Classification of Randomized Designs (2023)

Randomized designs are a type of experimental design where randomization process is used to assign the units (like people or objects) to different treatment groups. The randomization process helps to control for bias and ensures that any observed differences between the groups are likely due to the treatment itself, rather than some other factors.

Randomized Designs

In randomized designs, the treatments are applied randomly, therefore the conclusions drawn are supported by statistical tests. The classification of randomized designs for single-factor are:

Example: A market gardener wants to test three types of peas, $A$, $B$, and $C$, on his land. He divides a square plot into nine equal squares, three to be planted with each type of pea. The problem he then faces is which square to plant with which type.

Classification of Randomized Designs

One method is a Completely Randomized Design (CRD) which might,

123
CAC
BAA
BBC
Allocation of Different Types of Peas Randomly to plots

This would be all right if all the plots were equally desirable. If however, there were prevailing north wind so that the northernmost plots were exposed, he might decide to use, a Randomize Complete Block Design (RCBD).

Randomized Complete Block Design, where each of the types $A$, $B$, and $C$ is planted once in each west-east block.

123
ABC
ACB
CBA
Allocation of Different Types of Pease in each West-East Block

If the gardener also felt that the soil to the east was rather better than that to the west, he would use, a Latin Square Design (LSD).

A Latin Square design, where each type of pea is planted once in each row (west-east), and once in each column (north-south).

Block 1Block 2Block 3
ABC
BCA
CAB
Allocation of Different Types of Pease planted once in each row (West-East), and once in each column (North-South)

For Randomized Designs, Note that

  • Completely Randomized Design (CRD) is a statistical experimental design where the treatments are assigned completely at random so that each treatment unit has the same chance (equal chance) of receiving any one treatment.
  • In CRD any difference among experimental units receiving the same treatment is considered as an experimental error.
  • CRD is applicable only when the experimental material is homogeneous (eg., homogeneous soil conditions in the field).
  • Since soil is heterogeneous in the field, the CRD is not a preferable method in field experiments. Therefore, CRD generally applies to the lab experimental conditions, as in labs, the environmental conditions can be easily controlled.
  • The concept of “local control” is not used in CRD.
  • CRD is best suited for experiments with a small number of treatments.
Design of Experients

The best design for a study will depend on the specific research question and the factors that one needs to control for. By incorporating randomization, you can control for extraneous variables that might influence the outcome and improve the validity of the findings.

However, the choice of the randomized design depends on the specific research question(s) being asked. It is important to consider the strengths and weaknesses of each design before making a decision.

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Chi-Square Distribution ($\chi^2$) Made Easy

The Chi-square distribution is a continuous probability distribution that is used in many hypothesis tests. The Chi-Square statistic always results in a positive value.

A Chi-Square variate (with $v$ degrees of freedom (df)) is the sum of $v$ independent, squared standard normal variates ($\sum\limits_{i=1}^v z_i^2$). It is denoted by $\chi^2_v$. The variance $s^2$ from a sample of normally distributed observations is distributed as $\chi^2$ with $v$ (the df) as a parameter referred to as df of the calculated variance. Symbolically,

$$\frac{v\cdot s^2}{\sigma^2} \sim \chi^2_v$$

Chi Square Distribution Table

The variance $s^2$ for $n$ observations from a $N(\mu, \sigma^2)$, the df is equal to $v=n-1$. The Chi-Square distribution is also used for the contingency (analysis of frequency) tables as an approximation to the distribution of complex statistics. All the families of Chi-Square distribution are specified by their degrees of freedom.

Chi-Square Family of Distributions

Chi-Square Distribution Case of the Gamma Distribution

The Chi-Square distribution is a particular case of the Gamma Distribution, the pdf is

$$P_{\chi^2}(x) = [2^{v/2}\Gamma(v/2)]^{-1} \chi^{(v-2)/2}e^{-x/2}, \quad x\ge 0$$

where $\Gamma(x)$ is the Gamma Distribution.

Normal Approximation to $\chi^2$

Method 1: The PDF and df of Chi-Square can be approximated by the normal distribution. For large $v$ df, the first two moments $z=\frac{(X-v)}{\sqrt{2v}}$, $X\sim \chi^2$.

Method 2: Fisher approximation (compensates the skewness of $X$)

$$\sqrt{2X} – \sqrt{2v-1} \sim N(0, 1)$$

Method 3: Approximation by Wilson and Hilferty is quite accurate. Defining $A=\frac{2}{9v}$, we have

$$\frac{\sqrt[3]{(X/v)}-1+A}{\sqrt{A}}\sim N(0, 1)$$

For the determination of percentage points

$$\chi^2_{v[P]}=v[z_P\sqrt{A}+1-A]^3$$

Generating Pseudo Random Variates

Following the schema allows the generation of random variates from $\chi^2_v$ distribution with $v>2$ df. It requires to generate serially random variates from the standard uniform $U(0,1)$ distribution.

Let $n=v$ degrees of freedom

\begin{align*}
C1 &= 1 + \sqrt{2/e} \approx 1.8577638850\\
C2 &= \sqrt{n/2}\\
C3 &= \frac{3n^2-2}{3n(n-2)}\\
C4 &= \frac{4}{n-2}\\
C5 &= n-2\\
\end{align*}

FAQs about Chi-Square Distribution

  1. What is the use of Chi-Square Probability Distribution?
  2. By which parameter is the family of $\chi^2$Distribution is specified?
  3. How Pseudo Random variates be used to generate a Chi-Square distribution?
  4. What is a normal approximation to Chi-Square?
  5. For $v>100$, the Chi-Square percentiles may be approximated by?

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Important Online MCQs Multivariate 2

The post is about the MCQs Multivariate Analysis test. It includes a Variance-Covariance matrix, Principal Component Analysis, Factor Analysis, Factor Loading, etc. Let us start with the Online MCQs Multivariate Quiz.

Online Multivariate Quiz

1. A factor is a combination of variables

 
 
 
 

2. In factor analysis the reliable variance

 
 
 
 

3. An advantage of using an experimental multivariate design over separate univariate designs is that using the multivariate analysis – – – – – – -.

 
 
 
 

4. In multivariate analysis the distribution of $\overline{X}$ is

 
 
 
 

5. A multivariate statistic that allows you to investigate the relationship between two sets of variables is

 
 
 
 

6. ——- is used for causal analysis

 
 
 
 

7. In principal component analysis, the components are

 
 
 
 

8. In principal component analysis (PCA) the first component contains

 
 
 
 

9. In the relation $\Sigma = V^{1/2} \rho ^{1/2} V^{1/2}$, the $V^{1/2} is called

 
 
 
 

10. In PCA, when the variables are measured in different units then PC extracted on the basis of

 
 
 
 

11. In multivariate analysis Var-Cov matrix is

 
 
 
 

12. A multivariate statistic that allows you to analyze several dependent variables from an experimental design simultaneously is

 
 
 
 

13. In multivariate analysis the distribution of the sample covariance matrix is

 
 
 
 

14. A factor loading of 0.80 means, generally speaking, that

 
 
 
 

15. Ffactor loading is

 
 
 
 

16. Correlational multivariate analysis includes

 
 
 
 

17. If $X \sim N (\mu, \Sigma)$ then $(X-\mu)’ \Sigma^{-1} (X-\mu)$ is distributed as

 
 
 
 

18. Factor analysis pinpoints the clusters of correlations between variables and for each cluster

 
 
 
 

19. The goal of multiple regression is to

 
 
 
 

20. In multivariate analysis, $n(\overline{x} – \mu)’ S^{-1} (\overline{x} – \mu)$ is called

 
 
 
 

An application of different statistical methods applied to the economic data used to find empirical relationships between economic data is called Econometrics. In other words, Econometrics is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference”.

Online MCQs Multivariate

  • In multivariate analysis Var-Cov matrix is
  • In the relation $\Sigma = V^{1/2} \rho ^{1/2} V^{1/2}$, the $V^{1/2} is called
  • If $X \sim N (\mu, \Sigma)$ then $(X-\mu)’ \Sigma^{-1} (X-\mu)$ is distributed as
  • In multivariate analysis, $n(\overline{x} – \mu)’ S^{-1} (\overline{x} – \mu)$ is called
  • In multivariate analysis the distribution of $\overline{X}$ is
  • In multivariate analysis the distribution of the sample covariance matrix is
  • In factor analysis the reliable variance
  • In principal component analysis (PCA) the first component contains
  • In principal component analysis, the components are
  • In PCA, when the variables are measured in different units then PCs extracted on the basis of
  • The goal of multiple regression is to
  • A multivariate statistic that allows you to investigate the relationship between two sets of variables is
  • Correlational multivariate analysis includes
  • An advantage of using an experimental multivariate design over separate univariate designs is that using the multivariate analysis – – – – – – -.
  • A multivariate statistic that allows you to analyze several dependent variables from an experimental design simultaneously is
  • ——- is used for causal analysis
  • Factor loading is
  • A factor loading of 0.80 means, generally speaking, that
  • A factor is a combination of variables
  • Factor analysis pinpoints the clusters of correlations between variables and for each cluster
MCQs Multivariate itfeature.com
  • Partial Least Squares (PLS) Regression is an example of multivariate analysis (MVA).
  • Multivariate Multiple Regression is a method of modeling multiple dependent variables, with a single set of predictor variables.
  • Testing text and visual elements on a webpage together.
  • An example of multivariate data is Vital signs recorded for a newborn baby: This includes multiple variables such as heart rate, respiratory rate, blood pressure, and temperature.

Online MCQs Multivariate

Online MCQs about various Subjects

Best Econometrics Quiz (2023)

The post is about Econometrics Quiz.

MCQs Econometrics Quiz List

Econometrics Quiz – 6MCQs Econometrics – 5MCQs Econometrics – 4
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Econometrics Quiz with Answers

An application of different statistical methods applied to the economic data used to find empirical relationships between economic data is called Econometrics. In other words, Econometrics is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference”.

Econometrics means “Economic Measurement”. Econometrics is the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of statistical inference.

Econometrics can also be defined as the empirical determination of economic laws. Econometrics can be classified as (i) Theoretical Econometrics and (ii) Applied Econometrics.

MCQs Econometrics Quiz

Econometric methods allow economists to estimate relationships between different economic variables, identify causal relationships, and make informed decisions based on evidence from real-world data. Econometrics is widely used in academia, government, and industry to address a variety of economic questions and inform policy-making.

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