please, don't be plagiarism

1.Examine the statistics for central tendency and dispersion:

(a)Calculate the mean, median, and range for ASSESVAL, COSTVALU, and MKTVALU. Interpret the results. How do the statistics for last year's value estimate compare to the value estimates for this year? What are some possible explanations for the differences? Which of the two appraisal methods of estimating value produces the most uniform estimate? (12 marks)

(b)Calculate the mean, median, minimum, and maximum for ORIGASR, COSTASR, and MRKTASR. Interpret the results. Define ASR. Which estimate, last year's assessed values, or this year's two estimates using cost or direct comparison approach, provides the most accurate prediction of the sale price? In general, how do the assessed values of the properties compare to their sale prices? (10 marks)

(c)Which of the statistics calculated in part (a) is likely the best measure of central tendency in this case and why? (hint: outliers) (3 marks)

2.Define and calculate the standard deviation, variance, and coefficient of variation for ASSESVAL, COSTVALU, and MKTVALU. Interpret the results. In your answer, indicate which of the value estimates seem to be the least variant, both in absolute and relative terms. (12 marks)

Hint: In calculating the standard deviation in your spreadsheet (Excel), use the function "STDEV" or "STDEV.S", standard deviation of the sample and not "STDEVP" or "STDEV.P", standard deviation of the population.

3.Examine the correlation coefficients for ASSESVAL and each of the following: LIVAREA, LOT_SIZE, and BEDROOMS. Define the correlation coefficient. What does a "strong" relationship imply? Interpret the results. Which of the predictive variables, if any (e.g., living area, lot size, the number of bedrooms), have a strong relationship with assessed value? Does this necessarily mean that we can predict the assessed value of the property by knowing these variables? (6 marks)

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4.You are to create a linear regression model using PRICE as the dependent variable and LIVAREA as the independent variable. You can use the INTERCEPT and SLOPE functions in Excel OR you may wish to use the regression tool contained in the Excel Analysis Toolpak.

(a)Create a best fit regression equation that would enable you to predict the price of a property based on the square footage of the living area of the house. (4 marks)

(b)Using your equation in (a), obtain a predicted value for a property with 3,000 square feet of living area. (1 mark)

(c)Create a scattergram with PRICE on the Y-axis and LIVAREA on the X-axis. Using the graphic method, draw what you think is the best fit regression line with X as the independent variable. According to your regression line, what is the approximate sale price for a property with 3,000 square feet of living area? (6 marks)

(d)Are your answers to (b) and (c) different? Why or why not? (4 marks)

(e)Determine the standard error of the estimate (SEE) and discuss its significance. What does this tell you about the predicted sale price as calculated in part (b)? Calculate coefficient of variation (or COV), which is the SEE as a percentage of the mean. What does this statistic indicate about the model? (5 marks)

(f)Create a new column in the spreadsheet called ESTIMATE. For each property in the spreadsheet, use the formula you obtained in 4(a) to determine the estimated sale prices, based on the living area. Create a new assessment-to-sale price ratio column called ESTASR; to do this, for each of the properties, divide the new ESTIMATE by the PRICE and multiply by 100. Calculate the median and the standard deviation of ESTASR and do the same for the ASRs of the other models: ORIGASR, COSTASR, and MRKTASR. How do the statistics for ESTASR compare to those of the ASRs of the other models? Comment on the differences. Based on these results, do you think that the ESTIMATE value using only the living area as the independent variable is a good predictive model for sale price? (7 marks)

5.An appraisal specialist has applied multiple regression analysis to prepare a massappraisal model for this Edmonton data. Four variations of this model have been prepared, all using PRICE as the dependent variable. The model results are provided in the "MRA" worksheet in the "Project 2: Assessment Database" file.

(a)Compare the results of Regression 1 with the results obtained in Question 4. What are the main differences? Which results are better? (Hint: consider the descriptive statistics for the two regression results). (5 marks)

(b)Using the results for Regressions 1, 2, and 3, obtain a predicted value for a property with 2,500 square feet of living area, a 7,200 square foot lot, 3 bedrooms, 2 bathrooms, 2 fireplaces, and 1 family room. Why are they different? (4 marks)

(c)Which variables in Regression 3 are the most important? Why? List the top five variables in decreasing order of importance. (2 marks)

(d)In Regression 4 there are two variables that are related to the lot C LOT_SIZE and either WIDTH

or DEPTH. How would you explain the coefficient for the WIDTH or DEPTH variable? If the results are different than expected, explain why. (4 marks)

(e)Which of the four regression results do you consider to be the best and why? In your answer, consider the statistics for each regression (R², SEE, F-value, and COV) and the coefficients and t- statistics for each variable. Include a table summarizing your comparison of the four regression results. (Hint: calculate the COV for each regression). (10 marks)

6.Presentation: The report should be professionally presented, neat, concise, logical, and use appropriate numbers of decimal places.. Use summary tables to present your findings. Do not copy/paste any raw data into your final report. (5 marks)

NOTE: Regression 1 is in columns A through G; Regression 2 is in colun Regression 1 Descriptive Statistics Mean Std. Deviation N PRICE LIVAREA 383731.15 89729.216 260 1600.18 475.551 260 LOT_SIZE 7365.16 1839.691 260 Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 LOT_SIZE, LIVAREA a. Dependent Variable: PRICE b. All requested variables entered. Model 1 Enter Model Summary R R Square Adjusted R Square Std. Error of the Estimate .851a .724 .722 47325.146 a. Predictors: (Constant), LOT_SIZE, LIVAREA ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression Residual Total 1509699967105.230 2 754849983552.617 337.036 .000 575595050548.614 2085295017653.850 257 2239669457.388 259 a. Dependent Variable: PRICE b. Predictors: (Constant), LOT_SIZE, LIVAREA Model 1 (Constant) LIVAREA LOT_SIZE a. Dependent Variable: PRICE Coefficientsa Unstandardized Coefficients B Standardized Coefficients Std. Error Beta 89760.407 143.275 8.785 13302.136 6.814 1.761 t Sig. 6.748 .000 .759 21.025 .000 .180 4.987 .000