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WorksheetsAdvanced Statistics
Total questions: 140
Worksheet time: 1hrs 10mins
Analysis of labor turnover rates, performance appraisal, training programs and planning of incentives are examples of role of
statistics in personnel management
statistics in marketing
statistics in finance
statistics in production
Focus groups, individual respondents and panels of respondents are classified as
pointed data sources
secondary data sources
itemized data sources
primary data sources
Variables whose measurement is done in terms such as weight, height and length are classified as
continuous variables
flowchart variables
measuring variables
discrete variables
Technique used to analyze unemployment rate, inflation rate anticipation and capacity utilization to manufacture goods is classified as
data supplying technique
data importing technique
forecasting technique
data exporting technique
Numerical methods and graphical methods are specialized procedures used in
social statistics
descriptive statistics
business statistics
education statistics
Measure of how well is a technique, concept or process is considered as
continuity of variables
validity
goodness of variables
reliability
Branch of statistics which considers ratio scale and interval scale is considered as
parametric statistics
distribution statistics
non-parametric statistics
sampling statistics
Reports on quality control, production and financial accounts issued by companies are considered as
external secondary data sources
external primary data sources
internal secondary data sources
internal primary data sources
In every phenomenon, process of thought that focus on identifying, controlling and reduction of variations in data is classified as
parallel thinking
statistical thinking
serial thinking
managerial thinking
Scale which categorize events in collectively exhaustive manner and mutually exclusive manner is classified as
discrete scale
valid scale
continuous scale
nominal scale
Type of rating scale which allows respondents to choose most relevant option out of other stated options is classified as
marking rating scale
itemized rating scale
graphical rating scale
pointed rating scale
Government and non-government publications are considered as
external secondary data sources
external primary data sources
internal secondary data sources
internal primary data sources
Type of variable which can take fixed integer values is classified as
flowchart variable
continuous variable
discrete variable
measuring variables
Data which is generated within company such as routine business activities is classified as
external primary data sources
external secondary data sources
internal primary data sources
internal secondary data sources
Question which have different answers for its subparts is considered as
double barreled questions
multiple barreled questions
single barreled questions
dichotomous questions
Analytical study of relationship between output commodity and its price is classified as
demand analysis
imports analysis
supply analysis
export analysis
Process of converting inputs into outputs in presence of repeatedly same conditions is classified as
Sampler
Process
parameters
mixer
Branch of statistics which deals with development of particular statistical methods is classified as
industry statistics
applied statistics
economic statistics
mathematical statistics
Type of variable which can take any numerical figure for calculation is classified as
continuous variable
flowchart variable
discrete variable
measuring variable
Tools such decision making by nominal groups, brain storming and term buildings are all considered as
serial tools
statistical tools
behavioral tools
parallel tools
One of category of statistical method is
managerial statistics
inferential statistics
decision science
industry statistics
Branch of statistics in which data is collected according to ordinal scale or nominal scale is classified as
distribution statistics
parametric statistics
non-parametric statistics
sampling statistics
Time frame to complete a transaction in bank is classified as
Parameters
Mixer
process
sampler
Type of rating scale which represents response of respondents by marking at appropriate point is classified as
graphic rating scale
pointed scale
responsive scale
marking scale
Scale which is used to determine ratios equality is considered as
satisfactory scale
goodness scale
ratio scale
exponential scale
Measurement scale which allows researchers and statisticians to perform certain operations on data collected from respondents is classified as
interval scale
validity scale
reliability scale
flow measuring scale
Type of questions included in questionnaire to record responses in which respondent can answer in any way are classified as
multiple choices
open ended questions
itemized question
close ended questions
Numerical or descriptive measure which is associated with variable to describe entire population of statistical phenomenon is classified as
Mixer
Parameter
process
sampler
Scale used in statistics which provides difference of proportions as well as magnitude of differences is considered as
satisfactory scale
goodness scale
ratio scale
exponential scale
Measurement scale in which values are categorized to represent qualitative differences and ranked in meaningful manner is classified as
valid scale
ordinal scale
discrete scale
continuous scale
Measurement of how well particular concept and technique measures variables is classified as
Reliability
continuity of variables
validity
goodness of variables
Data measurement which arises from a specific process of counting is classified as
continuous data
reliable data
valid data
discrete data
Characteristics that are intended to be analyzed and investigated for a given population are classified as
Exponents
Variables
constants
exponential base
If vertical lines are drawn at every point of straight line in frequency polygon then by this way frequency polygon is transformed into
width diagram
histogram
length diagram
dimensional bar charts
Discrete variables and continuous variables are two types of
open end classification
qualitative classification
time series classification
quantitative classification
In stem and leaf display diagrams used in exploratory analysis, stems are considered as
central digits
leading digits
trailing digits
dispersed digits
Classification method in which upper limit of interval is same as of lower limit class interval is called
exclusive method
mid-point method
inclusive method
ratio method
Type of cumulative frequency distribution in which class intervals are added in top to bottom order is classified as
variation distribution
more than type distribution
less than type distribution
marginal distribution
Data based on workers salary is as 2500, 2700, 2600, 2800, 2200, 2100, 2000, 2900, 3000, 2800, 2200, 2500, 2700, 2800, 2600 and number of classes desired is 10 then width of class interval is
400
100
300
200
Largest value is 60 and smallest value is 40 and number of classes desired is 5 then class interval is
20
25
4
15
Summary and presentation of data in tabular form with several non-overlapping classes is referred as
nominal distribution
Chronological distribution
ordinal distribution
frequency distribution.
Type of cumulative frequency distribution in which class intervals are added in bottom to top order is classified as
more than type distribution
variation distribution
marginal distribution
less than type distribution
Distribution which shows cumulative figure of all observations placed below upper limit of classes in distribution is considered as
cumulative frequency distribution
class distribution
upper limit distribution
cumulative class distribution
Frequency distribution which is result of cross classification is called
bivariate frequency distribution
univariate frequency distribution
multi-variables distribution
close ended distribution
Halfway point between lower class limits and upper class limits is classified as
nominal mid-point
interval mid-point
class mid-point
ordinal mid-point
Largest numerical value is 85 and smallest numerical value is 65 and classes desired are 8 then width of class interval is
18.75
13.75
14.75
2.5
Stem and leaf displaying technique is used to present data in
descriptive data analysis
nominal data analysis
exploratory data analysis
ordinal data analysis
If midpoints of bars on charts are marked and marked dots are joined by a straight line then this graph is classified as
class interval polygon
marked polygon
paired polygon
frequency polygon
Graphical diagram in which total number of observations are represented in percentages rather than absolute values is classified as
asymmetrical diagram
grouped diagram
ungrouped diagram
pie diagram
Number of observations are represented in percentages rather than absolute values is classified as
asymmetrical diagram
grouped diagram
ungrouped diagram
pie diagram
Median, mode, deciles and percentiles are all considered as measures of
mathematical averages
sample averages
population averages
averages of position
In two units of company, employees in unit one are 650 and monthly salary is $2750, employees in unit two are 700 and monthly salary is $2500 then combined arithmetic mean is
$2,620
$2,420
$2,520
$2,320
If most repeated observations recorded are outliers of data then mode is considered as
intended measure
best measure
percentage measure
poor measure
Number of observations are 30 and value of arithmetic mean is 15 then sum of all values is
15
200
450
45
Value of Σfx is 180, A= 22, and width of class interval is 5, arithmetic mean is 120 then observations are
59
39.5
30
49.5
Value of Σfx is 300, A= 35, number of observations are 15 and width of class interval is 5 then arithmetic mean is
135
150
145
235
Quartiles, median, percentiles and deciles are measures of central tendency classified as
paired average
positioned averages
deviation averages
central averages
Considering probability distribution, if mode is greater than median then distribution is classified as
variable model
left skewed
right skewed
constant model
Frequency distribution whose most values are dispersed to left or right of mode is classified as
Skewed
Bimodal
explored
unimodal
If arithmetic mean is 25 and harmonic mean is 15 then geometric mean is
17.36
15.36
16.36
19.36
If central tendency is found by using whole population as input data then this is classified as
sample statistic
population tendency
population statistic
population parameters
Criteria of inferential statistics which considers sum of squared deviations is classified as.
central squares criterion
multiple squares criterion
outliers square criterion
least squares criterion
In a negative skewed distribution, order of mean, median and mode is as
mean
mean>median>mode
mean
mean>median
Measure which describes detailed characteristic of whole data set is classified as
average or central value
negative skewed value
positive skewed value
positive extended value
In arithmetic mean, sum of deviations of all recorded observations must always be
Two
One
minus one
zero
Distribution whose outliers are higher values is considered as
variable model
left skewed
right skewed
constant model
In quartiles, central tendency median to be measured must lie in
first quartile
second quartile
third quartile
four quartile
Arithmetic mean is 12 and number of observations are 20 then sum of all values is
8
240
32
1.667
At a grocery store, number of per day sold processed fruits cans in 15 days are 50, 70, 60, 40, 30, 20, 5, 150, 55, 75, 65, 45, 35, 25, 52 then outliers in observations are
50, 150
25, 70
5, 150
150
Measure of central tendency which represents over time multiplicative effects for inflation and compound interest is considered a
deviation square mean
geometric mean
paired mean
harmonic mean
Around central value of observations, extent to which values depart from normal distribution is classified as
negative variation
skewness
positive variation
positive trailing
Product W has per unit contribution of 8 with sold quantity of 124 units, product X has per unit contribution of 5 with sold quantity of 105 units, product Y has per unit contribution of 9 with sold quantity of 135 units, product Z has per unit contribution of 12 with sold quantity of 140 units then weighted average mean is
$11.75
$9.75
$10.75
$8.75
In measure of central tendency, population parameter is denoted by
Greek letter μ
roman letter μ
Athens letter μ
roman letter x ̅
Type of central tendency measures which divides data set into 100 equal parts is classified as
Quartiles
deciles
Percentiles
multiple pile of data
If value of three measures of central tendencies median, mean and mode then distribution is considered as
negatively skewed modal
unimodal
triangular model
bimodal
If central tendency is found by using sample data from population then this is classified as
tendency statistic
average statistic
sample statistic
population statistic
Product A has per unit contribution of 6 with sold quantity of 120 units, product B has per unit contribution of 8 with sold quantity of 100 units and product C has per unit contribution of 10 with sold quantity of 130 units then weighted average mean is
$7.06
$9.06
$8.06
$10.06
If value of mode is 14 and value of arithmetic mean is 5 then value of median is
12
8
18
14
Concept used in calculation of index numbers and where smaller observations must be taken into consideration is called
deviation square mean
geometric mean
paired mean
harmonic mean
Distribution which has outliers with relatively lower values is considered as
experimentally skewed
exploratory skewed
positively skewed
negatively skewed
Calculation of average which is calculated by pooling data together from different data sets is classified as
geometric mean
harmonic mean
deviation square mean
paired mean
If arithmetic mean is 20 and harmonic mean is 30 then geometric mean is
14.94
24.94
34.94
44.94
If quartile range is 24 then quartile deviation is
48
24
12
72
If mean absolute deviation of set of observations is 8.5 then value of quartile deviation is
7.08
10.2
9.08
11.2
Sum of all squared deviations is divided by total number of observations to calculate
population deviation
sample deviation
population variance
sample variance
For recorded observation, ratios measured by absolute variation are considered as
non-relative measures
high uniform measures
relative measures
low uniform measures
If arithmetic mean is multiplied to coefficient of variation then resulting value is classified as
coefficient of deviation
standard deviation
coefficient of mean
variance
If arithmetic mean is considered as average of deviations then resultant measure is considered as
close end deviation
mean absolute deviation
mean deviation
variance deviation
If positive square root is taken of population variance then calculated measure is transformed into
standard root
standard deviation
standard variance
sample variance
Formula of coefficient of range is
L+L⁄H+H
H+H⁄L+L
H-L⁄H+L
H+L⁄H-L
In a set of observations, amount of variation can be shown in form of figures with help of
absolute measures
non-uniform measures
uniform measures
exploratory measures
If value of first quartile is 49 and value of third quartile is 60 then value of inter quartile range is
21
11
31
41
If total sum of square is 20 and sample variance is 5 then total number of observations are
15
4
25
35
coefficient of range is 0.077 then sum of highest and lowest value is
210
260
220
240
In manufacturing company, number of employees in unit A is 40, mean is USD $6400 and number of employees in unit B is 30 with mean of Rs. 5500 then combined arithmetic mean is
9500
7014.29
8000
6014.2
If quartile deviation of given set of data of 20 observations is 12 then value of standard deviation is
1.667
8
18
32
High uniformity of 50% observations around median value is indicated with help of
larger value of quartile deviation
larger value of range deviation
smaller value of quartile deviation
smaller value of range deviation
Relative measures in measures of dispersion are also considered as
coefficient of deviation
coefficient of variation
coefficient of average
coefficient of uniformity
Standard deviation is divided by coefficient of variation to calculate
arithmetic mean
coefficient of variance
coefficient of arithmetic
multiplier of deviation
If standard deviation is 7 then mean absolute deviation is
9.75
7
5.6
8.75
According to empirical rule, standard deviation and mean interval that covers approximately 99.75% of data from a frequency distribution is
4μ±4σ
μ±3σ
3μ±3σ
2μ±2σ
Theorem which states least percentage of values that fall within z-standard deviations is classified as
Chebyshev's Theorem
Pearson Theorem
sampling theorem
population theorem
Categories of measures of dispersion are classified as
uniform measures
absolute measures
relative measures
both b and c
Population variance is also called.
sigma squared
negative sigma
square root
cubic root
Lesser uniformity of 50% observations around median value is indicated with help of
larger value of range deviation
larger value of quartile deviation
smaller value of range deviation
smaller value of quartile deviation
If large number of values lies in central part of data table then spread of values is measured by
percentile range
quartile range
inter quartile range
deciles range
Formula which considers relationship between set of observations, standard deviation and mean is classified as
empirical value
normal rule
three way rule
both a and b
If calculated value of total sum of squares in sample variance is larger than variation in data set is considered as
Smaller
Zero
greater
negative
Standard deviation of data is 12 and mean is 72 then coefficient of variation is
14.67%
12.67%
16.67%
13.67%
Value of third quartile is 61 and inter quartile range of set of observation is 48 then value of first quartile is
24
64
34
13
If in a formula, mean absolute deviation is numerator and arithmetic mean is denominator then resultant value is classified as
coefficient of mean deviation
coefficient of quartile range deviation
coefficient of absolute quartile deviation
coefficient of mean absolute deviation
Technique which implies in statistical process to measure variation in data is called
measures of dispersion
measures of statistics
measures of process
none of above
If standard deviation is 5 then quartile deviation is
5
0.234
0.334
0.134
Value of third quartile is 72, second quartile is 52 and first quartile is 45 then quartile deviation is
13.5
16.5
14
18.5
Undesirable consequences which causes estimated population variance to appear less as compared to real results are classified as
undesired error
non-calculate error
bias
non-zero error
Measuring theorem which helps in determining proportion of observations for specific interval of mean and standard deviation is classified as
Pearson Theorem
sampling theorem
Chebyshev's Theorem
population theorem
Considering individual values of data set, actual mean must always be
1
0
−1
2
Considering set of observations, percentage of values that lies within population mean plus two standard deviations is
60%
75%
55%
85%
Measure of variation which is useful for highly skewed distribution is
inter quartile deviation
inter quartile range
quartile deviation
quartile range
Sum of highest and lowest value is 80 and coefficient of range is 0.625 then difference between highest and lowest value is
70
150
100
50
Formula written as quartile deviation divided by sum of third and first quartile is used to calculate
coefficient of quartile deviation
coefficient of quartiles
coefficient of inter quartiles
coefficient of central tendency
Standard deviation of first 50 natural numbers is
45.43
20.43
14.43
16.43
Standard deviation of population is denoted by
Ω
σ
ω
Σ
Output of 20 workers in hand made pot painting store is as 55, 65, 62, 60, 74, 75, 65, 70, 70, 72, 67, 78, 79, 80, 68, 54, 56, 63, 69, 71 then coefficient of range is
0.29
0.49
0.19
0.39
If arithmetic mean is 78 and coefficient of variation is 12.3% then standard deviation is
10.594
9.594
8.59
11
Value of first quartile is 23 and inter quartile range is 20 then value of third quartile is
63
43
53
73
Sum of squared deviation of sample mean is 48 and total number of observation is 13 then population variance is
61
13
48
4
Sum of observations is 12 and coefficient of absolute mean deviation is 18 then value of mean absolute deviation is
516
216
716
616
Shape of frequency distribution constructed in consideration of empirical rule is classified as
bell shaped
wing shape
tower shape
fish shape
According to empirical rule, mean and standard deviation interval that covers approximately 95.45% of data from a frequency distribution is
μ±σ
3μ±2σ
2μ±2σ
μ±2σ
Output of 15 workers in hand made leather shoes company is as 50, 65, 70, 55, 62, 74, 75, 65, 70, 78, 79, 80, 68, 72, 67 then range is
30
75
80
79
If scatter or dispersion in distribution is high on each side then this indicates
outliers of data
high uniformity of data
low uniformity of data
dispersion of data
Technique used in measures of variations to show direction of variation in set of observations is classified as
measures of dispersion
measures of statistics
measures of skewness
measures of process
Total revenue (in crores) of five leather goods companies are as two companies have revenues between 10-20, one company has revenue between 20-30 and one company has revenue between 30-40 then standard deviation is
7.9
5.9
4.9
6.9
In classes of grouped data such as 10-15, 16-20, 21-25, 26-30 with respective frequencies of each class as 3, 5, 4, 3 then range is
5
15
6
20
Measures which considers mean or median to calculate average deviation does not includes
mean absolute deviation
variance
standard deviation
median deviation
Variability measuring tool in which standard deviation is divided by arithmetic mean and multiplied by 100 is classified as
coefficient of variation
coefficient of standard deviation
coefficient of deviation
coefficient of mean
Considering set of values, percentage of values that lies within three standard deviation of population plus population mean is
88.90%
68.90%
78.90%
98.90%
For a given set of number of customers who visit a shoes shop in 5 consecutive days, mean absolute deviation is 10 then standard deviation of data set is
2
12.5
10
50
Inter quartile range and coefficients of range are two categories of
average deviation measures
average measures
availability measures
distance measures
