This Statistics Test is about MCQs Basic Statistics Quiz with Answers. There are 20 multiple-choice questions from Basics of Statistics, measures of central tendency, measures of dispersion, Measures of Position, and Distribution of Data. Let us start with the MCQS Basic Statistics Quiz with Answers
Online Multiple-Choice Questions about Basic Statistics with Answers
Online MCQs Basic Statistics Quiz
If any value in the data is negative, it is not possible to calculate
Mode of the values 2, 6, 8, 6, 12, 15, 18, and 8 is
Mode of the values 3, 5, 8, 10, and 12 is
The first step in computing the median is
If $x=3$ then which of the following is the minimum
The dispersion expressed in the form of a ratio or coefficient and independent from units of measurement is called
The half of the difference between the third and first quartiles is called
The difference between the largest and smallest value in the data is called
The most important measure of dispersion is
Which of the following is a relative measure of dispersion
Which of the following is an absolute measure of dispersion
If 6 is multiple t all observations in the data, the mean is multiplied by
Which of the properties of Average Deviation considers Mathematics assumption wrong?
What would be the changes in the standard deviation if different values are increased by a constant?
Two sets of distribution are as follows. For both of the sets, the Range is the same. Which of the demerits of Range is shown here in these sets of distribution? Distribution 1: 30 14 18 25 12 Distribution 2: 30 7 19 27 12
For a set of distributions if the value of the mean is 20 and the mode is 14 then what is the value of the median for a set of distributions?
Who used the term Statistics for the first time?
The median is larger than the arithmetic mean when
Fill in the missing words to the quote: “Statistical methods may be described as methods for drawing conclusions about —————- based on ————– computed from the —————“.
In general, which of the following statements is FALSE?
This post is about Inferential Statistics (or statistical inference) and some of its related terminologies. This is a field of statistics that allows us to understand and make predictions about the world around us.
Table of Contents
Parameter and Statistic
Any measurable characteristic of a population is called a parameter. For example, the mean of a population is a parameter. OR
Numerical values that describe the characteristics of a whole population are called parameters, commonly presented in Greek Letters.
Any measurable characteristic of a sample is called a statistic. For example, the mean of a sample is a statistic. OR
Numerical measures describing the characteristics of a sample are called statistics, represented by Roman Letters.
Population and Sample
Population: The entire group of individuals, objects, or data points that one is interested in studying. A population under study can be finite or infinite. However, often too large or impractical to study directly.
Sample: A smaller, representative subset of the population. It is used to gain insights about the population without having to study every member. A sample should accurately reflect the characteristics of the population. Â
Inference
A Process of drawing conclusions about a population based on the information contained in a sample taken from that population
Estimator
An estimator is a rule (method, formula) that tells how to calculate the value of an estimate based on the measurements contained in a sample. The sample mean is one possible estimator of the population mean $\mu$.
An estimator will be a good estimator in the sense that the distribution of an estimator is concentrated near the value of the parameter.
Estimate
Estimate is a way to use samples. There are many ways to estimate a parameter. Estimates are near to reality (biased or crude). Decisions are very accurate if the estimate is near to reality.
$X_1, X_2, \cdots, X_n$Â is a sample and $\overline{X}$Â is an estimator. $x_1, x_2, \cdots, x_n$Â are sample observation and $\overline{x}=\frac{\Sigma x_i}{n}$Â is an estimate.
Estimation
Estimation is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable.
Statistical Inference (or Inferential Statistics)
Any process (art) of drawing inferences (conclusions) about the population based on limited information contained in a sample taken from the same population is called statistical inference (or inferential statistics). It is difficult to draw an inference about the population because the study of the entire universe (population) is not simple. To get some idea about the characteristics (parameters) of the population, we choose a part of a reasonable size, generally, referred to as a sample (by some appropriate method).
Statistical inference is a powerful set of tools used to conclude a population based on data collected from a sample of that population. It allows us to make informed decisions and predictions about the larger group even when we have not examined every single member.
Why Estimate?
Speed: Often, an estimate is faster to get than an exact calculation.
Simplicity: It can simplify complex problems.
Decision-Making: Estimates help one to make choices when one does not have all the details.
Checking: One can use estimates to check if a more precise answer is reasonable.
Why is Statistical Inference Important?
Decision-making: It helps us make informed decisions in various fields, such as medicine, business, and social sciences.
Research: It is crucial for conducting research and drawing meaningful conclusions from data.
Understanding the World: It allows us to understand and make predictions about the world around us.
This post is about some solved probability questions. These questions make use of (i) the Addition Law of Probabilities, and (ii) the Multiplication Law of Probabilities.
Solved Probability Questions
Question 1: Box A contains 5 Green and 7 Red balls. Box B contains 3 Green, 3 Red, and 6 Yellow balls. A box is selected at random, and a ball is drawn at random from it. What is the probability that the bill drawn is green?
Solution:
Box A
Total Balls: 5 + 7 = 12 Prob(Green) = $\frac{3}{12}$
Note that $P(X=11\,\, and X=5) = 0$, because the sum of two dice cannot be at the same time 5 and 11.
Question 3: A marble is drawn at random from a box containing 10 red, 30 white, 20 blue, and 15 orange marbles. What is the probability that it is (i) orange or red (ii) not red or blue (iii) not blue, (iv) white, (v) red, white, or blue.
Solution:
Total number of balls = 10 + 30 + 20 + 15 = 75 Number of Orange balls = 15 Number of Blue balls = 20 Number of White balls = 30 Number of Red balls = 10
P(a marble drawn is red or orange) = P(Red marble) + P(Orange marble) $$=\frac{10}{75} + \frac{15}{75} = \frac{1}{3}$$
P(a marble drawn is not red or blue) = P(not Red) + P(Blue) – P(Blue and not Red) $$=\frac{65}{75} + \frac{20}{75} – \frac{20}{75} = \frac{65}{75}$$
P(a ball drawn is not Blue) = $1 – P(Blue) = 1 – \frac{20}{75} = 0.733$
Question 4: If two dice are thrown what are the various total number of dots that may turn up? What are the probabilities of each of them? What is the probability that the number of dots will total at least four?
Solution:
When two dice are thrown together, the minimum total number of dots is 2 (1, 1), and the maximum dots possible are 12 (6, 6). Therefore
Probability of 2 dots (1, 1) = $\frac{1}{36}$
Probability of 3 dots {(2, 1), (1, 2)} = $\frac{2}{36} = \frac{1}{18}$
Probability of 4 dots {(2,2) (3,1) (1,3)} = $\frac{3}{36} = \frac{1}{12}$
Probability of 5 dots {(4,1) (1,4) (2,3) (3,2)} = $\frac{4}{36} = \frac{1}{9}$
Question 6: A class contains 10 men and 20 women of which half men and half women have brown eyes. What is the probability that a person chosen at random is a man or has brown eyes?
Solution:
Let $A$ be the event that it is a man (10 out of 30) Let $B$ be the event that the person has brown eyes (5 men and 10 women: 15 out of 30)
$P(A\cap B)$ is a man AND has brown eyes $\frac{5}{30}$
Question 7: A drawer contains 50 bolts and 150 nuts. Half of the bolts and half of the nuts are rested. If one item is chosen at random, what is the probability that it is rusted or is a bolt?
Solution:
Number of Bolts = 50 NUmber of Nuts = 150 Total number of Items = 50 + 150 = 200
Item chosen is rusted: $P(A) = \frac{100}{200} = \frac{1}{2}$ Item chosen is bolt: $P(B) = \frac{50}{200} = \frac{1}{4}$ Ite is Rusted and Bolt = $P(A\cap B) = P(A) \cdot P(B) = \frac{1}{2}\cdot \frac{1}{4} = \frac{1}{8}$