Statistics

The correct answer is "Descriptive Statistics."

Descriptive statistics is a branch of statistics that deals with the collection, organization, analysis, interpretation, and presentation of data. It is concerned with summarizing and describing the main features of a set of data using measures such as mean, median, mode, standard deviation, and range.

In contrast, inferential statistics is concerned with making inferences or generalizations about a population based on a sample of data. It involves hypothesis testing, estimating parameters, and making predictions.

Therefore, descriptive statistics provides the summary statistics of data, while inferential statistics is used to draw conclusions about a population based on sample data.

True.

A sample is a subset of a population that is selected for study. It is chosen to represent the entire population, as it is not feasible or practical to study the entire population. By studying a sample, researchers can make inferences about the entire population with a certain level of confidence. However, it is important to select a representative sample to ensure the validity of these inferences.

Population.

A population refers to the entire group of individuals, objects, or events that share a common characteristic or set of characteristics and are of interest to a researcher. In statistics, a population is the set of all measurements or data points that one wishes to analyze, and it includes all the individuals, items, or events that meet the criteria for inclusion in the study.

In contrast, a sample is a subset of the population that is selected for study. It is chosen to represent the entire population, as it is not feasible or practical to study the entire population.

Statistics holds the responsibility of describing the data collected.

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It provides a set of methods and tools that can be used to describe, analyze, and make inferences about data.

Probability, on the other hand, is a branch of mathematics that deals with the study of random events and the likelihood of their occurrence. It is often used in conjunction with statistics to make predictions and draw conclusions about data.

Therefore, statistics holds the responsibility of describing the data collected, while probability is used to study the likelihood of events and outcomes.

Data Analysis is the process of applying logical or statistical techniques to evaluate and describe data in a meaningful way.

Data analysis involves examining and interpreting data in order to extract insights and draw conclusions. This may involve applying various statistical techniques, such as descriptive statistics, hypothesis testing, regression analysis, and machine learning, to identify patterns, relationships, and trends in the data. The goal of data analysis is to transform raw data into useful information that can be used to inform decision-making, develop strategies, or solve problems.

Data collection, on the other hand, refers to the process of gathering and recording information, while data exploration refers to the initial examination and visualization of data to gain insights and identify patterns.

descriptive statistics can only be applied to sample datasets

Statistics is an art of learning data.

Statistics is a discipline that involves the collection, analysis, interpretation, and presentation of data. It provides a set of tools and techniques that can be used to explore, understand, and draw conclusions from data. In this sense, statistics can be thought of as an art form, as it requires creativity, intuition, and judgment to effectively analyze and interpret data.

Probability is also an important component of statistics, as it provides a foundation for understanding random events and the likelihood of their occurrence. However, probability is not specifically an art form, but rather a branch of mathematics that is concerned with the study of chance and uncertainty.

A branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data

Yes, statistics plays a vital role in many areas of computer science such as machine learning, data mining, and artificial intelligence.

Non-mutually Exclusive Event

A non-mutually exclusive event is an event that can occur simultaneously with another event. In this case, drawing either a King or a Queen card from a deck of cards are not mutually exclusive events, since a card can be both a King and a Queen (the Queen of Hearts, for example). The probability of drawing either a King or a Queen card is calculated by adding the probability of drawing a King card to the probability of drawing a Queen card, minus the probability of drawing a card that is both a King and a Queen (which is zero in this case).

In contrast, mutually exclusive events are events that cannot occur at the same time, such as drawing a King card and a Queen card from the same deck of cards. Simple events are the most basic outcomes of an experiment, while exhaustive events cover all possible outcomes of an experiment.

True

Continuous

Discrete

Null Hypothesis.

True

Range, Standard Deviation, and Variance

non-mutually exclusive event.

The expected value is calculated by multiplying each of the possible outcomes in the sample space with the likelihood of their occurrence, and then summing up all the values. It is a measure of the central tendency of a random variable, and represents the average value that we can expect to observe if we repeat the experiment many times.

Conditional probability is denoted by P(A|B), which represents the probability of event A occurring given that event B has occurred. It is read as "the probability of A given B".

The correct answer is "Exhaustive Event". An exhaustive event is a set of events that includes all possible outcomes in a sample space. In other words, an exhaustive event is a collection of events that covers all possibilities and leaves no outcomes outside the set. This means that one of the events in the set must occur when an experiment is performed.

The correct answer is "Poisson Distribution". The Poisson distribution is a discrete probability distribution that is often used to model the number of events occurring in a fixed interval of time or space, given the average number of events that occur during that interval. It is commonly used in fields such as insurance, finance, and telecommunications to model rare events such as accidents, equipment failures, or phone calls received per unit of time. The Poisson distribution is characterized by a single parameter λ (lambda), which represents the average rate of occurrence of the event.

The correct answer is "Binomial". In a binomial experiment, each trial has two possible outcomes, often referred to as success and failure. The probability of success is denoted by p, and the probability of failure is denoted by q, where q = 1 − p. The trials are independent, and the probability of success remains constant throughout the experiment. Binomial experiments are commonly used in fields such as biology, psychology, and quality control to model events that can be classified as either success or failure.

False. If another event does not influence the occurrence of an event, it is called an Independent Event. In probability theory, the occurrence of one event is said to be independent of the occurrence of another event if the occurrence of one event does not affect the probability of the occurrence of the other event.

The correct answer is "Inferential Statistics". Inferential statistics is the branch of statistics that deals with making inferences about a population based on information gathered from a sample. It involves using statistical methods to draw conclusions about a population parameter based on sample statistics. In contrast, descriptive statistics involves summarizing and describing the characteristics of a sample or population, without making any inferences beyond the data at hand.

The correct answer is "Random Variable". A random variable is a variable that takes on numerical values that are determined by the outcome of a statistical experiment. In other words, a random variable is a numerical representation of the outcomes of a random process. Random variables can be discrete or continuous, depending on whether they take on a countable or uncountable number of values, respectively. They are a fundamental concept in probability theory and statistics and are used to model a wide range of phenomena in many fields, including economics, engineering, and biology.

The correct answer is "Continuous". A continuous random variable is a variable that can take on any value in a given range or interval. It can take on an infinite number of values, and the values can be measured to any degree of accuracy, including decimals and fractions. Continuous random variables are often used to model phenomena that can take on a wide range of values, such as height, weight, and time. In contrast, a discrete random variable can only take on a finite or countably infinite number of values.

The correct answer is "Type 2". In hypothesis testing, the probability of not committing a Type 2 error is called the power of the test. A Type 2 error occurs when we fail to reject a null hypothesis that is actually false. The power of a test is the probability of rejecting the null hypothesis when it is false, or equivalently, the probability of correctly accepting the alternative hypothesis when it is true. The power of a test depends on various factors, such as the sample size, the level of significance, and the effect size.

The correct answer is "Hypothesis Testing". Hypothesis testing is a formal statistical procedure used to make decisions about whether to accept or reject a hypothesis. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and using statistical methods to determine the likelihood that the data is consistent with the null hypothesis. If the data is unlikely to have occurred by chance alone, the null hypothesis is rejected in favor of the alternative hypothesis. Hypothesis testing is widely used in fields such as science, engineering, and business to make informed decisions based on empirical evidence.

The correct answer is "All the options". The measures of central tendency are a set of descriptive statistics that provide information about the center of a distribution or a dataset. The three most commonly used measures of central tendency are the mean, median, and mode.

The mean is the arithmetic average of a dataset, and it is calculated by adding up all the values and dividing by the number of values.

The median is the middle value of a dataset, and it is the point that divides the data into two equal halves.

The mode is the value that appears most frequently in a dataset.

All three measures of central tendency are important in different contexts, and they can provide different insights into the characteristics of a dataset.