I am working on an assignment and I was trying to compare my results with what is given here. https://www.bartleby.com/questions-and-answers/the-sheet-called-housepr-contains-data-on-prices-of-houses-that-have-sold-recently-and-two-attribute/9927f067-cb79-4a9b-85fd-b5fe77442023 I am getting a different Excel output and I cannot figure out why. Can I have some assistance to what I am doing wrong? Or is the solution on here incorrect?
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
I am working on an assignment and I was trying to compare my results with what is given here. https://www.bartleby.com/questions-and-answers/the-sheet-called-housepr-contains-data-on-prices-of-houses-that-have-sold-recently-and-two-attribute/9927f067-cb79-4a9b-85fd-b5fe77442023 I am getting a different Excel output and I cannot figure out why. Can I have some assistance to what I am doing wrong? Or is the solution on here incorrect?

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