Running head: Statistics 2 Statistics Statistics Name: Course: Instructor: Institution: Date of Submission: Assignment #4: Model Diagnostics A fundamental requirement in the classical linear regression is that the regression error term must be normally distributed with zero mean and constant variance (Greene, 2008). The normality tests results are presented below. All the plots have values greater than the threshold probability value of 0.05 thus the null hypothesis of normality of the regression residuals could not be rejected at 5 per cent significance level. Conclusion is thus made that the regression residuals from the estimated equations followed a normal distribution. Since any linear function of normally distributed variables is considered to be normally distributed, normal distribution of the residuals had the implication that the coefficients of the estimates were also themselves normally distributed (Gujarati, 2008). The residual plot is shown below: From the residual plot it can be seen that all the residuals fall within the standard error bands thus confirming that the model is stable and can thus be used for forecasting. References Greene, W. (2008). Econometric analysis, 6th ed. . New Jersey: Pearson-Prentice Hall. Gujarati, D. (2004). Basic econometrics 4th ed. . New York: McGraw Hill Companies. Normal Probability Plot 2.6315789473684208 7.8947368421052602 13.15789473684211 18.421052631578942 23.684210526315791 28.947368421052641 34.21052631578948 39.473684210526301 44.73684210526315 50 55.26315789 4736857 60.526315789473699 65.789473684210563 71.052631578947384 76.315789473684163 81.578947368420984 86.842105263157904 92.105263157894726 97.368421052631547 10.7 11.3 11.8 11.9 12 12 12 12.4 12.5 12.6 13.1 13.2 13.4 13.5 13.5 14.2 14.5 14.5 14.6 Sample Percentile MedianSchoolYears Age Residual Plot 60 30 62 44 0 30 62 68 46 56 36 28 0 0 34 26 52 50 44 0.50878516451792 1.7144464705013149 -0.42159945482941003 0.54117037792769895 0.71299080887547295 1.269413932725179 0.26951686627728799 0.22131431339594501 -0.13472012994437299 0.22075061567252199 -1.3199768562363781 -0.18681496091028299 0.380020030213299 -1.451131014273024 -0.56052688701790399 -0.116260966970037 -0.67291294283960901 -0.49015761805784802 -0.48430774902780499 Age Residuals RUNNING HEADER: WEEK 3 ASSIGNMENT 4 1 WEEK 3 ASSIGNMENT 4 13 Week 3 Assignment 4 Introduction In this project I selected six variables from the ' SampleDataSet.xlsx'. Among these six variables three of them were continuous and the reaming three were discrete variables. The continuous variables selected for this study are Age, WealthScore and MedianSchoolYears. The discrete variables selected for this study are NumberOfChildren, MailResponder and NumberOfCars. Analysis Age The age is a continuous variable which takes only positive values even though we usually consider the integer part of it. The descriptive statistics summary of the age variable .

1. Running head: Statistics
2
Statistics
Statistics
Name:
Course:
Instructor:
Institution:
Date of Submission:
Assignment #4: Model Diagnostics
A fundamental requirement in the classical linear regression is
that the regression error term must be normally distributed with
zero mean and constant variance (Greene, 2008). The normality
tests results are presented below.
All the plots have values greater than the threshold probability
value of 0.05 thus the null hypothesis of normality of the
regression residuals could not be rejected at 5 per cent
significance level. Conclusion is thus made that the regression
residuals from the estimated equations followed a normal
distribution. Since any linear function of normally distributed
variables is considered to be normally distributed, normal
distribution of the residuals had the implication that the

2. coefficients of the estimates were also themselves normally
distributed (Gujarati, 2008).
The residual plot is shown below:
From the residual plot it can be seen that all the residuals fall
within the standard error bands thus confirming that the model
is stable and can thus be used for forecasting.
References
Greene, W. (2008). Econometric analysis, 6th ed. . New Jersey:
Pearson-Prentice Hall.
Gujarati, D. (2004). Basic econometrics 4th ed. . New York:
McGraw Hill Companies.
Normal Probability Plot
2.6315789473684208 7.8947368421052602
13.15789473684211 18.421052631578942
23.684210526315791 28.947368421052641
34.21052631578948 39.473684210526301
44.73684210526315 50 55.26315789 4736857
60.526315789473699 65.789473684210563
71.052631578947384 76.315789473684163
81.578947368420984 86.842105263157904
92.105263157894726 97.368421052631547 10.7
11.3 11.8 11.9 12 12 12 12.4 12.5 12.6 13.1 13.2
13.4 13.5 13.5 14.2 14.5 14.5 14.6
Sample Percentile
MedianSchoolYears
Age Residual Plot
60 30 62 44 0 30 62 68 46 56 36 28 0
0 34 26 52 50 44 0.50878516451792
1.7144464705013149 -0.42159945482941003
0.54117037792769895 0.71299080887547295
1.269413932725179 0.26951686627728799

3. 0.22131431339594501 -0.13472012994437299
0.22075061567252199 -1.3199768562363781 -
0.18681496091028299 0.380020030213299 -
1.451131014273024 -0.56052688701790399 -
0.116260966970037 -0.67291294283960901 -
0.49015761805784802 -0.48430774902780499
Age
Residuals
RUNNING HEADER: WEEK 3 ASSIGNMENT 4 1
WEEK 3 ASSIGNMENT 4 13
Week 3 Assignment 4

4. Introduction
In this project I selected six variables from the '
SampleDataSet.xlsx'. Among these six variables three of them
were continuous and the reaming three were discrete variables.
The continuous variables selected for this study are Age,
WealthScore and MedianSchoolYears. The discrete variables
selected for this study are NumberOfChildren, MailResponder
and NumberOfCars.
Analysis
Age
The age is a continuous variable which takes only positive
values even though we usually consider the integer part of it.
The descriptive statistics summary of the age variable are
shown below.
Table -1: Descriptive statistics of Age
Age
Mean
46.202
Median
48
Mode
0
Standard Deviation
20.85197
Sample Variance
434.8046
Kurtosis
0.249096
Skewness
-0.67306
Range
96
Minimum
0

5. Maximum
96
Sum
92404
Count
2000
Table-1, shows that the data consists of the ages of 2000 people.
The mean age is 46.202 years, median age is 48, but the mode is
0. The largest age is 96 with variability measured by standard
deviation is 20.85.
Graph-1: Distribution of Age
The age distribution using histogram is shown is Graph-1.
Bearing a clustering at 0, the distribution of age is
approximately symmetric.
Wealth Score
Table -2: Descriptive statistics of WealthScore
WealthScore
Mean
301.8376
Median
299.01
Mode
490.46
Standard Deviation
94.35198
Sample Variance

6. 8902.296
Kurtosis
-0.66676
Skewness
0.124734
Range
390.46
Minimum
100
Maximum
490.46
Count
2000
Table-2, shows that the data consists of the wealth scores of
2000 samples. The mean score is 301.84, median age is 299.01
and the mode is 490.46. The largest age is 490.46 with
variability measured by standard deviation is 94.35.
Graph-2: Histogram of wealth score
The histogram of the wealth scores in Graph -2 shows that the
distribution is approximately symmetric and bell shape.
Median School years
Table -3: Descriptive statistics of MedianSchoolYears
MedianSchoolYears
Mean
13.26322
Median
13.2
Mode
12

7. Standard Deviation
1.424966
Sample Variance
2.030528
Kurtosis
-0.21916
Skewness
0.303654
Range
12.4
Minimum
5.7
Maximum
18.1
Count
1903
From Table-3 we can see that the sample consists of the median
school years of 1903 people. The mean of the variable is 13.26
years, median age is 13.2 and the mode is 12 years. The
maximum years is 18.1 with variability measured by standard
deviation is 1.42 years.
Graph-3: Histogram of school years
The histogram of the median school years in Graph -3 shows
that, the distribution is approximately positively skewed.
Number of Children
Table -4: Descriptive statistics of the number of children
NumberOfChildren
Mean
0.586
Median

8. 0
Mode
0
Standard Deviation
1.020355
Sample Variance
1.041125
Kurtosis
3.045176
Skewness
1.876443
Range
6
Minimum
0
Maximum
6
Sum
1172
Count
2000
The descriptive statistics of the number of children shows that
the average number of children is 0.586 with majority having no
children. Further the distribution of the number of children is
positively skewed.
Table -4: Frequency table representing the number of children
NumberOfChildren
Frequency
0
1355
1
323
2

9. 174
3
99
4
42
5
6
6
1
Graph-4: Frequency distribution of the number of children
Graph-5: Pie chart representing the number of children
Mail Responder
The mode of the variable mail responder is 2
Table 5: Frequency of the mail responder
Mail responder
Frequency
0
447
1
515
2
1038

10. Graph-6: Pie chart representing the mail responder
It shows that the mode of the variable mail responder is 2.
Number of cars
Table 6: Descriptive statistics for the number of cars
NumberOfCars
Mean
1.0313
Median
1
Mode
1
Standard Deviation
1.0557
Sample Variance
1.1146
Kurtosis
6.0731
Skewness
1.72
Range
9
Minimum
0

11. Maximum
9
Sum
956
Count
927
Hypothesis tests
The first hypothesis tested in this study is regarding the mean
age. The null hypothesis H0 was that the mean age of the
population is 50. Alternative hypothesis H1 was that the mean
age of the population is less than 50. 5% level of significance is
used to test the hypothesis. The null hypothesis was rejected (t
=-8.145, df= 1999, p value <0.000) at 5% level. Therefore, we
conclude that there is enough evidence to support the claim that
the mean age is less than 50.
Table 7: Test for the mean age
Age
hypo. Value
Mean
46.202
50
Variance
434.8046
0
Observations
2000
2000
Pearson Correlation
#DIV/0!
Hypothesized Mean Difference

12. 0
df
1999
t Stat
-8.1456
P(T<=t) one-tail
3.29E-16
t Critical one-tail
1.645616
P(T<=t) two-tail
6.57E-16
t Critical two-tail
1.961151
Now the hypothesis tested in this study is regarding the mean
wealth score. The null hypothesis H0 was that the mean wealth
score of the population is 300. Alternative hypothesis H1 was
that the mean wealth score of the population is not equal to 300.
The null hypothesis was not able to rejected (t 0.871003, df=
1999, p value = 0.389) at 5% level. Therefore, we conclude that
there is not enough evidence to support the claim that the mean
wealth score is different from 300. Details are given in Table -
8
Table 8: Test for the mean wealth score
WealthScore
Null wealth

13. Mean
301.8376
300
Variance
8902.296
0
Observations
2000
2000
Pearson Correlation
#DIV/0!
Hypothesized Mean Difference
0
df
1999
t Stat
0.871003
P(T<=t) one-tail
0.191929
t Critical one-tail
1.645616
P(T<=t) two-tail
0.383857
t Critical two-tail
1.961151
The third hypothesis tested in this study is regarding the mean
value of the median school years. The null hypothesis H0 was

14. that the mean school year of the population is 12 years.
Alternative hypothesis H1 was that the mean school year of the
population is greater than 12 years. The null hypothesis was
rejected (t =38.672, df= 1902, p value <0.000) at 5% level.
Therefore, we conclude that there is enough evidence to support
the claim that the mean of teh median school years is greater
than 12.
Table 9: Test for the mean school years
MedianSchoolYears
Null schoolyears
Mean
13.26322
12
Variance
2.030528
0
Observations
1903
1903
Pearson Correlation
#DIV/0!
Hypothesized Mean Difference
0

15. df
1902
t Stat
38.67163
P(T<=t) one-tail
3.4E-242
t Critical one-tail
1.645655
P(T<=t) two-tail
6.9E-242
t Critical two-tail
1.961212
Now a chi-square test is conducted to test the independence of
the number of children and the number of Cars. The null
hypothesis H0 was the number of children and the number of
cars is independent. The alternative hypothesis H1 is that the
number of children and the number of cars is dependent
Table 10: Contingency table
zero
one
two
three
four
Five
Six above
Total
zero

16. 940
274
94
32
8
3
1
1352
one
223
51
30
16
1
1
1
323
two
113
35
15
6
2
0
1
172
three
75
14
6
2
1

17. 0
1
99
four
24
13
4
0
0
0
1
42
Five
5
1
0
0
0
0
0
6
Six
1
0
0
0
0
0
0
1
Total
1381

18. 388
149
56
12
4
5
1995
Table 11: Hypothesis test
35.99
chi-square
36
df
.4691
p-value
The null hypothesis was not to reject ( = 35.99, df=36, p value
<0.4961 ) at 5% level. There is not enough evidence that the
number of children is independent of the vehicles.
Limitations of the study
There were a number of missing observations in the variable,
number of cars. The t test assumes that the distribution of the
population is normal. But we have not conducted the study for
normality of the populations.

19. References
Doane and Seward (2010), Applied Statistics in Business and
Economics: the McGraw Hills Ltd.
Linda, Marchal, and Wathen (2008), Statistical Techniques in
Business & Economics, 13th
edition. New York, NY: McGraw Hill.
Number of Children
NumberOfChildren 0 1 2 3 4 5 6
Frequency 1355 323 174 99 42 6 1
Number of childrens
zero one two threefour five six 1355 323 174 99 42 6
1 Frequency zero one two 447 515 1038
Histogram of Age
Frequency 0 5 10 15 20 25 30 35 40 45
50 55 60 65 70 75 80 85 90 95 100
More 202 0 0 0 1 29 128 111 206
173 265 171 240 158 118 65 89 24 15 2 3
0
Age
Frequency
Distribution of WealthScore
Frequency 0 25 50 75 100 125 150 175 200

20. 225 250 275 300 325 350 375 400 425 450 475
500 More 0 0 0 0 9 26 64 82
118 167 183 172 192 179 174 176 138 98 64 58
100 0
WealthScore
Frequency
Histogram of MedianSchoolYears
Frequency 5 6 7 8 9 10 11 12 13 14
15 16 17 18 More 0 1 0 0 0 1
42 405 452 447 298 204 45 7 1
MedianSchoolYears
Frequency
Sheet1HouseholdIDCityStateMSACodeGenderAgeWealthScore
NumberOfChildrenMailResponderMedianSchoolYearsNumberO
fCarsAB100794446ManchesterCTF60141.120112AgeWealthSco
reMedianSchoolYearsBL103110217AnkenyIAM30246.380014.5
Mean46.202Mean301.83762Mean13.2632159748AL100297826L
ake
TappsWAM62375.330213.5Median48Median299.01Median13.2
AK103361183Grand
RapidsMIM44333.884213.42Mode0Mode490.46Mode12AP1008
08185O FallonMOF0336.181214.52Standard
Deviation20.8519686912Standard
Deviation94.3519774088Standard
Deviation1.4249661236AL102281729KnoxvilleTNM30323.682
214.61Sample Variance434.8045982991Sample
Variance8902.2956409562Sample
Variance2.0305284533AA100557115CambridgeMN5120F62332
.890213.51Kurtosis0.2490959725Kurtosis-
0.6667619166Kurtosis-
0.2191568703AU102418311StoughtonMA1120M68384.210114.
2Skewness-
0.6730571911Skewness0.1247339266Skewness0.3036538561BS
102737313RaymondNH4160F46295.392112.60Range96Range39
0.46Range12.4BQ103379666North

21. MankatoMNM56274.670213.12Minimum0Minimum100Minimu
m5.7AX102690269Los
AngelesCA4480M36363.492212.4Maximum96Maximum490.46
Maximum18.1AJ102079864AzleTX2800F28190.131011.8Count
2000Count2000Count1903BG101542955EasthamptonMA8000M
0217.760013.2AM100685288Las
VegasNV4120F0270.390012BF101795019RossvilleGA1560F34
102.30010.7AS101518382Lehigh
AcresFL2700M26174.670011.91NumberOfChildrenBT10258114
6West
ColumbiaSC1760M52376.972212.5Mean0.586AE102386836Wa
rner
RobinsGA4680F50180.261011.3Median0BT101916705AkronO
H80F44244.0812120Mode0AF101535688JohnstonRI6480M4835
7.571212.2Standard
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1.50Sample Variance1.0411245623AR100318955Grants
PassORF82284.210211.92Kurtosis3.0451756334AH100794933S
alemORF38261.513012.30Skewness1.87644329AB101731499So
mers
PointNJ560F66234.210012.3Range6BS100678069PetalumaCA7
500M66490.460214.6Minimum0BB100821672MoosupCTF4631
0.534212.1Maximum6BA102835475CovingtonGA520M66299.3
41211.5Sum1172AK100931054DillonSCF54190.131011Count2
000AP102795843FlemingtonMOF46191.120211.52AY10259939
6CerritosCA4480M64490.460215.3BH102034931ColumbiaSC1
760F52203.290015.2BT102671792WalhallaSCM68151.320111.
5AP102934858EdenNY1280M38285.862113.51NumberOfChildr
enFrequencyAR102810155CorvallisMTF64274.670213.1001355
AG102694587SalinaKSF76186.840213.31323AA100276439Cre
stviewFL2750F58229.28011212174AZ104193154BaltimoreMD7
20M38369.7412399AL101833829Chicago
HtsIL1600F0127.960211.4442AX100815576CovingtonVAF382
47.72111.956BT100779542Holly Lake
RaTXM70136.840112.8161AT103418941Crp
ChristiTX1880M88325.990214.12AF102360978Queens

22. VlgNY5600M58349.340212.31AH105381822DallasOR7080F02
06.2500AT103049290LititzPA4000F52200.660010.8BM102055
805LanettALM34178.950211.42AN103221326MishawakaIN780
0M48332.241213.7BK102944161WestfieldNY3610M46283.552
111.91BE102692940Walnut
CreekCA5775M46376.641015.3AM107675543Mount
AiryNCM46144.0802AD102631952W
LafayetteOHF50245.070011.91BL103145929BonoAR3700M401
53.290111.5BL101632308LavoniaGAF0191.450011.5BF102458
607MoorefieldWVF441000111.23NumberOfCarsBA102412106
El DoradoARF44232.570011.9Mean1.0313AH107223164San
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60M44234.870211.20Mode1AV101609047Rock
SpringsWYM46307.241212.40Standard
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ValleyAZF78312.170214.32Sample
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BaltimoreMD720F0160.20211.30Sum956BP103770881Springda
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HastingsMN5120F38397.374113.10AG102902028Mount
GileadNCF54158.550011.1AC103075969WestlandMI2160M682
93.420112.10AA103111338RayneLA3880M46235.861111.12BN
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BchVA5720M58304.930014BC102673603SheboyganWI7620F2
4155.260012.20BA100278000DecaturGA520F56239.140213.4B
N102583295Virginia
BchVA5720F50259.870213.1BT101169040WilliamstonSC3160
M40260.860213.2AE102603986MorgantonNC3290M32256.251
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2.171212AF101533001RoanokeTX1920M76376.9702152BT103
425202HarleysvillePA6160F82379.930213.9AY102734820Sn
BernrdnoCA6780M70411.841213BK101282194Arlington
HtsIL1600M34399.672215AG901164163Santa
FeNM7490M48338.1602BD102123770JupiterFL8960F56400.99

63. 1213.13BC103358905Seven
HillsOH1680M48317.434212.11BT102528985San Luis
ObisCA7460M50490.461215.4AP103222000TraffordPA6280F3
2249.013212.4BB100039991Central
IslipNY5380F42379.280212.42AN102652529Apple
ValleyCA6780M40308.553113.9AL103228013MokenaIL1600F4
8376.645213.4AL101471353NeptuneNJ5190F36325.330113.4B
P100840453UnderhillVT1305F50465.791215.8BJ103782997Bat
aviaOH1640F52330.260214.11AW102942728StoughtonWI4720
F50360.20213.31BQ102760132Granite
CityIL7040F32253.623212.2AQ102546037ValenciaCA4480M54
490.460215AU100845852LouisvilleKY4520F0314.140014.61B
H102664593CoronaCA6780M46487.833213.9AG102794205Stra
tfordCT1160M56356.250112.4BJ103284147DillsburgPA9280M
66316.120213.1AN103627090TewksburyMA4560M58448.3601
14.8BG102602110KenoshaWI3800M62368.090212.40BK10213
1850Buffalo GroveIL1600F42344.410215.5BM102884888San
DiegoCA7320M64405.261212.9BF103108156N Las
VegasNV4120M76307.240213.3AK103047940RenoNV6720M58
454.280214.5BQ102976715HarrisburgPA3240M48335.531213.2
AG102371958StratfordCT1160M60400.330212.4AT101309159
Rockaway ParkNY5600M66490.4612150BP103064089Tipp
CityOH2000F54395.390212.31BC100502133SomersetMA6480F
30353.951112.8BD102308139Laguna
HillsCA5945M58426.320214.3AS102510283LombardIL1600M6
2367.110012.7AC103123450RockwoodMI2160F52347.041212.7
0BM102125168FerndaleWA860M72330.920113.3AG102621965
NewingtonCT3280M48444.411215.3AW101052102Manchester
NJ5190M58367.110012.7AW103213567LexingtonKY4280F762
74.0101130BT102849710Oak
RidgeNJ875F62420.720213.5AK102965056ShirleyMA1120F48
248.030014.2BQ102993323New
YorkNY5600M46490.460215.9AH101671419WoodburyNJ6160
M40405.261213.2BR102737348Shelby
TownshMI2160M72356.581112.81AP102772584ForistellMO70
40M44368.423212.42AJ100023689Cortlandt

64. MnrNY5600M48397.371212.82BP103180794Yuba
CityCA9340M40324.013212.9AN103507705Apple
ValleyCA6780F80314.80213.1BP102785505VandaliaOH2000M
60336.510213.60AY100258704CarsonCA4480F46358.221213.3
BK102784653InglesideIL1600F80339.80213.1AW103091195Cr
oss
PlainsWI4720M64334.210213.70BG102829579HopewellNJ8480
M36446.383214.2BB100868897WyandanchNY5380F50450.332
213.71AB102883081ColumbiaIL7040M50349.011113.4BP1004
09389RutherfordNJ875F48370.070113.1BS102569668Placervill
eCA6920F62367.110013.5AB100482505East
GranbyCT3280M32402.633115.2BT102317546StockholmNJ564
0F34194.080112.8BB100003805HollywoodFL2680F0246.05001
3.2AQ107227016San
FranciscoCA7360M30490.4632AF102795829TucsonAZ8520M4
8351.971014.30AN103149892RayMI2160F52352.30212.92AC1
02773555Little
NeckNY5600M70328.951113.80BB100219772Deer
ParkNY5380F36261.840012.12AS101288827ScottsdaleAZ6200
M42402.630115.11AM103061415ElktonMD9160F54361.840012
.72BJ103347311EdenUT7160F60391.450214.11AQ102348266G
reenvilleRI6480M48415.461213.9AF100672533TucsonAZ8520
M0279.930014.11AF103186781Salt Lake
CtyUT7160M38371.381214.11AC101388775RoselleNJ5640M48
353.290212.7AU100688283PiscatawayNJ5015F36460.23115.3A
B102508782VancouverWA6440M56417.430113.6BN102069864
Forest
HillMD720M52466.781213.31BK103202210AntiochIL1600F26
324.011213BN102118796CaryNC6640F64477.961215.7AL1034
30099Staten
IslandNY5600F38359.870112.41AB103026306FarmingtonCT32
80M40381.580114.7BL107327910OcoeeFL5960F54324.0112BP
100315088ClarkNJ5640M26304.930012.1BS102594120Rocklin
CA6920M46466.451214.3AF102533256Grand
LedgeMI4040F52352.630213.21AR102352691OgdenUT7160M4
2307.574113.31BR102821328BallwinMO7040M54346.380115.3

65. 0BG101108337ColumbiaMO1740M44420.392215.60AS1022377
14MesaAZ6200M72355.5902140AZ101874366PikesvilleMD720
M56326.640115.11AB103034955KimberlyWI460M52312.50213
.61AN103144711GrovelandMA4160M64388.820213.9BT10187
2008Chester
SprgsPA6160M56348.030115.1AR102406661SchaghticokeNY1
60M36328.292112.40BF102950111SavannahGA7520M74439.47
0115.4BF102929294MoorheadMN2520F48234.540212.40AL103
101533KnoxvilleTN3840F52356.251114.81AD102061190Uppr
MarlboroMD8840M68355.920213.81AQ102641292PortlandCT3
280M72398.364214.8AT103601432AmarilloTX320F40329.9311
14.82BT102032705LansdalePA6160F0264.140214.6AS1021779
48Apollo
BeachFL8280M62374.344213.32AL100501945IndianapolisIN34
80F0164.470212AY101091397LynnMA1120F743000213.1BJ10
1732775LovelandOH1640F46281.250212.61BC102845256Taunt
onMA1120M52423.031213.6AD102636794Rapid
CitySD6660M74322.70214.31AC103269767GlendaleAZ6200M3
8276.320212.80AC101418381CantonMI2160F30347.370114.73
BR102676936MontgomeryIL1600M76376.970214.4AJ10238198
9PowellOH1840M68490.460215.61BN102618493Glen
BurnieMD720F76298.030211.52BM102943206Saint
LouisMO7040M50359.873213.21BR100380873Lake
OrionMI2160M42342.110113.20BB101397315DallastownPA92
80F0232.892112.2AA100244971American
ForkUT6520M32335.21014.42AB100396402SouthavenMS4920
F68224.0101121BG102619529MonsonMA8000F44334.542212.
9AR101371264BrawleyCAM42359.874214.1BM100486998San
DiegoCA7320F58457.240215.6BK101880278Mt
ProspectIL1600M54414.80213.3BT102739773HarleysvillePA61
60M40378.291212.8BJ100040321OceansideCA7320F46438.821
113.2AJ100022267EulessTX2800F0264.80114.40AB102984405
UrbandaleIA2120M46316.453115.10AQ100962454Wallingford
CT5480M34327.30211.9BC102603328PewaukeeWI5080F32337
.170114.51BB102573607Bay
ShoreNY5380M60402.960013.63AZ101549981ClaremontCA448

66. 0F60376.970216AN101104682Clinton
TwpMI2160M54454.610113.91AN103192345BuffaloNY1280M
48368.423215.61BS102967567SaginawMI6960M52362.170213.
71AE100831295ThomastonCT8880F38334.214212.2AL1034425
15FresnoCA2840M30226.321113.7AB101039481Stafford
SpgsCT3280F64304.930212.1BG101885919JacksonNJ5190M38
465.462213.4AP100418513PoquosonVA5720M62375.660212.7
AT103213955Long
ValleyNJ5640F60415.460114.7AF101656918MinneapolisMN51
20M38265.790012.41AL101982507Hazel
CrestIL1600F26253.950013.7AU102258773BridgewaterNJ5015
F50470.070215.7BP102856709Cedar
RapidsIA1360M62365.790213.72AY102476091PerkiomenvlleP
A6160M66247.72212.2AN103414303FontanaCA6780M48360.2
3111.6AS102205959ScottsdaleAZ6200F54338.490114.10BE102
420495Grand
PrairieTX1920F44337.51214.31BN100665416CaryNC6640F402
87.50014.3BS101217463Saint
PaulMN5120M44300.660114.10AT100538010Crp
ChristiTX1880M24284.870213.11BT102895139KailuaHI3320M
76451.320113.9BR100392513ArnoldMO7040F64261.840212.32
AD102888799BowieMD8840F74328.290214.11BP108727516Ru
therfordNJ875F40385.8602BP102628677Yuba
CityCA9340F62387.830014AL103374320Tinley
ParkIL1600F58343.420113AH100932346San
AntonioTX7240F58251.320213.63AQ102318882SpringTX3360
F60326.971213.82AR102721019MedfordOR4890F66423.68021
3.53BL102637356JohnstonIA2120M54490.462215.21BH102710
624SpringfieldPA6160F483501213.1AE103200919PalmdaleCA
4480F0275.991012.7BQ101976760N
ClarendonVTF26264.82212.3BK101230319Arlington
HtsIL1600M62401.970115.1AP102738881Lake St
LouisMO7040F60328.620214.40BP103033611Overland
ParkKS3760F0418.090215.7AJ102775849MahopacNY5600F484
45.7222139BA102552680YakimaWA9260M84301.320213.9AE
100501248Battle

67. CreekMI3720F36299.340113.51AQ102390041Sandy
HookCT1930M54433.220214.5AB102789536StowOH80M62321
.050214.61AM100762017Prior
LakeMN5120M36378.291112.81AP102839878StandishME6400
M46354.6132131BG100663944CamillusNY8160M64329.93221
5.31AR102723250OgdenUT7160M62284.870213.61BC1004911
52RaynhamMA1200F0432.890013.7AR102251761CheshireMA6
320M64274.670112.2BS100732648Spring
LakeMI3000F54339.140214.92AQ102423777CoventryRI6480M
58392.430213.8BN102501156MineolaNY5380F54444.410212.8
2BK102922619PhoenixAZ6200F34282.894113.81BH102716373
S
PortlandME6400M56334.210213.41AR100964245OgdenUT716
0F34282.570113.11BP102930748PoughkeepsieNY2281M52397.
3712140AW100263041TeaneckNJ875F56298.030013.1AN1031
35799S San
FranCA7360M58490.462214.1BE102439422SacramentoCA6920
F36356.581213.5AF100706442EdgewaterFL2020F54235.53021
2.22BS102461972Saint
PaulMN5120M54316.1202141BG102587351Feeding
HillsMA8000F64354.281212.1BE102368417GarlandTX1920M5
8289.80113.63AU100000644EdisonNJ5015M42204.280012.3B
Q103914125LouisvilleOH1320F72237.170212.43AP100240563
Newport
NewsVA5720F24156.250013AH103124632KeizerOR7080F6834
1.451213.41AJ107947203ColumbusOH1840M48237.502AE102
913951LawtonMI3720F58362.170214.72BJ103592920MonroeN
C1520M48222.70112.6AV102246382MiamiFL5000M44345.720
113.72BS102230173Colorado
SpgsCO1720F40296.710213.91BS107650148RosevilleCA6920
M54357.2402AY100076675LakewoodCA4480M0286.510213.1
AA100606437ShalimarFL2750M48270.720112.5BA102760610
MillburyMA9240F56254.280212.3AY102059473New
BernNCM30304.613113.7BQ101027451AltoMI3000F28390.793
114.31AD102546310SunriseFL2680M402753213.90BJ1022317
38AmmonIDM30311.841213.62AK100168167BellevueNE5920F

68. 30311.8422140BT100935159GreerSC3160F32303.293212BQ10
0496764HarrisburgPA3240F54248.680114.1BF101670154Ringg
oldGA1560M62287.50112.4AQ102471601Granada
HillsCA4480F46218.750114.3AB102506185VancouverWA6440
M0291.450013.3AM102316401North
EastMD9160M64355.2602131AE102038418RiverdaleGA520F6
4311.841213.5AX102849866ChicagoIL1600F56251.970211.9B
R101448704AustinTX640M38274.6702141BB100498639Homes
teadFL5000F60476.320213.51BS101187772Saint
PaulMN5120F48365.130213.81BK102812104Satellite
BeaFL4900M62352.30114.22AA102864432PensacolaFL6080M6
2320.070213.61BJ103136142StanleyNC1520F50249.010112.1B
T100743468KapoleiHI3320M66365.461013.6BC103286518Seek
onkMA6480M78378.620212.7BR102772650Sterling
HtsMI2160F48368.750213.62AV100434090CharlottesvleVA154
0M84249.340214.5AJ106888134HilliardOH1840F58282.2402B
C100815607Lemon
GroveCA7320M46368.750012.7AP102602191AuburnWA7600M
50390.460213.5BA102585121RichmondKY4280M44382.89121
2.22AT103645375TonawandaNY1280F52275.660212.71AH101
351218WaynesvilleNCF32137.170012.2AG102874891TacomaW
A8200M0354.611013.2AK102545752Indian
TrailNC1520M38318.753012.6BT101367840JacksonMI3520M5
0459.541214.43AQ102512691WarwickRI6480M444000114.1A
N102306161WestfordMA4560M363502212.6AG102596530Sain
t AlbansWV1480M72281.250212.52BD102098337Costa
MesaCA5945M58490.461214.6BA102932537ConcordNHF5031
1.510214.70BF101069227HendersonNV4120M40261.840113BN
100841610Virginia
BchVA5720F36214.142213.4BP102455318Richmond
HillGA7520M42340.130113.6AW100584833BrickNJ5190F2625
5.260212BH102485132Fstrvl TrvosePA6160M56348.032212.3
Number of Children
NumberOfChildren 0 1 2 3 4 5 6
Frequency 1355 323 174 99 42 6 1
GraphsAgeFrequency02025010015020125293012835111402064

69. 5173502655517160240651587011875658089852490159521003
More0binFrequency002505007501009125261506417582200118
22516725018327517230019232517935017437517640013842598
4506447558500100More0BinFrequency50617080901011142124
05134521444715298162041745187More1NumberOfChildrenFre
quency01355132321743994425661NumberOfChildrenFrequency
zero1355one323two174three99four42five6six1Mail
responderFrequencyzero447one515two1038
Histogram of Age
Frequency 0 5 10 15 20 25 30 35 40 45
50 55 60 65 70 75 80 85 90 95 100
More 202 0 0 0 1 29 128 111 206
173 265 171 240 158 118 65 89 24 15 2 3
0
Age
Frequency
Distribution of WealthScore
Frequency 0 25 50 75 100 125 150 175 200
225 250 275 300 325 350 375 400 425 450 475
500 More 0 0 0 0 9 26 64 82
118 167 183 172 192 179 174 176 138 98 64 58
100 0
WealthScore
Frequency
Histogram of MedianSchoolYears
Frequency 5 6 7 8 9 10 11 12 13 14
15 16 17 18 More 0 1 0 0 0 1
42 405 452 447 298 204 45 7 1
MedianSchoolYears
Frequency
Number of Children
NumberOfChildren 0 1 2 3 4 5 6
Frequency 1355 323 174 99 42 6 1
Number of childrens
zero one two threefour five six 1355 323 174 99 42 6
1 Frequency zero one two 447 515 1038

70. Frequency zero one two 447 515 1038
t
testsGenderAgeWealthScoreNumberOfChildrenMailResponder
MedianSchoolYearsNull AgeNull wealthNull
schoolyearsF60141.120118.15030012t-Test: for Meanst-Test:
Meanst-Test: Paired Two Sample for
MeansM30246.380017.65030012M62375.330217.45030012Age
hypo. ValueWealthScoreNull wealthMedianSchoolYearsNull
schoolyearsM44333.884217.35030012Mean46.20250Mean301.8
3762300Mean13.263215974812F0336.181217.25030012Varianc
e434.80459829910Variance8902.29564095620Variance2.03052
845330M30323.682217.25030012Observations20002000Observ
ations20002000Observations19031903F62332.890217.25030012
Pearson CorrelationERROR:#DIV/0!Pearson
CorrelationERROR:#DIV/0!Pearson
CorrelationERROR:#DIV/0!M68384.210117.15030012Hypothes
ized Mean Difference0Hypothesized Mean
Difference0Hypothesized Mean
Difference0F46295.392116.95030012df1999df1999df1902M562
74.670216.85030012t Stat-8.1455965183t Stat0.8710030992t
Stat38.6716287249M36363.492216.75030012P(T<=t) one-
tail3.28687579989981E-16P(T<=t) one-
tail0.1919285241P(T<=t) one-tail3.43807047660128E-
242F28190.131016.75030012t Critical one-tail1.6456162487t
Critical one-tail1.6456162487t Critical one-
tail1.6456551606M0217.760016.65030012P(T<=t) two-
tail6.57375159979962E-16P(T<=t) two-
tail0.3838570482P(T<=t) two-tail6.87614095320257E-
242F0270.390016.65030012t Critical two-tail1.96115137t
Critical two-tail1.96115137t Critical two-
tail1.9612119661F34102.30016.65030012M26174.670016.6503
0012M52376.972216.65030012F50180.261016.55030012F4424
4.081216.55030012M48357.571216.55030012M60178.623216.5
5030012F82284.210216.55030012F38261.513016.55030012F66
234.210016.55030012M66490.460216.45030012F46310.534216
.45030012M66299.341216.45030012F54190.131016.45030012F

71. 46191.120216.35030012M64490.460216.35030012F52203.2900
16.35030012M68151.320116.35030012M38285.862116.350300
12F64274.670216.35030012F76186.840216.35030012F58229.28
0116.35030012M38369.741216.25030012F0127.960216.250300
12F38247.72116.25030012M70136.840116.25030012M88325.9
90216.25030012M58349.340216.25030012F0206.250016.15030
012F52200.660016.15030012M34178.950216.15030012M48332
.241216.15030012M46283.552116.15030012M46376.641016.15
030012M46144.080216.15030012F50245.070016.15030012M40
153.290116.15030012F0191.450016.15030012F441000116.1503
0012F44232.5700165030012F32195.7202165030012M44234.87
02165030012M46307.2412165030012F78312.1702165030012F5
4361.5101165030012M48479.9322165030012M52364.47121650
30012F48321.380215.95030012F50479.931115.95030012F0160.
20215.95030012F82261.180215.95030012F64287.51115.950300
12F30186.840215.95030012F0195.070015.95030012F38397.374
115.95030012F54158.550015.95030012M68293.420115.950300
12M46235.861115.95030012M58304.930015.95030012F24155.
260015.95030012F56239.140215.95030012F50259.870215.9503
0012M40260.860215.95030012M32256.251015.95030012M482
93.423215.95030012M36384.872215.95030012F24113.160115.
85030012M0244.410215.85030012F70285.530215.85030012F5
0215.790215.85030012M58367.430215.85030012F582750215.8
5030012F36381.254215.85030012M76335.20215.85030012F36
376.641015.85030012F30488.820215.85030012F0113.820215.8
5030012F72205.920215.85030012M64335.860215.85030012F5
2362.170215.85030012M58225.330115.85030012M62242.1102
15.85030012F48279.610215.85030012F70263.490215.75030012
M68302.30215.75030012F52440.130215.75030012M56490.460
015.75030012F0193.421015.75030012M42331.252215.7503001
2M62391.780215.75030012M38267.430015.75030012F44294.4
10215.75030012F40314.82115.75030012M74262.830215.75030
012M62490.460215.75030012F78460.530215.75030012F34267.
430215.75030012M72289.80115.75030012U0247.040015.75030
012F60280.260215.75030012M40403.950115.75030012M44431
.583115.75030012M32383.221115.75030012M48341.122215.75

72. 030012U76441.780215.65030012F34205.920215.65030012M42
150.330115.65030012M72282.890115.65030012M0196.380015.
65030012F0314.470215.65030012F56261.180015.65030012F38
300.330215.65030012M58490.461215.65030012M44293.75011
5.65030012M52251.320215.65030012M52401.641215.6503001
2M58245.390215.65030012M26323.680015.65030012M76279.6
12115.65030012M26211.511015.65030012F28180.590115.6503
0012F76376.641115.65030012F38267.764215.65030012M4438
5.22215.65030012M0222.040015.65030012F40341.122215.650
30012F66232.890115.65030012F32359.543215.65030012F0220.
070215.55030012F70207.570115.55030012M36346.381115.550
30012F32164.143015.55030012M34284.211015.55030012M621
99.340015.55030012M78405.590215.55030012F38308.220115.
55030012M76196.380215.55030012M78364.80215.55030012M
60244.742215.55030012F52231.911115.55030012F56329.61021
5.55030012F58302.630215.55030012M64224.010215.55030012
F30223.680215.55030012F0123.360115.55030012M52390.1302
15.55030012F0131.250015.45030012M34281.911215.45030012
F34346.050215.45030012M62464.470215.45030012F88159.540
015.45030012M48366.452015.45030012F48268.091015.450300
12M64362.50215.45030012M78374.340215.45030012F0421.71
2215.45030012F40468.422215.45030012F62296.050115.450300
12F0216.780015.45030012M66141.120215.45030012M30168.0
90015.45030012M38287.170015.45030012M48328.622215.450
30012F62344.740215.45030012F0196.380015.45030012F44213.
820015.35030012F34188.160115.35030012F62244.740115.3503
0012F46300.331215.35030012M42266.120215.35030012F6033
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