1)In the this case, respect is characterized as a company's readiness to consider the commitments
and needs of their clients and making approaches and policies that are a direct consequence of client
Lee Mariya
Finance
PROBLEM SET 1
LECTURE 2
Problem 1
Explain the difference between a simple random sample and a random sample. Name
two kinds of non-simple random samples.
In order to collect a rando
Problem 1
The main difference is connected with the population issue. In random sample each unit of population has specified and n
While a simple random sample is a special case where each unit of pop
12 Problem
a) . The customer responses provided categorical data for six variables.
b) There was used a nominal measurement scale. Because subjects were based only on their
names.
17 Problem
a) The po
1.
a)
Softwar
e
Excel
Minitab
SAS
SPSS
Other
Total
Frequenc
y
distributio
n
34
17
3
4
12
70
%
49%
24%
4%
6%
17%
100%
b)
Table 1: Frequency distribution
Excel; 34
Minitab; 17
SAS; 3
Other; 12 SPSS; 4
T
1.
A nominal scale of measurement deals with variables that are non-numeric or where the numbers have no
value. In other words, we can put them in any order and it wouldn't matter. Whether an ordinal
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A Guide to
Basic Econome
Statistics and Applied
Econometrics
Galym Makhmejanov
KAZGUU Fall 2016
Lecture 2. Nature of Statistics
CONTENT
1.
2.
3.
4.
5.
Terminology
Population
Bias
Parameters
Sampling
Aims
A general introducti
Statistics and Applied
Econometrics
Galym Makhmejanov
KAZGUU Fall 2016
Lecture 5. Normal Distribution
CONTENT
5.
5.1.
5.2.
5.3.
The normal distribution and ideas of sampling
The random variable
Popula
Statistics and Applied
Econometrics
Galym Makhmejanov
KAZGUU Fall Semester 2016
Chapter 1
THE NATURE OF ECONOMETRICS
Econometric Methodology
Keynes Law of Consumption:
The fundamental psychological
KAZGUU Fall Semester 2016
Statistics and Applied Econometrics
Lecturer: Galym Makhmejanov
PROBLEM SET 2
LECTURE 5
Problem 1
S
(0,1).
, 1,1
1,9, , (1,1) = 0,864; (1,9) = 0,971.
Problem 2
S ,
.
Statistics and Applied
Econometrics
Galym Makhmejanov
KAZGUU Fall 2016
Lecture 4. Probability
CONTENT
4.
Probability
4.1.
4.2.
4.3.
4.4.
The concept of probability
Relative frequency
`Randomness'
Prop
23.
a.
(
P ( x<60 )=P z<
6080
=P ( z<2 )=0.02275
10
)
b.
P (60< x<75 )=P
(
6080 7580
<z<
= P (2< x<0.5 ) =0.2857
10
10
)
c.
(
P ( x>90 )=P z>
9080
= P ( z>1 )=0.1587
10
)
Number of students who will b
3) Number of permutations is
nPr = n!/(n-r)!
6!/(6-3)!=6*5*4*3!/3!=6*5*4=120.
Permutations out of B,D,F:
BDF,BFD,DBF,DFB,FBD,FDB
37)
a.0.37*0.19=0.0703
b.0.37*0.81=0.2997
c.abeinghighercorrespondstoth
1)
0.7
0.6
f(x) = - 0x + 0.6
R = 0.99
0.5
0.4
Average weight/Fruit
(pounds)
Linear (Average
weight/Fruit (pounds)
0.3
0.2
0.1
0
0
200
400
600
800
1000
As you can see from the scatter graph coefficient
Statistics and Applied
Econometrics
Galym Makhmejanov
KAZGUU Fall 2016
Content
1. Data presentation (Ways of summarizing data; measures of
location and dispersion; graphical presentation).
2. Probabil