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Uploaded: 2022-05-06
Duration: 4 min 31 s
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[Audio] Correlation Analysis:-In terms of market research, this means that correlation analysis is used to analyze quantitative data gathered from research methods such as surveys and polls, to identify whether there are any significant connections, patterns, or trends between the two. Correlation Coefficient:- It operates under the assumption that the data being used is ordinal, which here means that the numbers do not indicate quantity, but rather they signify a position of place of the subject's standing. If the correlation coefficient is greater than zero, it is a positive relationship. Conversely, if the value is less than zero, it is a negative relationship. A value of zero indicates that there is no relationship between the two variables.. 

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Correlation Analysis is a statistical method that is used to discover if there is a relationship between two variables/datasets, and how strong that relationship may be.

The  coefficient is used to see if there is any significant relationship between the two datasets

[Audio] What is then required is to fully specify both the deviated and un-deviated conditions, and then compare the two so that changes or differences can be identified. Any change identified in this process thus becomes a candidate cause of the overall deviation. Change analysis is heavily dependent on comparison with similar situations. However, there are varying degrees of similarity, depending on how close the un-deviated condition is to the deviation under investigation. The best-case scenario for change analysis is when you have the previous operational history for the exact same task or operation. In this case, changes or differences that could have contributed to the deviation are easily identifiable. 

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Change Analysis:-  As suggested by the name of the technique, change analysis is based on the concept that change (or difference) can lead to deviations in performance. This presupposes that a suitable basis for comparison exists.

C hange  analysis is a  Root Cause Analysis  technique that uses the precise specification of a single deviation ( problem  or adverse  event ) so that changes and/or differences (potential  causal factor s) can be found by comparison to closely related un-deviated situations.

[Audio] There is a wide range of techniques and tools used in outlier analysis. However, it's often very easy to spot outlying data points. As a result, there's really no excuse not to perform outlier analysis on any and all datasets. t improves the quality of the dataset being subject to analysis. Of course, this in turn brings benefits. With a higher-quality dataset, analysts can expect to draw more accurate conclusions (and more of them). There are a wide variety of techniques that can be used to identify outliers in datasets. Some of the techniques that are used to find the outliers are like sorting, graphing, Z-scores, etc.. 

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Outlier analysis is the process of identifying outliers, or abnormal observations, in a dataset. Also known as outlier detection, it’s an important step in data analysis, as it removes erroneous or inaccurate observations which might otherwise skew conclusions.

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SQL  is a database computer language designed for the retrieval and management of data in a relational database. It is a computer language for storing, manipulating, and retrieving data stored in a relational database. SQL is the standard language for Relational Database systems. All the Relational Database Management Systems (RDMS) like MySQL, MS Access, Oracle, Sybase, Informix, Postgres, and SQL Server use SQL as their standard database language. Why SQL Nowadays, SQL is widely used in data science and analytics. Following are the reasons which explain why it is widely used: The basic use of SQL for data professionals and SQL users is to insert, update, and delete the data from the relational database. SQL allows the data professionals and users to retrieve the data from the relational database management systems. It also helps them to describe the structured data.

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Correlation Analysis  is a statistical method that is used to discover if there is a relationship between two variables/datasets, and how strong that relationship may be. C hange  analysis  is a  Root Cause Analysis  technique that uses the precise specification of a single deviation ( problem  or adverse  event ) so that changes and/or differences (potential  causal factor s) can be found by comparison to closely related un-deviated situations. Outlier analysis  is the process of identifying outliers, or abnormal observations, in a dataset. Also known as outlier detection, it’s an important step in data analysis, as it removes erroneous or inaccurate observations which might otherwise skew conclusions.  SQL  is a database computer language designed for the retrieval and management of data in a relational database. It is a computer language for storing, manipulating, and retrieving data stored in a relational database.

[Audio] Answers. 1. b 2. c 3. a 4.b 5.a 6. c. Background pattern Description automatically generated. 

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1. How are correlational studies different from experiments? Causation can be inferred b. Causation cannot be inferred. c. The variable is manipulated by the researcher d. They are part of qualitative research. 2.  How are correlations graphically represented? Through a model b. Through a table c. Through a scatter plot d. Through box plot 3.  A value that is much higher or much lower than the other values in a set of data. Outlier  b.Box  plot  c.Range  d. Histogram 4.  A data set contains only one outlier. Which statistical measure will most likely remain the same if the outlier is removed? Mean b. Mode c. Range d. Standard Deviation 5.  Which of the following is not a Clause in SQL? a.Below  b. Limit c. Where d. Group By 6. How many Primary keys can be created on a table? Infinity b.2 c.1 d. 24

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Correlation Analysis:-In terms of market research, this means that correlation analysis is used to analyze quantitative data gathered from research methods such as surveys and polls, to identify whether there are any significant connections, patterns, or trends between the two.

Correlation Coefficient:- It operates under the assumption that the data being used is ordinal, which here means that the numbers do not indicate quantity, but rather they signify a position of place of the subject's standing.

If the correlation coefficient is greater than zero, it is a positive relationship.

Conversely, if the value is less than zero, it is a negative relationship.

A value of zero indicates that there is no relationship between the two variables.

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What is then required is to fully specify both the deviated and un-deviated conditions, and then compare the two so that changes or differences can be identified.

Any change identified in this process thus becomes a candidate cause of the overall deviation.

Change analysis is heavily dependent on comparison with similar situations.

However, there are varying degrees of similarity, depending on how close the un-deviated condition is to the deviation under investigation.

The best-case scenario for change analysis is when you have the previous operational history for the exact same task or operation.

In this case, changes or differences that could have contributed to the deviation are easily identifiable

There is a wide range of techniques and tools used in outlier analysis.

However, it's often very easy to spot outlying data points.

As a result, there's really no excuse not to perform outlier analysis on any and all datasets.

t improves the quality of the dataset being subject to analysis.

With a higher-quality dataset, analysts can expect to draw more accurate conclusions (and more of them).

There are a wide variety of techniques that can be used to identify outliers in datasets.

Some of the techniques that are used to find the outliers are like sorting, graphing, Z-scores, et cetera

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