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A streak is a short period of good or misfortune.

A team is said to experience a winning ability when it is victorious many game titles consecutively, also to have a loosing streak when it looses many suits in a line. It is quite easy to say that a team offers good players, and therefore contains a high chance of winning. After closer thought, though, it may become apparent that the skill and style of play of the teams playing against them has an important part to experience, and so are other factors like mentoring and the nature in the players. In this work, we have considered some variables that show up likely to affect the team’s chance of winning.

Specifically, we all chose adversary 3-points every game, group 3-points per game, crew free throws per video game, team renouvellement per game, opponent renouvellement per game, team springs back per video game and challenger rebounds every game as key determining variables in determining the winning chance of a basketball team. We had to deal with the occurrence unusually large or perhaps small beliefs in the info, since they affect the final outcome. As a result we shaped a multiple regression model for prediction, and altered it until we came across a model with six factors. Our style can be dependable to foresee the chance of any team successful by up to 80%, and the percentage win can be expected with an error margin zero. 1479 percentage points regarding 95% of the time.

Our model showed us the fact that more renouvellement a staff has and the more springs back from an opponent, the less the possibility of earning. However , the more 3-point photographs, free throws and rebounds made, plus the more turnovers an opponent makes, the more a team’s chance of earning.

3 TABLE OF CONTENTS Executive synopsis 2 Aim of the analyze 4 Data description five Technical statement 6 12 Conclusion and bureaucratic implications 13 Appendices Appendix I: Detailed statistics to get the factors 15 Appendix II: Container plots intended for the variables 16 Appendix III: Spread plots, winning chance vs . each variable 17 Appendix IV: Multiple regression information for 8-variable model twenty Appendix Sixth is v: Residual plots for the 8 factors 21 Appendix VI: Finest subsets regression details twenty-three Appendix VII: Regression information for 5-variable model twenty four. Appendix VIII: Residual Plots for your five variables 26 Appendix IX: Regression excluding residual outliers for 5-variable model twenty-eight Appendix Back button: Regression for 6-variable version 29 Appendix XI: Left over plots to get 6-variable unit 30 Appendix XII: (a) The final regression model 32 Appendix XII: (b) Residual plots intended for the final regression model 33 4 AIM OF THE STUDY The objective of his study should be to create a regression model intended for predicting the proportion wining of any basketball team among many basketball clubs in a particular basketball season.

Regression evaluation is a approach that aids us in predicting the outcome of a adjustable, given the values of 1 or more different (independent) variables. The style thus attained is reviewed to ascertain the reliability of its prediction. In our analysis, therefore , we could out to examine a multiple regression unit that we shall build, and improve on it until we find the best version for the work. We are enthusiastic by the fact that fans of teams every so often go into disputes (and even betting) as to what chance there may be for a particular group to succeed.

Winning a game title, we believe, is not totally a chance occurrence. We for that reason want to review what elements can be expected to look for the winning probability of a staff. We do not anticipate to get a mysterious model, yet that we must modify each of our model right up until its predictive ability has become greatly improved.

The importance of this work is based on the fact that, without accurate knowledge of the most influential factors affecting a phenomenon, you can end up spending a lot of resources (time, energy and money) on the factor that may not be so important, at the expense of the really important elements. This ends in a lot of input without having corresponding outcome, thereby bringing about frustration. This is especially true in sports and related activities. This operate is the little contribution to more effective planning and sport outing for a field hockey team. 5 DATA DESCRIPTION The data that we have used is usually taken from It reveals the statistics to get sixty-eight (68) teams in a sporting period.

Therefore we need to not always be going into concerns of time series or different techniques which come into perform when working with data that has been collected above an extended period. The data shows a list of sixty-eight basketball teams. Each team has played a number of games in a particular basketball sporting season.

The spreadsheet is made up of a lot of information about these sixty-eight teams, just like their winning percentage and vital statistics of the online games played through this particular period. In this function, we are going to designate a centered variable (Y) and eight independent factors (X1, X2, X3, X4, X5, X6 and X7). The factors are thought as follows: Con = Earning Percentage X1 = Opponent’s 3-point every game X2 = Team’s 3-point every game X3 = Team’s free punches pr video game X4 sama dengan Team’s proceeds per video game X5 = Opponent’s turnover per game X6 = Team’s recurring per video game X7 sama dengan Opponent’s rebound per video game With the over variables, we shall formulate a regression model for the winning percentage of a group in this info.

6 TECHNOLOGICAL REPORT 6th. 1 Preliminaries Our initially task, having obtained the info, is to look at the detailed statistics for every single of our impartial variables. The Minitab end result is provided in Appendix I. Your data appears to be normally distributed, because the mean and median are close. To increase verify this, we will appear at the box plots for every single of the parameters. The box plots reveal which the data is generally distributed, aside from turnover every game and opponent yield per game with a single outlier every single, and home rebound every game with three outliers.

The Box and building plots are presented in Appendix II. To increase understand each of our data, we still consider the scatter and building plots of each adjustable against the successful percentage. This will likely show us the extent where each of then effect the winning percentage. Although this is not the ultimate regression version, it shows us with marginal regression relationships between each changing and the earning percentage.

The details of the results are presented in Appendix III. The limited regressions expose that a few of the variables are usually more influential to the winning percentage than others, but all of us note that this may not be the final regression model but. On close examination, we observe that Opponent’s 3-point every game makes up very little from the chances of earning a game, and fact is negatively correlated with percentage wins of any team.

A similar case develops concerning Team’s turnover every game, only that the relationship is even weaker below. The same is true of Team’s rebound per game. The rest exhibit a positive relationship.

The most effective correlation visible from the spread plots is Team’s free throws every game, as well as the weakest positive correlation is Opponent’s turnover per game. 6. a couple of 6. 5. 1 7 Regression examination is a very beneficial analysis application. Moreover, with modern personal computers, data evaluation is even easier (and occasionally fun) to handle. The final style we have been in a position to come up with will assist in forecasting the successful chance of a basketball group. We would like to mention here which our model would not have marvelous powers of prediction.

The predictive precision of the style has been set by the body of this kind of work, and shows all of us that it will not incorporate EVERY SINGLE variable that affects the winning chance of a staff. It is common relief of knowing that factors like the co-operation among team management and players, relationship between players, the skills of the players plus the support of any team’s fans play a critical role within a team’s capacity to win a game, and so carry out many other elements. Yet these factors can not be quantitatively referred to so as to end up being included in the model. Nevertheless, we believe that the factors we have reviewed have extremely important roles to learn, and therefore should not be ignored.

We therefore advise, based on the findings, that a team will need to strategize their game so as to minimize their particular turnovers, since from our version they have the strongest unfavorable effect on their particular winning possibility. Similarly, the opponent’s recurring will do harm. On the other hand, a basketball crew should, whenever possible, maximize their particular 3-point shots, free throws, rebounds and the opponent’s renouvellement, since in respect to our version, these possess a positive effect on their winning chance.

Finally to the sports fan, you can really know what to expect from a staff if you can take notice of the above-mentioned variables. So , instead of raising the heart rate in blind expectation, you can examine for yourself the opportunity that your selected team will never let you down. Meanwhile, we wish you the best of luck! 8 APPENDIXES almost 8. 1 APPENDIX I: Descriptive Statistics to get the parameters 1 . Detailed Statistics Variable N N* Mean SE Mean StDev Variance Minimal Winning percentage 68 zero 0. 5946 0. 0197 0. 1625 0. 0264 0. 2333 Opp 3-point per game 68 zero 6. 318 0. 107 0. 880 0. 774 3. 788 3-point every game 68 0 6. 478 zero. 161 1 . 326 1 . 757 3. 645 Free throws per game 68 0 14.

203 zero. 280 2 . 307 five. 323 8. 536 Turn-over, pg 68 0 14. 086 zero. 164 1 . 355 1 . 835 12. 974 Adversary Turn-over, pg 68 zero 14. 755 0. hundranittiotv? 1 . 583 2 . 506 11. 438 Home rebound per game 68 zero 35. 380 0. 389 3. 209 10. 297 27. 323 Oppnt rebound per video game 68 zero 33. 841 0. 258 2 . 128 4. 528 28. 970 Variable Q1 Median Q3 Maximum Range IQR Successful percentage 0. 4707 zero. 5938 0. 7403 zero. 9487 0. 7154 zero. 2696 Opp 3-point per game 5. 688 6. 323 six. 956 almost eight. 138 some. 350 1 . 268 3-point per game 5. 782 6. 433 7. 413 9. 471 5. 825 1 . 631 Free throws per game 12. 619 14. 322 15. 883 19. 568 11. 032 3. 264 Turn-over, pg 13. 116 14. 000 14. 875 17. 656 6. 682 1 . 759 Opponent Turn-over, pg 13. 574 13. 769 12-15. 514 18. 406 6th.

969 1 . 939 House rebound every game 33. 304 thirty-five. 383 thirty seven. 063 45. 548 18. 226 a few. 758 Oppnt rebound every game thirty-two. 611 33. 754 35. 047 39. 938 15. 968 installment payments on your 436 installment payments on your Descriptive Stats: Winning percentage Variable D N* Imply SE Mean StDev Minimal Q1 Median Winning percentage 68 zero 0. 5946 0. 0197 0. 1625 0. 2333 0. 4707 0. 5938 Variable Q3 Maximum IQR Variance Range Winning percentage 0. 7403 0. 9487 0. 2696 0. 026 o. 7154 8. two APPENDIX II: Box And building plots for the variables 8. 3 APPENDIX III: Spread Plots (With Corresponding Regression Equations) Regression Analysis: Winning percentage vs Opp 3-point per video game The regression equation is Winning percentage = 0. 729 0. 0212 Opp 3-point per game S sama dengan 0. 162686 R-Sq sama dengan 1 . 3% R-Sq(adj) sama dengan 0. 0% Regression Evaluation: Winning percentage versus 3-point per video game The regression equation is definitely Winning percentage = zero.

397 + 0. 0304 3-point every game S i9000 = zero. 158646 R-Sq = six. 2% R-Sq(adj) = 5. 7% Regression Analysis: Earning percentage vs . Free throws per video game The regression equation is Winning percentage = zero. 058 & 0. 0378 Free throws per game S = 0. 138185 R-Sq sama dengan 28. 8% R-Sq(adj) sama dengan 27. seven percent Regression Examination: Winning percentage versus Turn-over, pg The regression equation is Earning percentage = 1 . 18 0. 0387 Turn-over, pg S i9000 = 0. 155019 R-Sq = twelve.

4% R-Sq(adj) = 9. 0% Regression Analysis: Earning percentage vs . Opponent Turn-over, pg The regression equation is Earning percentage sama dengan 0. 293 + zero. 0204 Opponent Turn-over, pg S sama dengan 0. 160503 R-Sq sama dengan 4. 0% R-Sq(adj) sama dengan 2 . 5% Regression Evaluation: Winning percentage versus House rebound per game The regression equation is Successful percentage = 0. 243 + 0. 0237 Home rebound per video game S = 0. 144773 R-Sq sama dengan 21. 9% R-Sq(adj) = 20.

7% Regression Evaluation: Winning percentage versus Oppnt rebound per game The regression equation is Successful percentage sama dengan 1 . 44 0. 0249 Oppnt rebound per game H = 0. 154803 R-Sq = 12. 7% R-Sq(adj) = on the lookout for. 3% almost eight. 4 APPENDIX IV: Multiple Regression Details Regression Analysis: Winning perc in short versus 3-point per, Cost-free throws, The regression equation can be Winning percentage = zero. 633 & 0. 0224 3-point every game & 0. 0176 Free throws per video game 0. 0622 Turn-over, pg & 0. 0414 Opponent Turn-over, pg + 0. 0267 Home recurring per video game zero.

0296 Oppnt rebound per game 0. 0172 Opp 3-point per video game Predictor Coef SE Coef T L Constant 0. 6327 0. 2123 installment payments on your 98 zero. 004 3-point per video game 0. 022369 0. 007221 3. twelve 0. 003 Free includes per game 0. 017604 0. 005720 3. 08 0. 003 Turn-over, pg -0. 062214 0. 007380 -8. 43 0. 1000 Opponent Turn-over, pg 0. 041398 zero.

006398 6th. 47 0. 000 Residence rebound per game 0. 026699 0. 004175 six. 39 0. 000 Oppnt rebound per game -0. 029645 0. 004594 -6. 45 zero. 000 Opp 3-point per game -0. 01724 0. 01130 -1. 53 zero. 132 S i9000 = 0. 0747588 R-Sq = 81. 1% R-Sq(adj) = 80. 8% Research of Difference Source DF SS MS F G Regression 7 1 . 43486 0. 20498 36. sixty-eight 0. 000 Residual Problem 60 zero. 33533 0. 00559 Total 67 1 . 77019 Supply DF Seq SS 3-point per game 1 zero. 10906 Free of charge throws every game one particular 0. 53614 Turn-over, pg 1 0. 24618 Opposition Turn-over, pg 1 0. 13117 Home rebound every game you 0. 13403 Oppnt recurring per game 1 0. 26527 Opp 3-point per game 1 0. 01302 Unusual Observations 3-point Successful Obs every game percentage Fit APRENDI Fit Recurring St Resid 2 5. 59 zero.

79412 0. 63575 0. 02114 zero. 15837 2 . 21R twenty-seven 6. sixty 0. 76667 0. 60456 0. 01272 0. 16211 2 . 20R 30 6. 21 zero. 50000 zero. 65441 zero. 01571 -0. 15441 -2. 11R forty five 4. 75 0. 25000 0. 39253 0. 02404 -0. 14253 -2. 01R R means an declaration with a significant standardized recurring.

8. 5 APPENDIX Versus: Residuals and building plots for the 8 factors 8. six APPENDIX NI: Best Subsets Regression Ideal Subsets Regression: Winning perc in short versus Opp 3-point, 3-point per, Response can be Winning percentage O Um H l O Farreneheit p to p p r l m d p assim como o e big t e d 3 a few e ur r t n e e g p they would t b b u o l T to o my spouse and i i o u Big t u u n d w r u n n big t t t n 3rd there’s r d d n p l p o g p elizabeth e at the v u e at the r 3rd there’s r r electronic v r r ur e g g g, r g g a a a, a a Mallows m m m p g m m. Vars R-Sq R-Sq(adj) Cp S elizabeth e elizabeth g g e elizabeth 1 twenty-eight. 8 28. 7 161. 5 0. 13818 Back button 1 21 years old. 9 twenty.

7 183. 5 zero. 14477 X 2 46. 9 forty five. 3 106. 1 0. 12021 Times X a couple of 41. 2 39. some 124. 5 0. 12658 X X 3 fifty five. 2 53. 1 seventy eight. 7 zero. 11126 Back button X Times 3 fifty four. 9 52. 8 82. 9 zero. 11172 By X Times 4 73. 8 seventy two. 2 twenty-four. 9 zero. 085772 Back button X Back button X 5 65. 1 62. on the lookout for 52. 5 0. 098958 X Back button X Times 5 seventy seven. 7 75. 9 13. 6 zero. 079790 By X Times X X 5 seventy six. 8 74. 9 18. 6 zero. 081431 By X Back button X X. 6 70. 3 78. 4 eight. 3 zero. 075569 X X X X Times X 6 78. 1 75. being unfaithful 15. your five 0. 079781 X Back button X Back button X Back button 7 seventy eight. 1 78.

8 8. 0 0. 074759 Back button X Back button X Times X Times 8. several APPENDIX VII: Regression Evaluation with Five Variables Regression Analysis The regression formula is Earning percentage = 0. 528 + 0. 0250 3-point per video game zero. 0631 Turn-over, pg + 0. 0471 Opponent Turn-over, pg + 0. 0349 Home recurring per game 0. 0336 Oppnt rebound every game Predictor Coef ZE Coef To P Regular 0. 5280 0. 2213 2 . 39 0. 020 3-point per game zero. 025031 zero.

007617 three or more. 29 0. 002. Turn-over, pg -0. 063103 0. 007859 -8. 03 0. 000 Challenger Turn-over, pg 0. 047061 0. 006531 7. 21 years old 0. 000 Home rebound per game 0. 034908 0. 003176 10.

99 0. 1000 Oppnt rebound per game -0. 033572 0. 004713 -7. doze 0. 000 S sama dengan 0. 0797903 R-Sq sama dengan 77. 7% R-Sq(adj) = 75. 9% Analysis of Variance Origin DF SS MS F P Regression 5 1 . 37547 0. 27509 43.

21 0. 000 Recurring Error sixty two 0. 39472 0. 00637 Total 67 1 . 77019 Source DF Seq DURE 3-point every game you 0. 10906. Turn-over, pg 1 zero. 13137 Challenger Turn-over, pg 1 zero.

15696 Residence rebound every game one particular 0. 65508 Oppnt recurring per video game 1 zero. 32300 Unconventional Observations 3-point Winning Obs per video game percentage Match SE In shape Residual Street Resid 8 4. 13 0. 83333 0. 66281 0. 02375 0. 17053 2 . 24R 13 6th. 79 0. 55172 zero. 72095 0. 02073 -0. 16923 -2.

20R 27 6. 60 0. 76667 0. 60253 0. 01331 0. 16414 2 . 09R 30 six. 21 zero. 50000 0. 66321 zero. 01474 -0. 16321 -2. 08R forty-five 4. seventy five 0. 25000 0. 41575 0. 02187 -0. 16575 -2. 16R. R denotes an declaration with a huge standardized residual. APPENDIX VII (Continued): Descriptive Statistics intended for five Variables Descriptive Stats Variable N N* Indicate SE Imply StDev Variance Minimum Successful percentage sixty-eight 0 zero. 5946 zero. 0197 zero. 1625 0. 0264 zero. 2333 3-point per game 68 zero 6. 478 0. 161 1 . 326 1 . 757 3. 645 Turn-over, pg 68 0 14. 086 0. 164 1 . 355 1 . 835 10. 974 Opponent Turn-over, pg sixty-eight 0 13. 755 zero. 192 1 ) 583 2 . 506 10. 438 Home rebound every game sixty-eight 0 35. 380 0. 389 3. 209 15. 297 28.

323 Oppnt rebound every game 68 0 thirty-three. 841 0. 258 installment payments on your 128 5. 528 twenty eight. 970 Varying Q1 Typical Q3 Maximum Range IQR Winning percentage 0. 4707 0. 5938 0. 7403 0. 9487 0. 7154 0. 2696 3-point every game 5. 782 six. 433 7. 413 9. 471 5. 825 1 ) 631 Turn-over, pg 13. 116 14.

000 14. 875 18. 656 6th. 682 1 ) 759 Adversary Turn-over, pg 13. 574 14. 769 15. 514 18. 406 6. 969 1 . 939 Home rebound per video game 33. 304 35. 383 37. 063 45. 548 18. 226 3. 758 Oppnt rebound per video game 32. 611 33. 754 35. 047 39. 938 10. 968 2 . 436 8. almost eight. APPENDIX VIII: Residual And building plots for a few variables 8. 9 APPENDIX IX: Regression Excluding Recurring Outliers Regression Analysis: The regression formula is Winning percentage = 0. 487 + 0. 0184 Cost-free throws every game + 0. 0240 Opponent Turn-over, pg & 0. 0188 Home recurring per game 0. 0303 Oppnt rebound every game 0. 0243 Opp 3-point per video game Predictor Coef SE Coef T S Constant 0. 4873 0. 2956 1 . 65 zero. 105 Free throws every game 0. 018444 zero. 009412 1 ) 96 zero.

055 Challenger Turn-over, pg 0. 024021 0. 009784 2 . 46 0. 017 Home rebound per game 0. 018835 0. 006555 2 . 87 0. 006 Oppnt rebound per video game -0. 030258 0. 007625 -3. 97 0. 500 Opp 3-point per game -0. 02428 0. 02129 -1. 18 0. 259 S sama dengan 0. 118905 R-Sq = 49.

8% R-Sq(adj) = 45. 7% Analysis of Variance Resource DF SS MS Farrenheit P Regression 5 0. 84309 zero. 16862 14. 93 0. 000 Left over Error 62 0. 84831 0. 01414 Total sixty-five 1 . 69140 Source DF Seq DURE Free punches per video game 1 zero. 47458 Adversary Turn-over, pg 1 0. 03295 Residence rebound per game you 0. 04175 Oppnt recurring per video game 1 zero.

27543 Opp 3-point every game 1 0. 01839 Unusual Observations Free punches Winning Obs per video game percentage In shape SE Suit Residual Saint Resid doze 12. a couple of 0. 3333 0. 5854 0. 0270 -0. 2521 -2. 18R 34 doze. 2 zero.

9487 0. 6218 0. 0297 zero. 3269 installment payments on your 84R forty two 14. a few 0. 2333 0. 5227 0. 0400 -0. 2893 -2. 58R 43 doze. 5 0. 2500 zero. 4925 0. 0367 -0. 2425 -2. 14R R denotes a great observation with a large standardized residual. almost eight. 10 APPENDIX X: Regression with 6th Variables Regression Analysis: Successful perc compared to 3-point every, Free punches, The regression equation is Earning percentage sama dengan 0. 565 + 0. 0239 3-point per game + zero. 0163 Free of charge throws every game 0. 0630 Turn-over, pg + 0. 0436 Opponent Turn-over, pg + zero. 0265 Residence rebound per game 0. 0310 Oppnt rebound per video game Predictor Coef SE Coef T G Constant zero. 5654 zero. 2100 installment payments on your 69 zero.

009 3-point per video game 0. 023949 0. 007224 3. 32 0. 002 Free tosses per game 0. 016290 0. 005717 2 . 85 0. 006 Turn-over, pg -0. 062984 0. 007443 -8. 46 0. 000 Opponent Turn-over, pg 0. 043571 0. 006305 6. 91 zero. 000 House rebound per game 0. 026482 0. 004218 6th. 28 0. 000 Oppnt rebound per game -0.

031028 zero. 004552 -6. 82 0. 000 S i9000 = 0. 0755690 R-Sq = 80. 3% R-Sq(adj) = 80. 4% Examination of Variance Source DF SS MS F G Regression 6th 1 . 42184 0. 23697 41.

40 0. 000 Residual Error 61 zero. 34835 zero. 00571 Total 67 1 ) 77019 Supply DF Seq SS 3-point per video game 1 zero. 10906 Free throws per game you 0. 53614 Turn-over, pg 1 zero. 24618 Challenger Turn-over, pg 1 zero.

13117 House rebound every game 1 0. 13403. Oppnt rebound per game 1 0. 26527 Unusual Observations 3-point Winning Obs per video game percentage Suit SE Match Residual Street Resid twenty seven 6. 70 0. 76667 0. 60084 0. 01262 0. 16582 2 . 23R 44 6. 03 0. 23333 0. 38536 0. 02559 -0. 15202 -2.

14R forty five 4. 75 0. 25000 0. 41158 0. 02076 -0. 16158 -2. 22R R denotes an declaration with a significant standardized recurring. 8. eleven APPENDIX XI: Residual Plots for the 6-variable Style 8. 12 APPENDIX XII (a): A final Regression Version. Regression Analysis: Winning perc in short versus 3-point per, Free of charge throws, The regression equation can be Winning percentage = zero.

604 + 0. 0226 3-point per game + 0. 0167 Free punches per game zero. 0660 Turn-over, pg + 0. 0420 Opponent Turn-over, pg + 0. 0256 Home rebound per video game 0. 0292 Oppnt rebound per game Predictor Coef SONY ERICSSON Coef To P Frequent 0. 6038 0. 2065 2 . 92 0. 005 3-point every game 0. 022564 zero. 007108 three or more.

17 0. 002 Totally free throws every game zero. 016706 zero. 005600 2 . 98 0. 004 Turn-over, pg -0. 066016 0. 007456 -8. 85 zero.

000 Adversary Turn-over, pg 0. 041969 0. 006229 6. 74 0. 500 Home rebound per game 0. 025649 0. 004152 6. 18 0. 500 Oppnt recurring per game -0. 029173 0. 004561 -6. forty five 0. 500 S = 0. 0739739 R-Sq = 80. 8% R-Sq(adj) = 78. 8% Analysis of Variance Supply DF DURE MS Farrenheit P Regression 6 1 ) 37853 0. 22976 41.

99 0. 000 Residual Error 70 0. 32833 0. 00547 Total sixty six 1 . 70686 Source DF Seq SS 3-point every game 1 0. 10202 Free tosses per game 1 0. 50620 Turn-over, pg you 0. 30758 Opponent Turn-over, pg one particular 0. 11512 Home recurring per game 1 zero. 12372. Oppnt rebound per game one particular 0. 22390 Unusual Findings 3-point Successful Obs per game percentage Fit SE Fit Residual St Resid 26 6th.

60 zero. 76667 zero. 60237 0. 01238 zero. 16429 installment payments on your 25R 30 6. 21 years old 0. 50000 0. 64694 0. 01477 -0. 14694 -2. 03R 43 6th. 03 zero. 23333 0. 38546 0. 02505 -0. 15213 -2. 19R forty-four 4. 75 0. 25000 0. 41580 0. 02045 -0. 16580 -2. 33R R means an declaration with a significant standardized left over.

APPENDIX XII (b): Left over Plots to get the final regression model. APPENDIXXII (b): Extended REFERENCES Make sure you state the source of data here.