This is to be done individually. 1. Collect data on about ten to fifteen single family houses for sale in one locality on these variables (using websites such as realtor.com or zillow.com): address area in square feet, number of bedrooms, age in years, and asking price. Try to get a random sample: for example, do not select all high priced or low priced houses. Make sure that all houses that you select have these information - if any is missing for a particular house, choose another house. If year built is given, subtract that from 2021 to get age in years. Example: address area (sq.ft) number of bedrooms age (years) asking price 1060 Blackhawk Dr, University Park, IL 60484 1,870 3 19 135,000 2. Run a simple regression analysis on 'asking price' versus 'area in square feet' (the latter being the independent variable). You may use Excel or StatCrunch. Create a report with page 1: the data table (address, area in square feet, number of bedrooms, age, asking price) page 2: scatter plot of 'asking price' versus 'area in square feet' and page 3: a narrative with the line of best fit (the equation), a statement about how good the fit is, a numerical measure for the fit, an interpretation of the measure of "fit", whether the slope is significant, an interpretation of the slope, a 95% confidence interval for the slope, and an estimate of the 'asking price' for a house with an area of 2100 square feet. (10% Extra credit: using StatCrunch, find the 95% confidence interval for the mean price of houses with an area of 2100 square feet, and a prediction interval for the price of a house with 2100 square feet). 3. Run a multiple regression analysis on 'asking price' versus 'area in square feet', 'number of bedrooms', and 'age'. Add one more page to the previous report: page 4: a narrative with the line of best fit, a statement about how good the fit is, a numerical measure for the fit, an interpretation of the measure of "fit", whether at least one slope is (significantly) different from zero, and if so, whether each of the individual slopes is different from zero, a 95% confidence interval for each slope, and an estimate of the 'asking price' for a house with an area of 2100 square feet, 4 bedrooms, and 20 years of age. (10% Extra credit: using StatCrunch, find the 95% confidence interval for the mean price of houses with an area of 2100 square feet, 4 bedrooms, and 20 years of age; and a prediction interval of the price of a house with an area of 2100 square feet, 4 bedrooms, and 20 years of age.)