Assessed Value of Small Commercial Real Estate Buildings (n = 32, k = 5)
Assessed Floor Offices Entrances Age
1796 4790 4 2 8
1544 4720 3 2 12
2094 5940 4 2 2
1968 5720 4 2 34
1567 3660 3 2 38
1878 5000 4 2 31
949 2990 2 1 19
910 2610 2 1 48
1774 5650 4 2 42
1187 3570 2 1 4
1113 2930 3 2 15
671 1280 2 1 31
1678 4880 3 2 42
710 1620 1 2 35
678 1820 2 1 17
1585 4530 2 2 5
842 2570 2 1 13
1539 4690 2 2 45
433 1280 1 1 45
1268 4100 3 1 27
1251 3530 2 2 41
1094 3660 2 2 33
638 1110 1 2 50
999 2670 2 2 39
653 1100 1 1 20
1914 5810 4 3 17
772 2560 2 2 24
890 2340 3 1 5
1282 3690 2 2 15
1264 3580 3 2 27
1162 3610 2 1 8
1447 3960 3 2 17
H2 Document1 - Word X AutoSave lisandio chacon O Tell me what you want to do Review View File Design References Mailings Help Share Home Insert Draw Layout Comments O Find Replace Cut A A Aa A Calibri (Body) 11 BbDc ABbcDc AaBbC Bcl Copy Paste Dictate B IUab x, x2A I.A Heading 1 T Normal T No Spac... Heading 2 Title Subtitle Select Format Painter Clipboard Styles Font Paragraph Editing Voice 1) Are the variables cross section or time series data? 2) How do you imagine that the data were collected? 3) Is the sample size sufficient to yield a good estimate? Does it fulfill Doane's Rule or Evan's Rule? 4) State you hypothesis about the sign of the slope for each predictor variable. 5) Generate a correlation matrix for your predictors. Based on the matrix is collinearity a problem? 6) Run the regression in Megastat requesting VIF's. Do they suggest a problem? 7) Interpret the slope coefficient. Does the intercept have meaning given the range of the data? 8) Use Megastat to fit the regression model, including residuals and standardized residuals 9) Interpret the P-value for each slope coefficient 10) Interpret the R^2 value 11) Study the table of residuals. Identify outliers, and unusual observations (standardized obs. that exceed 3, and 2 std. deviations respectively) 12) Output a normal probability plot. Do you see evidence that your regression violates the assumption of normality? 13) Inspect the residual plot to check for heteroscedasticity. Report your conclusions. 14) Identify any observations with high leverage. Page 1 of 2 498 words 100% 5:25 PM Type here to search 10/31/2019 H2 Document1 - Word X AutoSave lisandio chacon O Tell me what you want to do Review View File Design References Mailings Help Share Home Insert Draw Layout Comments O Find Replace Cut A A Aa A Calibri (Body) 11 BbDc ABbcDc AaBbC Bcl Copy Paste Dictate B IUab x, x2A I.A Heading 1 T Normal T No Spac... Heading 2 Title Subtitle Select Format Painter Clipboard Styles Font Paragraph Editing Voice 1) Are the variables cross section or time series data? 2) How do you imagine that the data were collected? 3) Is the sample size sufficient to yield a good estimate? Does it fulfill Doane's Rule or Evan's Rule? 4) State you hypothesis about the sign of the slope for each predictor variable. 5) Generate a correlation matrix for your predictors. Based on the matrix is collinearity a problem? 6) Run the regression in Megastat requesting VIF's. Do they suggest a problem? 7) Interpret the slope coefficient. Does the intercept have meaning given the range of the data? 8) Use Megastat to fit the regression model, including residuals and standardized residuals 9) Interpret the P-value for each slope coefficient 10) Interpret the R^2 value 11) Study the table of residuals. Identify outliers, and unusual observations (standardized obs. that exceed 3, and 2 std. deviations respectively) 12) Output a normal probability plot. Do you see evidence that your regression violates the assumption of normality? 13) Inspect the residual plot to check for heteroscedasticity. Report your conclusions. 14) Identify any observations with high leverage. Page 1 of 2 498 words 100% 5:25 PM Type here to search 10/31/2019
