Use the following data in any software like Excel or Eviews and develop a regression model using the price variable as the dependent variable. [you can copy and paste this in the software] Run and save your output results with a best-fit model with the highest significance. Copy your output results to a Word file and interpret your results. Results interpretation includes interpretation of the following: - Overall model significance - Coefficients (including their significance). Price Rooms Income In $ Tax Rate Commercial 100646 4.4 47150 1.3 24.84 100838 4.4 47150 1.46 24.84 92959 4 47735 2.94 25.18 93431 4 47735 3.08 24.77 106811 4.4 50990 2.48 7.89 108301 4.4 50990 2.97 9.01 114311 4.5 56195 3.6 25.35 114912 4.5 56195 3.77 25.52 109154 4.5 53915 2.21 13 110226 4.5 53915 2.13 13.24 97428 4.2 53345 2.19 9.16 102428 4.2 53345 2.05 9.1 96261 4.5 51470 3.91 16.04 96967 4.5 51470 3.97 15.46 118291 4.3 54080 1.54 13.19 118621 4.3 54080 1.92 13.3 95668 4.3 53270 1.03 21.3 98649 4.3 53270 0.97 21.84 100747 4.3 49895 3.32 24.95 101501 4.3 49895 3.36 24.94 104647 4.4 55880 2.66 18.64 105753 4.4 55880 2.71 19.04 105005 4.7 61805 1.87 9.96 106864 4.7 61805 1.6 10.3 101860 4.3 52625 1.94 26.67 102510 4.3 52625 2.42 28.43 106011 4.4 57620 2.37 19.75 106864 4.4 57620 2.37 20.79 97996 4.1 64625 2.46 13.92 98410 4.1 64625 2.53 14.12 109471 4.6 57455 2.24 22.53 110117 4.6 57455 2.22 24.51 110922 4.7 60140 2 12.89 111602 4.7 60140 2.2 13.39 110679 4.5 58835 2.5 18.73 111426 4.5 58835 2.63 18.42 99491 4.6 55610 2.7 13.72 100353 4.6 55610 2.56 13.58 92652 4.5 50120 2.73 20.44 92884 4.5 50120 2.53 20.94 101649 4.4 61010 3.88 14.37 103423 4.4 61010 3.82 13.77 111332 4.7 58205 2.89 8.38 113501 4.7 58205 2.81 9.3 109571 4.6 52835 1.85 12.99 112370 4.6 52835 1.63 12.18 100907 4.4 56510 3.59 22.37 101021 4.4 56510 3.31 22.74 124620 4.6 67895 2.22 28.2 125122 4.6 67895 2.35 28.37 113671 4.6 55910 2.8 14.12 114803 4.6 55910 3.01 15.82 99374 4.3 56825 1.96 16.45 100258 4.3 56825 2.24 16.35 94584 4.4 51785 2.2 19.03 95051 4.4 51785 2.24 19.27 94466 4.6 51275 4.41 18.91 97299 4.6 51275 4.17 18.52 103522 4.2 51320 3.4 14.37 106451 4.2 51320 3.85 16.69 105113 4.6 54155 3.41 17.25 105898 4.6 54155 3.41 18.83 117074 5 52970 2.51 9.92 119141 5 52970 2.72 9.84 92316 4.3 54140 4.7 9.55 91599 4.3 54140 5.34 9.46 100360 4.3 58760 4.16 19.5 100414 4.3 58760 4.16 19.83 104375 4.3 61640 3.78 7.64 105453 4.3 61640 4.28 24.93 114465 4.4 54935 0.7 14.21 118744 4.4 54935 0.89 13.08 107574 4.4 50615 2.91 18.36 107832 4.4 50615 2.98 16.95 136952 4.7 60140 2.35 11.21 136472 4.7 60140 2.15 11.57 99331 4.4 46085 4.02 25.01 99941 4.4 46085 3.95 27.56 105150 4.8 60305 3.49 11.77 108168 4.8 60305 3.5 11.45 121064 4.5 54245 1.58 3.94 122638 4.5 54245 1.94 5.72 114224 4.1 51500 1.84 3.26 115775 4.1 51500 2.25 2.8 112955 5.1 64175 2.9 8.86 115457 5.1 64175 2.98 7.62 132758 5.2 60680 1.47 9.48 131871 5.2 60680 1.91 9.63 102710 4.4 52955 2.31 17.07 103014 4.4 52955 2.97 16.86
Use the following data in any software like Excel or Eviews and develop a regression model using the price variable as the dependent variable. [you can copy and paste this in the software] Run and save your output results with a best-fit model with the highest significance. Copy your output results to a Word file and interpret your results. Results interpretation includes interpretation of the following: - Overall model significance - Coefficients (including their significance). Price Rooms Income In $ Tax Rate Commercial 100646 4.4 47150 1.3 24.84 100838 4.4 47150 1.46 24.84 92959 4 47735 2.94 25.18 93431 4 47735 3.08 24.77 106811 4.4 50990 2.48 7.89 108301 4.4 50990 2.97 9.01 114311 4.5 56195 3.6 25.35 114912 4.5 56195 3.77 25.52 109154 4.5 53915 2.21 13 110226 4.5 53915 2.13 13.24 97428 4.2 53345 2.19 9.16 102428 4.2 53345 2.05 9.1 96261 4.5 51470 3.91 16.04 96967 4.5 51470 3.97 15.46 118291 4.3 54080 1.54 13.19 118621 4.3 54080 1.92 13.3 95668 4.3 53270 1.03 21.3 98649 4.3 53270 0.97 21.84 100747 4.3 49895 3.32 24.95 101501 4.3 49895 3.36 24.94 104647 4.4 55880 2.66 18.64 105753 4.4 55880 2.71 19.04 105005 4.7 61805 1.87 9.96 106864 4.7 61805 1.6 10.3 101860 4.3 52625 1.94 26.67 102510 4.3 52625 2.42 28.43 106011 4.4 57620 2.37 19.75 106864 4.4 57620 2.37 20.79 97996 4.1 64625 2.46 13.92 98410 4.1 64625 2.53 14.12 109471 4.6 57455 2.24 22.53 110117 4.6 57455 2.22 24.51 110922 4.7 60140 2 12.89 111602 4.7 60140 2.2 13.39 110679 4.5 58835 2.5 18.73 111426 4.5 58835 2.63 18.42 99491 4.6 55610 2.7 13.72 100353 4.6 55610 2.56 13.58 92652 4.5 50120 2.73 20.44 92884 4.5 50120 2.53 20.94 101649 4.4 61010 3.88 14.37 103423 4.4 61010 3.82 13.77 111332 4.7 58205 2.89 8.38 113501 4.7 58205 2.81 9.3 109571 4.6 52835 1.85 12.99 112370 4.6 52835 1.63 12.18 100907 4.4 56510 3.59 22.37 101021 4.4 56510 3.31 22.74 124620 4.6 67895 2.22 28.2 125122 4.6 67895 2.35 28.37 113671 4.6 55910 2.8 14.12 114803 4.6 55910 3.01 15.82 99374 4.3 56825 1.96 16.45 100258 4.3 56825 2.24 16.35 94584 4.4 51785 2.2 19.03 95051 4.4 51785 2.24 19.27 94466 4.6 51275 4.41 18.91 97299 4.6 51275 4.17 18.52 103522 4.2 51320 3.4 14.37 106451 4.2 51320 3.85 16.69 105113 4.6 54155 3.41 17.25 105898 4.6 54155 3.41 18.83 117074 5 52970 2.51 9.92 119141 5 52970 2.72 9.84 92316 4.3 54140 4.7 9.55 91599 4.3 54140 5.34 9.46 100360 4.3 58760 4.16 19.5 100414 4.3 58760 4.16 19.83 104375 4.3 61640 3.78 7.64 105453 4.3 61640 4.28 24.93 114465 4.4 54935 0.7 14.21 118744 4.4 54935 0.89 13.08 107574 4.4 50615 2.91 18.36 107832 4.4 50615 2.98 16.95 136952 4.7 60140 2.35 11.21 136472 4.7 60140 2.15 11.57 99331 4.4 46085 4.02 25.01 99941 4.4 46085 3.95 27.56 105150 4.8 60305 3.49 11.77 108168 4.8 60305 3.5 11.45 121064 4.5 54245 1.58 3.94 122638 4.5 54245 1.94 5.72 114224 4.1 51500 1.84 3.26 115775 4.1 51500 2.25 2.8 112955 5.1 64175 2.9 8.86 115457 5.1 64175 2.98 7.62 132758 5.2 60680 1.47 9.48 131871 5.2 60680 1.91 9.63 102710 4.4 52955 2.31 17.07 103014 4.4 52955 2.97 16.86
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Concept explainers
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Question
Use the following data in any software like Excel or Eviews and develop a regression model using the price variable as the dependent variable. [you can copy and paste this in the software]
Run and save your output results with a best-fit model with the highest significance.
Copy your output results to a Word file and interpret your results.
Results interpretation includes interpretation of the following:
- Overall model significance
- Coefficients (including their significance).
| Price | Rooms | Income In $ | Tax Rate | Commercial |
| 100646 | 4.4 | 47150 | 1.3 | 24.84 |
| 100838 | 4.4 | 47150 | 1.46 | 24.84 |
| 92959 | 4 | 47735 | 2.94 | 25.18 |
| 93431 | 4 | 47735 | 3.08 | 24.77 |
| 106811 | 4.4 | 50990 | 2.48 | 7.89 |
| 108301 | 4.4 | 50990 | 2.97 | 9.01 |
| 114311 | 4.5 | 56195 | 3.6 | 25.35 |
| 114912 | 4.5 | 56195 | 3.77 | 25.52 |
| 109154 | 4.5 | 53915 | 2.21 | 13 |
| 110226 | 4.5 | 53915 | 2.13 | 13.24 |
| 97428 | 4.2 | 53345 | 2.19 | 9.16 |
| 102428 | 4.2 | 53345 | 2.05 | 9.1 |
| 96261 | 4.5 | 51470 | 3.91 | 16.04 |
| 96967 | 4.5 | 51470 | 3.97 | 15.46 |
| 118291 | 4.3 | 54080 | 1.54 | 13.19 |
| 118621 | 4.3 | 54080 | 1.92 | 13.3 |
| 95668 | 4.3 | 53270 | 1.03 | 21.3 |
| 98649 | 4.3 | 53270 | 0.97 | 21.84 |
| 100747 | 4.3 | 49895 | 3.32 | 24.95 |
| 101501 | 4.3 | 49895 | 3.36 | 24.94 |
| 104647 | 4.4 | 55880 | 2.66 | 18.64 |
| 105753 | 4.4 | 55880 | 2.71 | 19.04 |
| 105005 | 4.7 | 61805 | 1.87 | 9.96 |
| 106864 | 4.7 | 61805 | 1.6 | 10.3 |
| 101860 | 4.3 | 52625 | 1.94 | 26.67 |
| 102510 | 4.3 | 52625 | 2.42 | 28.43 |
| 106011 | 4.4 | 57620 | 2.37 | 19.75 |
| 106864 | 4.4 | 57620 | 2.37 | 20.79 |
| 97996 | 4.1 | 64625 | 2.46 | 13.92 |
| 98410 | 4.1 | 64625 | 2.53 | 14.12 |
| 109471 | 4.6 | 57455 | 2.24 | 22.53 |
| 110117 | 4.6 | 57455 | 2.22 | 24.51 |
| 110922 | 4.7 | 60140 | 2 | 12.89 |
| 111602 | 4.7 | 60140 | 2.2 | 13.39 |
| 110679 | 4.5 | 58835 | 2.5 | 18.73 |
| 111426 | 4.5 | 58835 | 2.63 | 18.42 |
| 99491 | 4.6 | 55610 | 2.7 | 13.72 |
| 100353 | 4.6 | 55610 | 2.56 | 13.58 |
| 92652 | 4.5 | 50120 | 2.73 | 20.44 |
| 92884 | 4.5 | 50120 | 2.53 | 20.94 |
| 101649 | 4.4 | 61010 | 3.88 | 14.37 |
| 103423 | 4.4 | 61010 | 3.82 | 13.77 |
| 111332 | 4.7 | 58205 | 2.89 | 8.38 |
| 113501 | 4.7 | 58205 | 2.81 | 9.3 |
| 109571 | 4.6 | 52835 | 1.85 | 12.99 |
| 112370 | 4.6 | 52835 | 1.63 | 12.18 |
| 100907 | 4.4 | 56510 | 3.59 | 22.37 |
| 101021 | 4.4 | 56510 | 3.31 | 22.74 |
| 124620 | 4.6 | 67895 | 2.22 | 28.2 |
| 125122 | 4.6 | 67895 | 2.35 | 28.37 |
| 113671 | 4.6 | 55910 | 2.8 | 14.12 |
| 114803 | 4.6 | 55910 | 3.01 | 15.82 |
| 99374 | 4.3 | 56825 | 1.96 | 16.45 |
| 100258 | 4.3 | 56825 | 2.24 | 16.35 |
| 94584 | 4.4 | 51785 | 2.2 | 19.03 |
| 95051 | 4.4 | 51785 | 2.24 | 19.27 |
| 94466 | 4.6 | 51275 | 4.41 | 18.91 |
| 97299 | 4.6 | 51275 | 4.17 | 18.52 |
| 103522 | 4.2 | 51320 | 3.4 | 14.37 |
| 106451 | 4.2 | 51320 | 3.85 | 16.69 |
| 105113 | 4.6 | 54155 | 3.41 | 17.25 |
| 105898 | 4.6 | 54155 | 3.41 | 18.83 |
| 117074 | 5 | 52970 | 2.51 | 9.92 |
| 119141 | 5 | 52970 | 2.72 | 9.84 |
| 92316 | 4.3 | 54140 | 4.7 | 9.55 |
| 91599 | 4.3 | 54140 | 5.34 | 9.46 |
| 100360 | 4.3 | 58760 | 4.16 | 19.5 |
| 100414 | 4.3 | 58760 | 4.16 | 19.83 |
| 104375 | 4.3 | 61640 | 3.78 | 7.64 |
| 105453 | 4.3 | 61640 | 4.28 | 24.93 |
| 114465 | 4.4 | 54935 | 0.7 | 14.21 |
| 118744 | 4.4 | 54935 | 0.89 | 13.08 |
| 107574 | 4.4 | 50615 | 2.91 | 18.36 |
| 107832 | 4.4 | 50615 | 2.98 | 16.95 |
| 136952 | 4.7 | 60140 | 2.35 | 11.21 |
| 136472 | 4.7 | 60140 | 2.15 | 11.57 |
| 99331 | 4.4 | 46085 | 4.02 | 25.01 |
| 99941 | 4.4 | 46085 | 3.95 | 27.56 |
| 105150 | 4.8 | 60305 | 3.49 | 11.77 |
| 108168 | 4.8 | 60305 | 3.5 | 11.45 |
| 121064 | 4.5 | 54245 | 1.58 | 3.94 |
| 122638 | 4.5 | 54245 | 1.94 | 5.72 |
| 114224 | 4.1 | 51500 | 1.84 | 3.26 |
| 115775 | 4.1 | 51500 | 2.25 | 2.8 |
| 112955 | 5.1 | 64175 | 2.9 | 8.86 |
| 115457 | 5.1 | 64175 | 2.98 | 7.62 |
| 132758 | 5.2 | 60680 | 1.47 | 9.48 |
| 131871 | 5.2 | 60680 | 1.91 | 9.63 |
| 102710 | 4.4 | 52955 | 2.31 | 17.07 |
| 103014 | 4.4 | 52955 | 2.97 | 16.86 |
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