MAT 240 Module 5 Assignment PDF

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Applied stats course work module 5...

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Hypothesis Testing for Regional Real Estate Company Hypothesis Testing for Regional Real Estate Company Maleka Monono Southern New Hampshire University

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Hypothesis Testing for Regional Real Estate Company

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Introduction This report is to help analyze real estate data. The purpose of this analysis is to confirm or reject the salesperson advertisement. I have designed a hypothesis test on the sample 1,001 home sales for the pacific region. Based on the results, if the null hypothesis is supported, the salespersons advertisement cannot be approved. The p value will determine if the null hypothesis is rejected or supported based on the significance level of 0.5. Setup The real mean for the population of the pacific region is unknown. The population parameter being investigated is u = mean cost per square foot is pacific region. The null hypothesis (H0) is that the mean cost per square foot in the pacific region is greater than or equal to $275. The alternative hypothesis is (Ha) is that the mean cost per square foot in the pacific region is less than $275. The test I will be using is a left – tailed. Data Analysis Preparations This sample is unimodal, right skewed, non – symmetric and bell shaped. The measures of center are in the table below. This sample has a large spread (lot of variability).

Hypothesis Testing for Regional Real Estate Company

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Since, the alternative hypothesis is less than $275 and the critical region is on the extreme left under the curve we will be conducting a left – tailed test (this can be seen using the curve graph presented above). The test statistic appropriate for this hypothesis test is the t-statistic. The formula for calculating you t-statistic is Mean – Target / Standard Error. To calculate the Standard Error, Standard Deviation / Square root of sample size (161.7565/square root of 1000). The T-static is $264 - $275 / 5.11 = -2.1483. The significance level is 0.5. Calculations

Hypothesis Testing for Regional Real Estate Company

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Since my data is left – tailed, I’ll be using =T.DIST([test statistic], [degree of freedom], 1) to calculate my p value. My p value is 0.015. The graph below shows where the p – value and t-statistic are.

Test Decision Depending on the p value, the significance level will with determine if the null hypothesis I s supported or rejected. In this case the p value is 0.015 and the significance level is 0.5. The p value is lower than the significance level. Therefore, we reject the null hypothesis that the mean cost per square foot is greater than or equal to $275. The evidence supports the alternative, suggesting that the mean cost per square foot in the pacific region is less than $275. Conclusion My hypothesis was tested to see if the advertisement will be approved or not. From my analysis, the null hypothesis was rejected, and the alternative hypothesis was supported. Meaning that the mean cost per square foot in the pacific region is less than $275. This allows the advertisement to be approved. My conclusions are statistically significant because my p value (0.015) does not exceed my significance level of 0.5....