Easy MCQs Non-Parametric Methods

Most of the MCQs on this page covered Estimate and Estimation, Testing of Hypothesis when the assumption of population parameters are unknown, that is Non-Parametric Methods, etc.

MCQs Non-Parametric Methods Quizzes List

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MCQs Non-Parametric Methods

The relationship/ Dependency between the attributes is called association and the measure of degrees of relationship between the attributes is called the coefficient of association. The Chi-Square Statistic is used to test the association between the attributes. The Chi-Square Association is defined as

$$\chi^2 = \sum \frac{(of_i – ef_i)^2}{ef_i}\sim \chi^2_{v},$$

where $v$ denotes the degrees of freedom.

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Best Time Series MCQ with Answers Quizzes

This post contains the Online Time Series MCQ with Answers. Click the quiz and start with the Online Time Series MCQs Test.

Time Series MCQ with Answers

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Time series analysis deals with the data observed with some time-related units such as a month, days, years, quarters, minutes, etc. Time series data means that data is in a series of particular periods or intervals. Therefore, a set of observations on the values that a variable takes at different times.

Time Series MCQ with Answers

Real-World Applications of Time Series Analysis

  • Finance: Predicting stock prices, and analyzing market trends.
  • Sales and Marketing: Forecasting demand, and planning promotions.
  • Supply Chain Management: Optimizing inventory levels, and predicting product needs.
  • Healthcare: Monitoring patient health trends, and predicting disease outbreaks.
  • Environmental Science: Forecasting weather patterns, and analyzing climate change.
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Best Online Correlation and Regression Quiz

This Post contains Online Correlation and Regression Quiz, Multiple Regression AnalysisCoefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, Multicollinearity, Heteroscedasticity, Autocorrelation, etc. Click the links below to start with the MCQs on the Online Correlation and Regression Quiz.

MCQs Online Correlation and Regression Quiz

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Correlation analysis is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of the correlation lies between $-1$ and $+1$. The regression analysis describes how an explanatory variable is numerically related to the dependent variables.

Application or Regression

The formula to compute the correlation coefficient is:

$$r = \frac{n\sum X_i Y_i – \sum X_i \sum Y_i}{\sqrt{[n\sum X_i^2 – (\sum X_i)^2][n\sum Y_i^2 – (\sum Y_i)^2]}} $$

The general regression equation is $Y_i = a + bX_i$. The slope coefficient and intercept of the regression model can be computed as

$$\begin{align*}
b &= \frac{n\sum X_i Y_i – \sum X_i \sum Y_i}{n\sum X_i^2 – (\sum X_i)^2}\\
a &= \overline{Y} – b\overline{X}
\end{align*}$$

Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed using a graphical representation between the variables. We can also compute the strength of the relationship between variables by performing numerical calculations using appropriate computational formulas.

Online Correlation and Regression Quiz

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both correlation and regression are used to indicate how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.

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Designs of Experiment Terminology (2023)

Planning an experiment to obtain appropriate data and drawing inferences from the data concerning any problem under investigation is known as the design and analysis of the experiment or simply the designs of experiment (DOE).

Important Designs of Experiment Terminology are:

EXPERIMENT: An experiment deliberately imposes a treatment on a group of objects or subjects in the interest of observing the response.

EXPERIMENTAL UNIT: The experimental unit is the basic entity or unit on which the experiment is performed. It is an object to which the treatment is applied and in which the variable under investigation is measured and analyzed. For example, the experimental unit may be a single person, animal, plant, manufactured item, or country that belongs to a larger collection of such entities being studied.

Designs of Experiment

Identify the Experimental Units

  • A teacher practices the different teaching methods on different groups in her class to see which yields the best results.
  • A doctor treats a patient with a skin condition with different creams to see which is most effective.

The experimental unit is the physical entity or subject exposed to the treatment independently of other units. In other words, it is the basic unit on which the experiment is performed (smallest division of experimental material).

TREATMENTS: In experiments, a treatment is something that researchers administer to experimental units. For example, a corn field is divided into four, each part is ‘treated’ with a different fertilizer to see which produces the most corn.

Treatment is an experimental condition whose effect is to be measured and compared. For example, animal diets, temperature, soil types, and brands of tires.

FACTOR: A factor of an experiment is a controlled independent variable; a variable whose levels are set by the experimenter. A factor is a general type or category of treatments. Different treatments constitute different levels of a factor.

Designs of Experiment

EXPERIMENTAL ERROR

It describes the variation among identically and independently treated experimental units. In the designs of experiments, various origins of experimental error include:

  • The natural variation among the experimental units.
  • Inability to reproduce the treatment conditions exactly from one unit to another.
  • Interaction of treatments and experimental units.
  • Any other extraneous factors that influence the measured characteristics.

There are two types of errors:

  1. Systematic Errors
    Systematic Errors are caused by a consistent bias in one direction, consistently pushing your results away from the true value. Systematic errors can be caused by a variety of factors, such as a faulty instrument, an incorrect calibration, or an error in the experimental design. Systematic errors will cause data points to shift all in the same direction, away from the true value.
  2. Random Error
    The random error is caused by small and unpredictable variations that occur in every experiment. Random errors can come from a variety of sources, such as slight differences in how a measurement is made, or fluctuations in environmental conditions. Random errors tend to cause data points to scatter randomly around the true value.

The experimental error can be controlled by

  • Blocking
  • Proper plot technique
  • Data Analysis

EXPERIMENTAL DESIGN

An experimental design is a plan to collect the data relevant to the problem under investigation. In such a way as to provide a basis for valid and objective inferences about the stated problem.

The plan usually consists of the selection of the treatments, specifications of the experimental layouts, allocation of the treatments, and collection of observations for analysis.

Hence designs of Experiments are simply a sequence of steps taken ahead of time to ensure that the appropriate data will be obtained in a way that permits an objective analysis leading to a valid analysis concerning the problem.

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