Regression analysis explains the relationship between two variables, the stronger the correlation between variables, the better the prediction. Multiple regression is an important technique that uses several variables to predict values of another variable. From the excel output, the coefficient of the intercept represents the slope which is given by -100,012.3227, this is the point at which the regression line cuts the vertical axis. The 95% confidence interval for the intercept is -163.6855 to -36,194.95991 which mean that based on the sample data the intercept of the population in the three areas is 95% likely to lie in the range of -163,829.6855 to -36,194. On the other hand, the regression coefficient between the numbers of bedrooms in the three areas is 127,792.4504 and this implies that for every increase of 1 in the horizontal axis, the number of bedrooms on the vertical axis change by 127,792. The 95% confidence interval for the number of bedrooms ranges from 108,797.9557 to 146,786. Since the regression analysis is based on the samples from Mersea Island, Jaywick, and Colchester and not the total population, there exists a risk that the sample regression coefficient is not the same as that of the population and therefore the 95% confidence interval gives the range of regression slopes within which we are 95% sure that the population slope will lie. Analysis of the Housing Market.
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