# Need help-STAT2112.13

Need help-STAT2112.13
Quiz #
8
(last)
NAME:____________________GWID:G_________________
Fall 201
6
sba
(Take Home)
Due
on Tuesday office Hour (5pm)
Rome
Hall
Questions 1
3
are based on the following
quarterly
data collected on
the
average nights
foreign
tourists
spent in
Washington DC
area
from 2011
2016 (
quarterly
data)
.
Time
Average Stay (
nights
)
Mar
11
1
41.7
Jun
11
2
24
Sep
11
3
32.3
Dec
11
4
37.3
Mar
12
5
46.2
Jun
12
6
29.3
Sep
12
7
36.5
Dec
12
8
43
Mar
13
9
48.9
Jun
13
10
31.2
Sep
13
11
37.7
Dec
13
12
40.4
Mar
14
13
51.2
Jun
14
14
31.9
Sep
14
15
41
Dec
14
16
43.8
Mar
15
17
55.6
Jun
15
18
33.9
Sep
15
19
42.1
Dec
15
20
45.6
Mar
1
6
21
59.8
Jun
1
6
22
35.2
Sep
1
6
23
44.3
Dec
1
6
24
47.9
1.
Use
Exponential
Smoothing
with w=0.6
to
predict average
stay (
nights
)
by
foreign
tourists
during
four (4) quarters of
201
7
.
2.
Assuming there is a trend in the da
ta, use
appropriate
s
moothing
technique
with
coefficients
w=0.6 and ν=0.2
,
to
predict the
average
stay (
nights
)
by
foreign
tourists
during
four (4) quarters
of
201
7
.
3.
Which of the above two models do you prefer?
W
hy
?
th
is question.
4
.
Which one the
assumption
(if any) is/
are required for using
Kruskal
Wallis
test?
I
. We assume that the samples drawn from the population are random.
II
. We also assume tha
t the cases of each group are independent.
III
. The measurement scale for should be at least ordinal.
A. I, II but not III
B. I, I
I
I but not II
C. I, II and III
D.
Kruskal
Wallis
is a distribution free statistics and
therefore
no assumption is requir
ed.
Questions
5
6
are based on the following data
.
S
uppose weights of
an
exotic
plant (lbs) a
re
different based on treatments (no
treatment, fertilizer, irrigation, or fertilizer and irrigation). Each
weight samples that determined by the treatments is independent and random
.
W
e
ight samples
are not normally distributed.
NO
Fert
Irrig
F
&I
0.15
1.34
0.23
2.03
0.02
0.14
0.04
0.27
0.16
0.02
0.34
0.92
0.37
0.08
0.16
1.07
0.22
0.08
0.05
2.38
0.
0
2
2.38
5
. T
est whether the
weights
of plants
are different under the
treatments.
6. What is your conclusion and why
.
7
8
. Six
restaurant
food
critics
were randomly assigned to
all
four
restaurant
s (A, B,
C, and D)
and
o
n the scale of 0
100 (100 being the best)
Rater A B C D
1
70
61
82
74
2
77
75
88
76
3
76
67
90
80
4
80
63
96
76
5
84
66
92
84
6
78
68
98
86
Are
there any differences
among
the
restaurant
conclusion
with
objective
facts
/statistics
.
9
. Which of the following nonparametric tests can be used for a paired difference experiment?
a. The Wilcoxon Signed Ranks test.
b. The Sign test.
c.
The
Kruskal
Wallis test
d
. Spearman’s Rank Correlation test
10. The following table provides
M
ath and
English
scores
on 10
stu
d
ents
.
The relationship may
not be linear. Use
appropriate
statistics
to investigate the possible
ass
ociation
between these
scores
Exam
Scores
English
56
75
45
71
61
64
58
80
76
61
Maths
66
70
40
60
65
56
59
77
67
63

# Need help-Statistics Assignment:Geochemistry 1

Need help-Statistics Assignment:Geochemistry 1

Field and Laboratory Techniques in Geochemistry 1

Statistics Assignment

Question 1)  10%

The data for a digestion of Bolivian tailings are provided. The elements are grouped as majors, traces and rare earth elements. Produce, using the descriptive statistics command in Excel (or any other suitable programme), summary data for each of these three groupings (Mean, Standard Error, Median, Mode, Standard Deviation, Sample Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum and Count).

The data should be presented in tabulated form.

Question 2)  10%

Explain, using a maximum of three sentences for each, what you understand by the terms: Mean, Standard Error, Median, Mode, Standard Deviation, Sample Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum and Count.

Question 3)  10%

Calculate the precision for each element analysed. (Hint the copy and paste tool is very useful for formulae).

Question 4)   10%

The data for a digestion of Bolivian uncontaminated soil are provided. The elements are grouped as majors, traces and rare earth elements. Calculate the mean, standard deviation, standard error and median of the samples for each of these three categories. Comment on any elements which show a large median/mean difference (hint Bi might be worth comparing in this context; for example against a major element). Your answer should incorporate the word ‘outlier’.

Question 5)   10%

BCR-1 and JB-3 are soil CRM materials. They were digested at the same time and using exactly the same methodology as the samples themselves. Calculate the accuracy of the digestion by comparing the results with the given elemental concentrations of the reference materials (BCR-1 rv and JB-3 rv). Comment on the accuracy of the analysis.

Question 6)  25%

The data for chloride concentrations of Regent’s canal and Pennine stream water, as determined by Ion Chromatography (IC), are presented. The data to be analysed are those collected from the Regent’s canal (RC in the spreadsheet itself). To the right of the spreadsheet these data has been extracted to help you answer the following questions.

Question 6a) the samples were analysed at two dilutions: a hundred fold (*100) and neat (*1). Why do you think that such a difference was reported in concentration? Which of these ‘dilutions’ do you trust?

Question 6b) construct a calibration curve (hint, scatter graph). The calibration standards employed were made up to 10, 20, 40 and 60 mg L-1. Plot a suitable regression line and display the R2 value on the graph, from this calculate the Pearson correlation coefficient (hint this is a one-step transformation)

Question 6c) do you think that drift correction might be necessary? Plot a suitable scatter graph to illustrate your answer. Note there is not a definitive yes or no answer to this question. You will be awarded marks on the strength of your reasoning.

Question 6d), using the blank data determine the LOD and LOQ for the complete analytical run. Describe, in a maximum of four sentences, what is meant by the terms LOD and LOQ.

Question 6e) Determine the precision* and accuracy (hint, consider the CRM dilution factor) of the Regent’s canal data. The concentration of chloride in the Battle reference standard is as follows:

*Note there are two duplicate pairs: 1 and 1a together with 2 and 2a. Calculate the individual precisions and the combined overall precision by any appropriate method.

Question 7)  25%

The data provided are from a column experiment which investigated the evolution of pore water concentrations over a modelled twenty year period. The column was packed with uncontaminated Bolivian soil together with sulphide mine tailings.

Question 7a) Produce a correlation matrix encompassing all of the elements (hint spreadsheet 30 gives a suitable method and also remember to remove all non-numerical data).

Question 7b) Produce three scatter graphs from the data. The first should show a strong positive correlation, the second a negative correlation and the third show minimal correlation. For each of these graphs plot a regression line, produce a linear equation and a R2 value. From the latter obtain the value of r (Pearson’s correlation).

7c) Calculate a Spearman correlation coefficient for the Zn and Cd concentrations (hint, follow the ranking formulae given in spreadsheet 11).

When comparing the Pearson and Spearman correlation coefficient, which of the two is more sensitive to outliers? Looking at the formulae can you suggest a reason for your conclusion?

Pearson

Spearman

7d) Give an example, not necessarily from the scientific literature, of correlation not implying causation (hint, Wikipedia has a good page addressing this specific question).

Need help-Statistics Assignment:Geochemistry 1

# Purchase Assignment-Quantitative Methods for Decision Making

Purchase Assignment-Quantitative Methods for Decision Making

Department of Management and Marketing

Quantitative Methods for Decision Making

Project 1

Word report (the hand writing reports will not be accepted) that includes the following three parts:

Question 1:

An investment company has classified its clients according to their gender and the composition of their investment portfolio (bonds, stocks, or a diversified mix of bonds and stocks). The proportions of clients falling into the various categories are shown in the following table:

 Gender Portfolio composition B (Bonds) S (Stocks) D (Diversified) M (Male) 0.18 0.20 0.25 F (Female) 0.12 0.10 0.15

1. What is the probability that a randomly selected client is male and has a diversified portfolio?
2. Find the probability that a randomly selected client is male, given that the client has a diversified portfolio?
3. Find the probability that a randomly selected client is Female, given that the client has a portfolio is composed of Bonds?
4. Let us define the following two events:

E1: the investor is a male

E2: the portfolio is composed of Stocks.

Can we conclude that the events E1 and E2 are independent?

Question 2:

In a hotel chain, the average number of rooms rented daily during each month is 50 rooms. The population of rooms rented daily is assumed to be normally distributed with a standard deviation of 4 rooms. For a specific month of the year, what is the probability that the number of rented rooms, in that month, is between 45 and 60 rooms?

Question 3:

The University registrar office has got some data regarding a sample of 200 students among those enrolled in the MBA Program. The registrar notes that 50 students of the sample are holders of a bachelor degree in business administration. What is the probability that among this sample 20 students hold a bachelor in business administration?

Purchase Assignment-Quantitative Methods for Decision Making

# Need Help: MATH238 COURSEWORK (Statistics)

MATH238 COURSEWORK (Statistics)

# Econ 4400, Elementary Econometrics

HOMEWORK 6

Econ   4400,   Elementary Econometrics

Directions: Please follow the instructions closely. Completion of this assignment re- quires the data set gpa2.dta available on Carmen. Due: All problem sets have to be turned in at the beginning of the  class.

Due Dates:

April 21, 2016

1.(20 points)Estimate the following equations and report the results in a table: (a)GPA as a function of SAT score, total hours, athlete, high school  percentile

rank, sex, race, high school size, and school size squared

(b)Add an interaction term between SAT and athlete to regression (a) (c)Add high school rank to regression (b)

(d)Estimate High school rank as a function of all of the regressors in (b)

2.(15 points)Which model do you prefer and why?(There is not necessarily a correct answer. As long as you use the criteria that we discussed in class correctly, you will get credit.)

3.(15 points)Interpret the coefficient on the interaction term between athlete and SAT.

4.(25 points)Calculate a variance inflation factor for high school rank.  What does   a high VIF indicate? Is the estimate cause for concern? (pages 259-260 in text) Report the necessary regression results.

5.(25 points)Use a new regression and a t-test to test whether sat scores differently predict gpa for men and women. Report regression results in a  table.

Table 1:  Regression Results

 (1) GPA (2) GPA (3) GPA (4) high school rank (5) GPA combined SAT score 0.00153∗∗∗ (0.0000679) 0.00158∗∗∗ (0.0000695) 0.00156∗∗∗ (0.0000695) -0.0150∗∗∗ (0.00391) 0.00158∗∗∗ (0.0000881) total hours 0.00175∗∗∗ (0.000243) 0.00174∗∗∗ (0.000242) 0.00171∗∗∗ (0.000242) -0.0231∗ (0.0136) 0.00174∗∗∗ (0.000242) =1 if athlete 0.210∗∗∗ (0.0423) 0.909∗∗∗ (0.225) 0.946∗∗∗ (0.225) 28.46∗∗ (12.67) 0.912∗∗∗ (0.227) high school percentile -0.0135∗∗∗ (0.000574) -0.0136∗∗∗ (0.000575) -0.0104∗∗∗ (0.000879) 2.413∗∗∗ (0.0323) -0.0136∗∗∗ (0.000576) =1 if female 0.148∗∗∗ 0.150∗∗∗ 0.148∗∗∗ -1.991∗∗ 0.162 (0.0178) (0.0178) (0.0177) (0.999) (0.133) =1 if white -0.0388 -0.0319 -0.0248 5.363 -0.0318 (0.0623) (0.0622) (0.0621) (3.500) (0.0623) =1 if black -0.349∗∗∗ (0.0720) -0.359∗∗∗ (0.0720) -0.342∗∗∗ (0.0719) 12.47∗∗∗ (4.048) -0.358∗∗∗ (0.0720) size grad. class, 100s -0.0548∗∗∗ -0.0564∗∗∗ -0.0252 23.79∗∗∗ -0.0564∗∗∗ (0.0161) (0.0161) (0.0174) (0.907) (0.0161) hsize2 0.00430∗ 0.00452∗∗ 0.00448∗∗ -0.0337 0.00452∗∗ (0.00222) (0.00222) (0.00221) (0.125) (0.00222) asat -0.000753∗∗∗ (0.000238) -0.000785∗∗∗ (0.000238) -0.0247∗ (0.0134) -0.000755∗∗∗ (0.000240) rank in grad.  class -0.00131∗∗∗ (0.000276) femsat -0.0000114 (0.000128) Constant 1.331∗∗∗ (0.102) 1.278∗∗∗ (0.103) 1.215∗∗∗ (0.103) -48.14∗∗∗ (5.786) 1.273∗∗∗ (0.119) Observations 4137 4137 4137 4137 4137

R2                                          0.312               0.314                0.318                   0.775                   0.314

Adjusted R2                           0.311               0.312                0.316                   0.775                   0.312

Standard errors in parentheses

p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01