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Statistics is a branch of mathematics, used to perform various operations, i.e., data collection, organization, analyzing, and so on. There are several statistical methods used to get the conclusion for organized data (mainly numerical data) and make the best visualization of this data. The primary purpose of statistics is to make the raw or displayed data more useful for the statisticians
Suppose that you want to calculate the average number of students who got below 50% in their final exams. You also have to calculate the difference between their marks with other students' marks. We can solve this complex problem with statistical reports.
It is a short descriptive coefficient that summarizes the data set, which can be either a team of the entire or a sample of the population. It is the measure of central tendencies and variable measures.
Measures of Central Tendency include Mean, Median, and Mode. It is used to find the center point value of the numerical dataset.
The variability measure is a summary statistics used to represent the dispersion amount in a dataset. It indicates how far the data points from the center.
Inferential statistics is a set of assumptions that are made on the basis of information found by Descriptive Statistics. This type of statistics is used to take a random sample of data taken from a population to describe and make deductions about that population.
Regression analysis is one of the best statistical methods. It is used to examine the relationship between two dependent and independent variables with the same interest.
It is the collection of statistical models. ANOVA associated estimation procedures are used to analyze the differences among group means. It was developed by one of the greatest statisticians and biologist Ronald Fisher.
The ANCOVA is the general linear model that is used to blend ANOVA and regression. ANCOVA's primary motive is to evaluate that the means of dependent variables are equal to the categorical independent variable.
Correlation analysis is a statistical method. It is used to evaluate the relationship strength between two independent quantitative variables. If the correlation is high, then the variables may have a strong relationship with each other. If the correlation is weak, then the variables are hardly related to each other.
Least square is used in regression analysis to approximate the solution of an overdetermined system. It does that by minimizing the sum of the squares of the residuals. That has resulted in the results of every single equation. The best example of least squares is data fitting. Our experts can solve all equations of least squares.
Outlier refers to those data points, which significantly differs from the other observations. An outlier exists because of the measurement variability or if you have any experimental error. Most of the time, outliers create a critical error in statistical analysis. Every statistician always tries to avoid outliers in their statistical analysis. We also help you to get rid of outliers in your statistics analysis homework.
Statistical significance is interrelated to hypothesis testing. When it occurs, then it gives the null hypothesis. We have complete knowledge of statistical significance. We are always ready to solve complex problems with statistical significance.
Bayes is one of the crucial theorems in statistics. It is based on the prior knowledge of the condition that is related to the events. We have solved lots of Bayes’ theorem questions so far.
The combination is widely used in our day to day life. We use it to find the combo of any number of things. For this, we take k at a time without repetition. There are lots of types of combinations, and we use the terms like k-selection, k-multiset, or k-combination to find the combination.
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When we want to have the height, weight, and temperature measurements, then we use the continuous variables. Continuous distributions usually have a zero probability. On the other hand, the discrete probability is used when each particular value has no zero relationship with each other.
With the help of point estimation, we get the estimated value of the population parameter. On the other hand, interval estimation gives us the range of values that might contain the population parameter.
The basic phenomenon behind the principal component analysis is to reduce the large set into the small sets. And the small set should contain the relevant information of the large set. The variables in the small set are known as principal factors.
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The random variables can be a random quantity, aleatory variables, or stochastic variables. The random variables can be generated from random phenomena. Random variables are widely used in probability to predict the uncertain outcomes.
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The king has one sibling only. Here we are going to find out what is the probability that the sibling is male. Here is the hint you can assume that the probability of the birth of the male is ½ i.e. independent of other births. Please be careful about the additional assumptions that you will state to reach the answer.
Sport Obermeyer has to decide how many parkas to produce in this session of Winter 2019. The demand for the parkas depends on the Normal distribution with a mean 1200 and standard deviation 130. Here in this question, the Sport Obermeyer has 1250 Parkas. You need to find the probability that the demand is fully met.
You need to create a portfolio that is with three assets o.e. Bound A, bound B and Stock C. Their expected returns value on the investment horizon are µA = 3%, µB = 12%, µC =15%. The standard deviations of the returns are σA = 2%, σB = 8%, σC = 40%. The correlation between the assets are ρAB = −0.3, ρAC = −0.4, ρBC = 0.
Suppose that you have a portfolio in which the investor put 70% of the money in stock B and 15% on stock C i.e. P=0.7+1.5C. Find out the expected return and standard deviation. You can assume that the investor has a risk tolerance of σP = 25%. It means the Portfolio standard deviations can not exceed 25%. All you need to do is construct the portfolio with an expected return under the given risk constraint. Take the help of statistics software to solve this problem.
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