# Statistics Question

DirectionsUsing the “North Valley Real Estate” Excel Dataset located in the “Files” Section of the
course (Left-hand Side Menu Bar). The Final Research Project for the course, and all
supporting assignments to the project, will be executed using the “North Valley Real
Estate” dataset.
Please make sure that your paper conforms to APA style requirements, 7th edition.
General Guidelines for a Successful Capstone Term Project
Report include the following:
1. Provide a general introduction, background, and purpose of the paper, with
your thesis resting on the idea of using statistical analysis to achieve better
include a discussion of the real estate industry and the impacts that influence
the health, viability, and success of the real estate marketplace; particularly in
the Northeastern region of the U.S.
2. State why the dependent variable has been chosen for analysis. Then make a
general statement about the model you will be employing, for example:
“The dependent variable _______ is determined by variables ________, ________, ________,
and __________.”
3. Identify the primary independent variable and defend why it is important by
stating:
“The most important independent variable in this analysis is ________ because _________.”
In your paragraphs, cite and discuss the research sources/references that support the
thesis, i.e., the model you have chosen.
4. Write the general form of the regression model (less intercept and
coefficients), with the variables named appropriately so the reader can identify
each variable at a glance:
Dep_Var = Ind_Var_1 + Ind_Var_2 + Ind_Var_3

o
For instance, a typical model would be written:
Price_of_Home = Square_Footage + Number_Bedrooms + Lot_Size.

o

Price_of_Home: brief definition of dependent variable
Square_Footage: brief definition of first/primary
independent variable
Number_Bedrooms: brief definition of second
independent variable
Lot_Size: brief definition of third independent variable
5. Define and defend all variables, including the dependent variable, in a single
paragraph for each variable. Also, state the expectations for each independent
variable. These paragraphs should be in numerical order, i.e., dependent
variable, X1, then X2, etc. In each paragraph, the following should be

o

How is the variable defined in the data source?
Which unit of measurement is used?
For the independent variables: why do the independent
variables determine the dependent variable?
What sign is expected for the independent variable’s
coefficient, positive or negative? Why?
6. Data Description: Describe the data and identify the data sources. From
which general sources and from which specific tables are the data
taken? Which year or years were the data collected. Are there any data
limitations?
7. Presentation and Interpretation of Results. Write the regression (prediction)
equation:
Dep_Var = Intercept + c1 * Ind_Var_1 + c2 * Ind_Var_2 + c3 * Ind_Var_3
8. Identify and interpret the adjusted R2 (one paragraph). Define “adjusted R2,”
what does the value of the adjusted R2 reveal about the model? If the
adjusted R2 is low, how has the choice of independent variables created this
result?
9. Identify and interpret the F-test (one paragraph). Using the p-value approach,
is the null hypothesis for the F-test rejected or not rejected? Why or why
not? Interpret the implications of these findings for the model.
10. Identify and interpret the t-tests for each of the coefficients (one separate
paragraph for each variable, in numerical order): Are the signs of the
coefficients as expected? If not, why not? For each of the coefficients,
interpret the numerical value. Using the p-value approach, is the null
hypothesis for the t-test rejected or not rejected for each coefficient? Why or
why not? Interpret the implications of these findings for the variable. Identify
the variable with the greatest significance.
11. Analyze multicollinearity of the independent variables (one
paragraph); Generate the correlation matrix. Define multicollinearity. Are any
of the independent variables highly correlated with each other? If so, identify
the variables and explain why they are correlated. State the implications of
multicollinearity (if found) for the model you have created for this analysis.
12. Other (not required): If any additional techniques for improving results are
employed, discuss these at the end of the paper. As for grading, the inclusion
of additional statistical methods will be rewarded appropriately.
13. Reference Page: Use the proper format to list the works cited and place the
entire page in APA format 7th edition. Include at least 10 references from
across the spectrum of possible reference sources (books, magazines, journals,
periodicals, newspapers, videos, etc.)
General Paper Format:

o

Cover Page
Introduction
Background
Hypothesis
Variables
Data Set and Variable(s) description
Analysis
Conclusions
Recommendations
Reference page
Appendices (as needed)
record
Agent
Price
Size
Bedrooms Baths Pool (yes is 1)
Garage (Yes is 1) Days
1 Marty
206424
1820
2
1.5
1
1
33
2 Rose
346150
3010
3
2
0
0
36
3 Carter
372360
3210
4
3
0
1
21
4 Peterson
310622
3330
3
2.5
1
0
26
5 Carter
496100
4510
6
4.5
0
1
13
6 Peterson
294086
3440
4
3
1
1
31
7 Carter
228810
2630
4
2.5
0
1
39
8 Isaacs
384420
4470
5
3.5
0
1
26
9 Peterson
416120
4040
5
3.5
0
1
26
10 Isaacs
487494
4380
6
4
1
1
32
11 Rose
448800
5280
6
4
0
1
35
12 Peterson
388960
4420
4
3
0
1
50
13 Marty
335610
2970
3
2.5
0
1
25
14 Rose
276000
2300
2
1.5
0
0
34
15 Rose
346421
2970
4
3
1
1
17
16 Isaacs
453913
3660
6
4
1
1
12
17 Carter
376146
3290
5
3.5
1
1
28
18 Peterson
694430
5900
5
3.5
1
1
36
19 Rose
251269
2050
3
2
1
1
38
20 Rose
547596
4920
6
4.5
1
1
37
21 Marty
214910
1950
2
1.5
1
0
20
22 Rose
188799
1950
2
1.5
1
0
52
23 Carter
459950
4680
4
3
1
1
31
24 Isaacs
264160
2540
3
2.5
0
1
40
25 Carter
393557
3180
4
3
1
1
54
26 Isaacs
478675
4660
5
3.5
1
1
26
27 Carter
384020
4220
5
3.5
0
1
23
28 Marty
313200
3600
4
3
0
1
31
29 Isaacs
274482
2990
3
2
1
0
37
30 Marty
167962
1920
2
1.5
1
1
31
31 Isaacs
175823
1970
2
1.5
1
0
28
32 Isaacs
226498
2520
4
3
1
1
28
33 Carter
316827
3150
4
3
1
1
22
34 Carter
189984
1550
2
1.5
1
0
22
35 Marty
366350
3090
3
2
1
1
23
36 Isaacs
416160
4080
4
3
0
1
25
37 Isaacs
308000
3500
4
3
0
1
37
38 Rose
294357
2620
4
3
1
1
15
39 Carter
337144
2790
4
3
1
1
19
40 Peterson
299730
2910
3
2
0
0
31
41 Rose
445740
4370
4
3
0
1
19
42 Rose
410592
4200
4
3
1
1
27
43 Peterson
667732
5570
5
3.5
1
1
29
44 Rose
523584
5050
6
4
1
1
19
45 Marty
336000
3360
3
2
0
0
32
46 Marty
202598
2270
3
2
1
0
28
47 Marty
326695
2830
3
2.5
1
0
30
48 Rose
321320
2770
3
2
0
1
23
49 Isaacs
246820
2870
4
3
0
1
27
50 Isaacs
546084
5910
6
4
1
1
35
51 Isaacs
793084
6800
8
5.5
1
1
27
52 Isaacs
174528
1600
2
1.5
1
0
39
53 Peterson
392554
3970
4
3
1
1
30
54 Peterson
263160
3060
3
2
0
1
26
55 Rose
237120
1900
2
1.5
1
0
14
56 Carter
225750
2150
2
1.5
1
1
27
57 Isaacs
848420
7190
6
4
0
1
49
58 Carter
371956
3110
5
3.5
1
1
29
59 Carter
404538
3290
5
3.5
1
1
24
60 Rose
250090
2810
4
3
0
1
18
61 Peterson
369978
3830
4
2.5
1
1
27
62 Peterson
209292
1630
2
1.5
1
0
18
63 Isaacs
190032
1850
2
1.5
1
1
30
64 Isaacs
216720
2520
3
2.5
0
0
2
65 Marty
323417
3220
4
3
1
1
22
66 Isaacs
316210
3070
3
2
0
0
30
67 Peterson
226054
2090
2
1.5
1
1
28
68 Marty
183920
2090
3
2
0
0
30
69 Rose
248400
2300
3
2.5
1
1
50
70 Isaacs
466560
5760
5
3.5
0
1
42
71 Rose
667212
6110
6
4
1
1
21
72 Peterson
362710
4370
4
2.5
0
1
24
73 Rose
265440
3160
5
3.5
1
1
22
74 Rose
706596
6600
7
5
1
1
40
75 Marty
293700
3300
3
2
0
0
14
76 Marty
199448
2330
2
1.5
1
1
25
77 Carter
369533
4230
4
3
1
1
32
78 Marty
230121
2030
2
1.5
1
0
21
79 Marty
169000
1690
2
1.5
0
0
20
80 Peterson
190291
2040
2
1.5
1
1
31
81 Rose
393584
4660
4
3
1
1
34
82 Marty
363792
2860
3
2.5
1
1
48
83 Carter
360960
3840
6
4.5
0
1
32
84 Carter
310877
3180
3
2
1
1
40
85 Peterson
919480
7670
8
5.5
1
1
30
86 Carter
392904
3400
3
2
1
0
40
87 Carter
200928
1840
2
1.5
1
1
36
88 Carter
537900
4890
6
4
0
1
23
89 Rose
258120
2390
3
2.5
0
1
23
90 Carter
558342
6160
6
4
1
1
24
91 Marty
302720
3440
4
2.5
0
1
38
92 Isaacs
240115
2220
2
1.5
1
0
39
93 Carter
793656
6530
7
5
1
1
53
94 Peterson
218862
1930
2
1.5
1
0
58
95 Peterson
383081
3510
3
2
1
1
27
96 Marty
351520
3380
3
2
0
1
35
97 Peterson
841491
7030
6
4
1
1
50
98 Marty
336300
2850
3
2.5
0
0
28
99 Isaacs
312863
3750
6
4
1
1
12
100 Carter
275033
3060
3
2
1
1
27
101 Peterson
229990
2110
2
1.5
0
0
37
102 Isaacs
195257
2130
2
1.5
1
0
11
103 Marty
194238
1650
2
1.5
1
1
30
104 Peterson
348528
2740
4
3
1
1
27
105 Peterson
241920
2240
2
1.5
0
1
34
Township Mortgage type
Years FICO Default (Yes is 1)
2 Fixed
2
824
0
4 Fixed
9
820
0
2 Fixed
18
819
0
3 Fixed
17
817
0
4 Fixed
17
816
0
4 Fixed
19
813
0
10
813
0
2 Fixed
6
812
0
4 Fixed
3
810
0
3 Fixed
6
808
0
4 Fixed
8
806
1
9
805
1
9
801
1
1 Fixed
20
798
0
10
795
0
3 Fixed
18
792
0
9
792
1
10
788
0
3 Fixed
16
786
0
5 Fixed
2
785
0
4 Fixed
6
784
0
1 Fixed
10
782
0
4 Fixed
8
781
0
1 Fixed
18
780
0
1 Fixed
20
776
0
9
773
0
9
772
1
3 Fixed
19
772
0
3 Fixed
5
769
0
5 Fixed
6
769
0
9
766
1
3 Fixed
8
763
1
4 Fixed
2
759
1
2 Fixed
17
758
0
3 Fixed
5
754
1
4 Fixed
12
753
0
2 Fixed
18
752
0
4 Fixed
10
751
0
3 Fixed
15
749
0
2 Fixed
13
748
0
3 Fixed
5
746
0
9
741
1
5 Fixed
4
740
0
10
739
0
3 Fixed
6
737
0
1 Fixed
10
737
0
4 Fixed
8
736
0
4 Fixed
6
736
0
5 Fixed
13
735
0
10
731
0
4 Fixed
6
729
0
2 Fixed
15
728
0
4 Fixed
17
726
0
3 Fixed
10
726
0
3 Fixed
18
723
0
2 Fixed
15
715
0
1 Fixed
5
710
0
5 Fixed
8
710
0
2 Fixed
14
707
0
5 Fixed
11
704
0
4 Fixed
10
703
0
3 Fixed
10
701
0
2
675
0
5
674
1
2
673
0
1
673
0
6
670
0
8
669
1
4
667
0
3
665
0
8
662
1
2
656
0
3
653
0
7
652
1
7
647
1
5
644
1
2
642
0
3
639
0
7
639
1
6
631
1
7
630
1
3
626
0
5
626
1
6
624
1
1
623
0
8
618
1
3
618
1
7
614
0
6
614
1
7
613
0
3
609
1
1
609
0
3
605
1
1
604
0
6
601
1
8
599
1
8
596
1
6
595
1
2
595
0
3
593
0
6
591
1
8
591
1
7
590
1
3
584
1
8
583
1
1.2
Residuals
1
0.8
0.6
0.4
0.2
0
0
1.2
1.2
1
1
0.8
0.8
Years
Residuals
FICO Residual
Type code
Plot Residual
Normal Probability
Plot
Plot
0.6
0.4
0.4
0.2
0.2
0
100
0.6
0 200
0
20
300 0
400
40500
0.2
FICO
80800
60060 0.4 700
0.6
9001000.8
Type code
Sample Percentile
120
120 1
1.2

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