A real estate company is analyzing the selling prices of residential homes in a given community. 140 homes that have been sold in the past month are randomly selected and their selling prices are recorded. The statistician working on the project has stated that in order to perform various statistical tests, the data must be distributed according to a normal distribution. In order to determine whether the selling prices of homes included in the random sample are normally distributed, the statistician divides the data into 6 classes of equal size and records the number of observations in each class. She then performs a chi-square goodness-of-fit test for normal distribution. The results are summarized in the following table. Goodness-of-Fit Test Observed Expected O-E (0-E)?/E % of chi-square 10 3.192 6.808 14.520 64.81 23 19.026 3.974 0.830 3.70 37 47.782 -10.782 2.433 10.86 40 47.782 -7.782 1.267 5.66 27 19.026 7.974 3.342 14.92 3 3.192 -0.192 0.012 0.05 140 140.000 0.000 22.404 100.00 22.40 | chi-square p-value .0001 What is the appropriate null hypothesis? O Ho: The residential home selling prices are distributed according to a normal distribution. O Ho: The residential home selling prices are not distributed according to a normal distribution. O Ho: The distribution of residential home selling prices is either right or left skewed. O Ho: The distribution of the residential home selling prices is symmetric. None of the other answers is correct.

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Estimation

A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.