Saturday, July 27, 2019

Statistics 401 Mod 4 Case - Regression Analysis Coursework

Statistics 401 Mod 4 Case - Regression Analysis - Coursework Example In some cases, the scattered plotted points do form a pattern that resembles a straight line. These points all scatter around single straight line which is termed as the line of best fit. On drawing the line of best fit, it has a linear equation of the form y= mx + c. The equation can be used to predict the corresponding values of the X- variables or the Y- variable given the values of the Y- variables or the X- variables respectively. I inserted the data in the excel file in an effort to compute a scatter plot. By so doing, I made X to be the interest rate expressed as a decimal (e.g., 5% = 0.05). At the same time, I made Y to be the Housing Starts. This led to a scatter plot as shown in the graph below. It The regression equation that I computed as shown in the graph is:- y = 13357x – 12607 This is a linear equation or an equation of the straight line. The equation does indeed have the form Y = m*X + B, were Y is the number of starts, and B is the regression constant.  B i s the hypothetical value of Y when X = 0.   In accordance to the nature of this problem, It sure does make a practical sense. The equation is very useful in making predictions of the corresponding values of the variables given the other corresponding piece. ... The fact that the scatter plot so formed has a line of best fit with a linear equation confirms that indeed there is a relationship between the Housing stats and the interest rates. Given one of the values, the corresponding value can be easily predicted using the shared relationship. I Used the regression equation found above to calculate  what the approximate number of housing starts would be at the following interest rates: 8.5%, 4.5%, 3.7%, 2.3%. This is sown in the computations below.  I understood perfectly that I would not simply "guess" values, based on the historical data that was given.   That is clearly wrong. I also understood that I ought not have used linear interpolation between the historical data values;  that's also wrong. I saw to it that I rounded off estimates of starts to the nearest whole number.   This is because a house-building project either starts in a given month, or it doesn't.   Therefore, it makes no sense to talk about fractions of a start . X= 8.5 = 0.0885 y = 13357x - 12607 =(13357*8.85) – 12607 = 105602.45 = 105602 X= 4.5 = 0.045 y = 13357x – 12607 =(13357*4.5) – 12607 = 47499.5 = 47499 X= 3.7 = 0.037 y = 13357x - 12607 =(13357*3.7) – 12607 = 36813.9= 36813 X= 2.3 = 0.023 y = 13357x - 12607 =(13357*2.3) – 12607 = 18114.1 = 18114 If I were the owner of a business in the housing construction sector and I knew how interest rates were likely to change, I would use this information very effectively to make better decisions. The housing construction sector is a business venture where the risks involved are rather very huge. It involves the investment of a lot of money and this puts the investor in a lot of danger of losing a large sum of money all at once. This calls for a proper

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