In classification the data are arranged according to Ratios Differences Percentages Similarities

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This quiz contains MCQs about Basic Statistics with answers covering variable and type of variable, Measures of central tendency such as mean, median, mode, Weighted mean, data and type of data, sources of data, Measures of Dispersion/ Variation, Standard Deviation, Variance, Range, etc. Let us start the MCQs Basic Statistics Quiz.

In classification the data are arranged according to Ratios Differences Percentages Similarities

Basic statistics deals with the measure of central tendencies (such as mean, median, mode, weighted mean, geometric mean, and Harmonic mean) and measure of dispersion (such as range, standard deviation, and variances).

Basic statistical methods include planning and designing the study, collecting data, arranging, and numerical and graphically summarizing the collected data.

Basic statistics also used to perform statistical analysis to draw meaningful inferences.

In classification the data are arranged according to Ratios Differences Percentages Similarities

A basic visual inspection of data using some graphical and numerical statistics may give some useful hidden information already available in the data. The graphical representation includes a bar chart, pie chart, dot chart, box plot, etc.

Companies related to finance, communication, manufacturing, charity organizations, government institutes, simple to large businesses, etc. are all examples that have a massive interest in collecting data and measuring different sorts of statistical findings. This helps them to learn from the past, noticing the trends, and planning for the future.

Concept:

  • Frequency of any class = cumulative frequency of class - cumulative frequency of preceding class
  • Cumulative frequency is the sum of all the previous frequencies up to the current point.

Calculation:

We are already having the cumulative frequencies. Let's find out the frequencies:

Salary (in Rs.) Cumulative Frequency Frequency
0 - 10,000 51 51 - 48 = 3
10,000 - 20,000 48 48 - 37 = 11
20,000 - 30,000 37 37 - 25 = 12
30,000 - 40,000 25 25 - 19 = 6
40,000 - 50,000 19 19 - 9 = 10
More than or equal to 50,000 9 9

Hence, the frequency of class intervals 30,000 - 40,000 is 6.

  1. 33 - 35
  2. 36 - 38
  3. 39 - 41
  4. 42 - 44

Concept:

Cumulative frequency is the sum of all the previous frequencies up to the current point.

The cumulative frequency is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors. The last value will always be equal to the total for all observations since all frequencies will already have been added to the previous total.

Also, Frequency of any class = cumulative frequency of class - cumulative frequency of preceding class

Calculation:

Let's calculate the frequency of each class using the cumulative frequencies of each class:

Class Cumulative Frequency Frequency
30 - 32 5 5
33 - 35 12 7
36 - 38 20 8
39 - 41 33 13
42 - 44 42 9

Clearly, we can see that 39 - 41 has the maximum frequency i.e.,13.

Hence, 39 - 41 has the maximum frequency.

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In classification the data are arranged according to Ratios Differences Percentages Similarities

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  1. Ogive
  2. Frequency polygon
  3. Pie diagram
  4. Frequency curve

A graph of a cumulative frequency distribution is called Ogive.

In classification the data are arranged according to Ratios Differences Percentages Similarities

Ogive: In statistics, an ogive, also known as a cumulative frequency polygon, can refer to one of two things:

  • any hand-drawn graphic of a cumulative distribution function
  • any empirical cumulative distribution function.

The points plotted as part of an ogive are the upper-class limit and the corresponding cumulative absolute frequency or cumulative relative frequency. The ogive for the normal distribution resembles one side of an Arabesque or ogival arch, which is likely the origin of its name.

In classification the data are arranged according to Ratios Differences Percentages Similarities

1. Frequency Polygon: A frequency polygon is a graph constructed by using lines to join the midpoints of each interval or bin. The heights of the points represent the frequencies. A frequency polygon can be created from the histogram or by calculating the midpoints of the bins from the frequency distribution table.

2. Frequency Curve: A Frequency Curve is a smooth curve which corresponds to the limiting case of a histogram computed for a frequency distribution of a continuous distribution as the number of data points becomes very large.

3. Pie DiagramA pie diagram is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice is proportional to the quantity it represents.

Thus, option 1 is the correct answer.

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  1. Average
  2. Below average
  3. Above average
  4. Extreme

Calculation 

Let assume the class interval and frequency table for this histogram to solve this  question

Class interval

frequency

x

fx

0 - 10

4

5

20

10 - 20

5

15

75

20 - 30

6

25

150

30 - 40

7

35

245

40 - 50

8

45

360

50 - 60

3

55

165

60 - 70

2

65

130

Total

35

1145

Histogram for this data

In classification the data are arranged according to Ratios Differences Percentages Similarities

Average = Σfx/Σf

⇒ Average = 1145/35 = 32.7

⇒ Range = Maximum frequency - minimum frequency = 8 - 2

⇒ Range = 6

∴ The range of frequency is below average

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Concept:

Cumulative frequency:

  • The cumulative frequency of a class is defined as the sum of all frequencies up to the given class.
  • cumulative frequency is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors.
  • The last value will always be equal to the total for all observations

Calculation:

Given that, cumulative frequency of fifth observations is 8.

Frequency of given observations are 

x - 2, x - 4, y, -2x, x

Sum of frequencies = - 6 + y + x

Since this sum will be equal to cumulative frequency up to 5th observations.

⇒ - 6 + y + x = 8

⇒ y = 14 - x

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  1.  55 - 57
  2. 46 - 48
  3. 49 - 51
  4. 52 - 54

Concept:

Cumulative frequency is the sum of all the previous frequencies up to the current point.

The cumulative frequency is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors. The last value will always be equal to the total for all observations since all frequencies will already have been added to the previous total.

Also, Frequency of any class = cumulative frequency of class - cumulative frequency of preceding class

Calculation:

Let's calculate the frequency of each class using the cumulative frequencies of each class:

Class Cumulative Frequency Frequency
40 - 42 6 6
43 - 45 14 8
46 - 48 24 10
49 - 51 35 11
52 - 54 49 14
55 - 57 61 12

Clearly, we can see that 52 - 54 has the maximum frequency i.e.,14.

Hence, 52 - 54 has the maximum frequency.

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  1. size of responses
  2. experiment condition
  3. size of class
  4. number of observation

Calculation

Frequency distribution = frequency distribution is a list, table or graph that displays the frequency of various outcomes in a sample.

In frequency distribution, the number of classes usually depends upon the size of the classes

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Concept:

  • Cumulative frequency distribution is the sum of the frequency of class and all classes below it in a frequency distribution.
  • This means we can get cumulative frequency by adding up a value and all of the values that came before it.

Also,

Frequency of any class=cumulative frequency of class - cumulative frequency of preceding class.

Calculation:

From the above discussion, we can calculate the frequency from the cumulative frequency as below.

Class Cumulative frequency frequency
60 - 62 3 3
63 - 65 20 17
66 - 68 36 16
69 - 71 48 12
72 - 74 50 2


Clearly, from the above table, we can see that the 63 - 65 class has a maximum frequency.

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Given

Frequency density is actually the frequency per unit width.

Frequency of class interval 20 - 40 = 14

Formula used

\(Frequency ~density = \frac{Frequency}{(upper ~limit – lower~ limit) } \)

Calculation

\(⇒\frac{14}{40-20}\)

\(⇒\frac{14}{20}=0.7\)

Frequency density is 0.7 

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  1. Less than cumulative frequency ​
  2. More than cumulative frequency
  3. discrete frequency distribution
  4. Individual series

Option 1 : Less than cumulative frequency ​

Given

Frequency

4

7

11

16

23

Class interval

0 - 5

0 - 10

0 - 15

0 - 20

0 - 25

Calculation

Class interval

frequency

Cumulative frequency

0 – 5

4

4

0- 10

7

11

0 - 15

 11

22

0 - 20

16

38

          0 - 25

`23

61


Here, total frequency = 23

Cumulative frequency = 61

∴ Frequency distribution is less than cumulative frequency

In classification the data are arranged according to Ratios Differences Percentages Similarities
Important Points

 Cumulative frequency = it is the sum of the class and class below it in a frequency distribution

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Concept:

In a pie chart, the central angle of each slice of the pie is proportional to its value as compared to the total value.

θ = \(\rm \frac{\text{Value of Slice.}}{\text{Total Value.}}\times360^\circ\)

Calculation:

Angle corresponding to the Science graduates = θ = \(\rm \frac{\text{Number of Science graduates.}}{\text{Total number of graduates.}}\times360^\circ\).

\(\rm \frac{30}{30+70+50}\times360^\circ\)

\(\rm \frac{30}{150}\times360^\circ\)

= 72°.

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  1. 34 - 44
  2. 34.5 - 43.5
  3. 33.5 - 44.5
  4. 34 - 45

Given

Mid values = 30, 39, 48, 57, 66

Formula used

Mid value of class interval = (L1 + L2)/2

L1 = Lower limit

L2 = Upper limit

Calculation

For Second class mid value would be (L1 + L2)/2

Here L1 and L2 are Lower value and upper value of class interval

 Mid point of 30 and 39 = (L1 + L2)/2

⇒ (30 + 39)/2 = 34.5

⇒ Mid point of 39 and 48

⇒ (39 + 48)/2 = 43.5

⇒ Lower limit (L1 = 34.5)

⇒ Upper limit (L2 = 43.5)

The second class  of this distribution is 34.5 – 43.5

In classification the data are arranged according to Ratios Differences Percentages Similarities
Shortcut Trick

By options

1 – 34 – 44

Class size  44 – 34 = 10

39 – 30  =9

So size gap does not match so option 1 is wrong

2 – 34.5 – 43.5

Class size = 43.5 – 34.5 = 9

The mid point gap

39 – 30 = 9, 48 – 39 = 9, 57 – 48 = 9, and 66 – 57 = 9

So option (2) match with Class Size so option (2) is correct

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  1. 25 percent
  2. 45 percent
  3. 30 percent
  4. 40 percent

Screen savers do not reduce the use of power by your computer; once your screen saver kicks in, your monitor will draw its full power load.

All users, whether on desktops or laptops, should configure their computers to use the power-saving or energy star modes that shut down power to the monitor, hard drive, and computer itself after periods of inactivity.

Putting your computer in sleep mode allows it to use substantially less power, allows it to respond to some types of network activity, and allows you to not power off the computer.

A computer display in full use gobbles up 65 watts – but still uses 25 watts when in sleep mode. When off it uses 0.8 watts.

Setting computers, monitors, and copiers to use sleep-mode when not in use helps cut energy costs by approximately 40%.

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  1. Mean 
  2. Mode
  3. Median
  4. All of the above

Explanation:

Graphically mode can be determined by presenting the data in the form of a Histogram.

The highest Histogram indicates the modal class.

The intersection point of the lines diagonally joining the two top corners of the modal rectangles to the corners of the adjacent Histograms indicates the Modal Value.

Hence, option (2) is correct.

In classification the data are arranged according to Ratios Differences Percentages Similarities
Additional Information

Histogram - To find mode 

Ogives - To find median 

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Calculation:

Let the circle be the total number of students.

Therefore 100% = 360°

⇒ 1% = \(\frac{{360^\circ }}{{100}}\)

\(\therefore \;40\% \; = \frac{{360^\circ }}{{100}} \times 40 = 144^\circ\)

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  1. the height of the rectangle.
  2. the area of the rectangle.
  3. the width of the rectangle.
  4. the perimeter of the rectangle.

Option 2 : the area of the rectangle.

Concept:

  • When constructing a histogram with non-uniform (unequal) class widths, we must ensure that the areas of the rectangles are proportional to the class frequencies.
  • Remember that the histogram differs from a bar chart in that it is the area of the bar that denotes the value, not the height. This means that we would need to consider the widths in order to determine the height of each rectangle.

Calculation:

From the definition of histogram, it is clear that the frequency of a class is proportional to the area of the rectangle, and NOT to its height.

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  1. Discrete frequency distribution
  2. Cumulative frequency distribution
  3. Percentage frequency distribution
  4. Continuous frequency distribution

Option 1 : Discrete frequency distribution

The frequency distribution according to individual variate values is called Discrete frequency distribution.

Concept:

A discrete frequency distribution is a table that lists each number and the number of times (frequency) that it occurs in a list. The numbers are typically integers but they can be other step sizes provided that each number is an integral multiple of the step size.

The following frequency distribution is classified:

x: 5 15 38 47 68

y: 2   4   9   3   1

Here we can not find the relationship between the numbers 2, 4, 9, 3, and 1 hence, in this frequency distribution the values of the variable are determined individually.

In classification the data are arranged according to Ratios Differences Percentages Similarities
Additional Information

Continuous frequency distribution: A continuous frequency distribution is a series in which the data are classified into different class intervals without gaps and their respective frequencies are assigned as per the class intervals and class width.

Percentage frequency distribution: A percentage frequency distribution is a display of data that specifies the percentage of observations that exist for each data point or grouping of data points.

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  1. Bar diagram and Ogive
  2. Bar diagram and Line diagram
  3. Histogram and Line diagram
  4. Histogram and Ogive

Option 4 : Histogram and Ogive

Explanation

In the determination of median graphically we adopt any of the two methods. In both the methods we draw ogives as per requirement.

To calculate mode graphically a histogram of the given data is drawn at first.

∴ For determination of mode and median graphically, one considers Histogram and Ogive

In classification the data are arranged according to Ratios Differences Percentages Similarities
 

Bar diagram = A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent.

We can represent Mean on Bar graph

Ogive = An ogive (oh-jive), sometimes called a cumulative frequency polygon, is a type of frequency polygon that shows cumulative frequencies. In other words, the cumulative percents are added on the graph from left to right.

We represent partition value on ogive curve like median

Line graph = line graph is a type of chart used to show information that changes over time. We plot line graphs using several points connected by straight lines. We also call it a line chart

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  1. Mode
  2. Mean
  3. Median
  4. None of the above

Explanation:

Graphically, the median can be determined by the intersection point of Less than Ogive and More than Ogive.

The value of the x-axis corresponding to the intersection point indicates the median.

Hence, option (3) is correct.

In classification the data are arranged according to Ratios Differences Percentages Similarities
Additional Information

Ogives - To find median

Histogram - To find mode  

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  1. Only I
  2. Neither I nor II
  3. Only II
  4. Both I and II

Data are information collected in a systematic manner with the aim of deriving certain related conclusion. Let us discuss the sources for the collection of data. 

  • The collected data from different sources which are not arranged or organized in any manner are called raw data.
  • When data are collected directly from the source i.e., from the students. This is an instance of the data being collected from the primary source.
  • The data which are collected indirectly i.e., from the documents containing the information collected for some other purpose. Such indirect sources are called secondary source.
  • If the raw data consists of a few scores, then we arrange them in ascending (increasing) or descending (decreasing) order. Then it is called arrayed data

Hence, we conclude that the above statement is about arrayed data.

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