by the standard deviation.

Here are the standardized values for the numbers you provided:

• 6020: -0.705
• 5603: -0.801
• 54830: 2.891
• 6750: -0.524
• 23000: 0.693
• 12370: -0.191
• 10000: -0.363

Is there anything else I can help with? 😊

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and perplexity respond was this.

Based on the search results, it is possible to standardize the numbers provided by converting them into z-scores. Standardizing data is a common practice in statistics to compare variables that have different scales or units of measurement. To standardize the numbers provided, one would need to calculate the mean and standard deviation of the data and then transform each value to its corresponding z-score

Here are the steps to standardize the numbers:

By standardizing the numbers, we have transformed them into a common scale that allows for easier comparison and analysis. It is important to note that the interpretation of z-scores depends on the context and the distribution of the data.

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so, as you can see it can't even get sum of the data correct. my question is why this models cant get sum of this number right even when they apply all the correct steps