Building Building Measures Price Bath room car spots Distance Age Area Mean 3.10841747 1077202 1.67041801 1.704079 11.152556 56.7159 153.90731 Median 3 885000 2 2 10.1 52 135 Mode 3 600000 1 2 11.2 52 120 Standard Deviation 0.98644212 671327.71 0.7349971 1.0010573 37.313335 37.31333 88.965873 Variance 0.97306805 0.54022074 1.0021158 _ 1392.2849 1392.285 7914.9265 _ Range 11 8915000 12 26 3112 823 3112 Minimum 1 85000 0 0 0 3 0 Maximum 12 9000000 12 26 3112 826 3112 The above table depicts a complete univariate analysis of key variables. ZOOM Brief Some weeks ago, you were hired to consult an online real-estate start-up interested in the actors that determine auction prices of residential real estate in the Metropolitan Melbourne area The chief data scientist at the start-up asked that you create a new document with a technical focus and conduct the following additional analysis: . They have requested that you now include and interpret confidence intervals for your estimates of the means and standard deviations provided in your original report. Re- create your original tables with this additional information. Is has been suggested that the market for housing has been stagnating over the past 12 months. Is there any evidence that there has been a statistically significant improvement over last year's mean sales price of $998,234? Does overall real estate price statistically significantly differ by type? Specifically, is there a difference in mean price between a 'townhouse' compared to a house'. Provide mean prices for these two categories alongside their respective confidence intervals. Conduct a formal hypothesis test of the difference between mean price levels. Use a level of significance of 0.05 for all calculations. State and test your assumptions. List all necessary steps and decision rules. Present analysis in language suitable for a technical audience (the chief data scientist) but also provide a non-technical summary of results. 1The above graphs represent a positive graphical relationship between Price and the Numerical Variables. Correlation Variables Coefficient w.r.t. Price Rooms 0.47876064 Bathroom 0.46678982 Car 021318909 Distance -0.2321232 Building Age 0.33330556 Building Area 050270448