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[Scarlett OHara]

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The value of the perfect negative correlation is -1. This means that when two variables have a perfect negative correlation, they move in opposite directions with a linear relationship. As one variable increases, the other decreases by the same proportion. The closer the correlation coefficient is to -1, the stronger the negative correlation between the variables.

 

[Scarlett OHara]

The value of the perfect negative correlation is -1. This means that when two variables have a perfect negative correlation, they move in opposite directions with a linear relationship. As one variable increases, the other decreases by the same proportion. The closer the correlation coefficient is to -1, the stronger the negative correlation between the variables.

The correlation coefficient is sensitive to outlying points therefore the correlation coefficient is resistant.
Select one:
True
False

 

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The correlation coefficient is sensitive to outlying points therefore the correlation coefficient is resistant.
Select one:
True
False

False. The correlation coefficient is not resistant to outlying points. A single outlying point can greatly affect the value of the correlation coefficient, making it appear stronger or weaker than it actually is. Therefore, it is important to visually inspect the data and remove any outliers before calculating the correlation coefficient. Alternatively, one can use a more robust measure of correlation, such as Spearman's rank correlation coefficient, which is less sensitive to outliers.

 

[Scarlett OHara]

False. The correlation coefficient is not resistant to outlying points. A single outlying point can greatly affect the value of the correlation coefficient, making it appear stronger or weaker than it actually is. Therefore, it is important to visually inspect the data and remove any outliers before calculating the correlation coefficient. Alternatively, one can use a more robust measure of correlation, such as Spearman's rank correlation coefficient, which is less sensitive to outliers.

This particular type of analysis is useful when a researcher wants to establish if there are possible connectionsbetween variables.
Select one:
a.T-Test
b.Chi-Square
c.ANOVA
d.Correlation Analysis

 

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This particular type of analysis is useful when a researcher wants to establish if there are possible connectionsbetween variables.
Select one:
a.T-Test
b.Chi-Square
c.ANOVA
d.Correlation Analysis

The correct answer is d. Correlation Analysis. Correlation analysis is a statistical method used to examine the relationship between two or more variables. It is useful when a researcher wants to establish if there are possible connections between variables. The correlation coefficient, which ranges from -1 to +1, is used to measure the strength and direction of the relationship between the variables. A positive correlation indicates that as one variable increases, the other variable also increases, while a negative correlation indicates that as one variable increases, the other variable decreases.

 

[Scarlett OHara]

The correct answer is d. Correlation Analysis. Correlation analysis is a statistical method used to examine the relationship between two or more variables. It is useful when a researcher wants to establish if there are possible connections between variables. The correlation coefficient, which ranges from -1 to +1, is used to measure the strength and direction of the relationship between the variables. A positive correlation indicates that as one variable increases, the other variable also increases, while a negative correlation indicates that as one variable increases, the other variable decreases.

If most points depict a dispersed descending line this would suggest there would be a weak positive relationship
Select one:
True
False

 

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If most points depict a dispersed descending line this would suggest there would be a weak positive relationship
Select one:
True
False

False. If most points depict a dispersed descending line, this would suggest that there is a weak negative relationship between the variables. A negative relationship means that as one variable increases, the other variable decreases. The degree of the negative relationship can be determined by calculating the correlation coefficient, which ranges from -1 to +1. If the correlation coefficient is close to -1, then the negative relationship is strong, while a correlation coefficient close to 0 suggests a weak negative relationship.

 

[Scarlett OHara]

False. If most points depict a dispersed descending line, this would suggest that there is a weak negative relationship between the variables. A negative relationship means that as one variable increases, the other variable decreases. The degree of the negative relationship can be determined by calculating the correlation coefficient, which ranges from -1 to +1. If the correlation coefficient is close to -1, then the negative relationship is strong, while a correlation coefficient close to 0 suggests a weak negative relationship.

It is the measurement of correlation and ranges (depending on the correlation) between +1 and -1. +1 indicates the strongest positive correlation possible, and -1 indicates the strongest negative correlation possible.

 

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It is the measurement of correlation and ranges (depending on the correlation) between +1 and -1. +1 indicates the strongest positive correlation possible, and -1 indicates the strongest negative correlation possible.

True. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. It ranges from -1 to +1, with a value of +1 indicating a perfect positive correlation (when one variable increases, the other variable also increases), a value of -1 indicating a perfect negative correlation (when one variable increases, the other variable decreases), and a value of 0 indicating no correlation (there is no linear relationship between the variables). The closer the correlation coefficient is to +1 or -1, the stronger the correlation between the variables.

 

[Scarlett OHara]

True. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. It ranges from -1 to +1, with a value of +1 indicating a perfect positive correlation (when one variable increases, the other variable also increases), a value of -1 indicating a perfect negative correlation (when one variable increases, the other variable decreases), and a value of 0 indicating no correlation (there is no linear relationship between the variables). The closer the correlation coefficient is to +1 or -1, the stronger the correlation between the variables.

It is the measurement of correlation and ranges (depending on the correlation) between +1 and -1. +1 indicates the strongest positive correlation possible, and -1 indicates the strongest negative correlation possible.
Select one:
a.Autocorrelation
b.Correlation Analysis
c.Spearman's Rank
d.Pearson's product-moment coefficient

 

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It is the measurement of correlation and ranges (depending on the correlation) between +1 and -1. +1 indicates the strongest positive correlation possible, and -1 indicates the strongest negative correlation possible.
Select one:
a.Autocorrelation
b.Correlation Analysis
c.Spearman's Rank
d.Pearson's product-moment coefficient

The correct answer is d. Pearson's product-moment coefficient. Pearson's product-moment coefficient is a measure of the strength and direction of the linear relationship between two continuous variables. It ranges from -1 to +1, with a value of +1 indicating a perfect positive correlation, a value of -1 indicating a perfect negative correlation, and a value of 0 indicating no correlation. The closer the correlation coefficient is to +1 or -1, the stronger the correlation between the variables.

 

[Scarlett OHara]

The correct answer is d. Pearson's product-moment coefficient. Pearson's product-moment coefficient is a measure of the strength and direction of the linear relationship between two continuous variables. It ranges from -1 to +1, with a value of +1 indicating a perfect positive correlation, a value of -1 indicating a perfect negative correlation, and a value of 0 indicating no correlation. The closer the correlation coefficient is to +1 or -1, the stronger the correlation between the variables.

In this correlation the correlation coefficient is not robust due to the fact that strong linear relationships between the variables are not recognized
Select one:
a.Correlation Analysis
b.Spearman's Rank
c.Pearson's product-moment correlation
d.Autocorrelation

 

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In this correlation the correlation coefficient is not robust due to the fact that strong linear relationships between the variables are not recognized
Select one:
a.Correlation Analysis
b.Spearman's Rank
c.Pearson's product-moment correlation
d.Autocorrelation

The correct answer is b. Spearman's Rank. Spearman's rank correlation coefficient is a non-parametric measure of the strength and direction of the monotonic relationship between two continuous or ordinal variables. Unlike Pearson's product-moment correlation coefficient, Spearman's rank correlation coefficient does not assume that the relationship between the variables is linear, and is therefore more robust to non-linear relationships. It ranges from -1 to +1, with a value of +1 indicating a perfect monotonic positive correlation, a value of -1 indicating a perfect monotonic negative correlation, and a value of 0 indicating no monotonic correlation.

 

[Scarlett OHara]

The correct answer is b. Spearman's Rank. Spearman's rank correlation coefficient is a non-parametric measure of the strength and direction of the monotonic relationship between two continuous or ordinal variables. Unlike Pearson's product-moment correlation coefficient, Spearman's rank correlation coefficient does not assume that the relationship between the variables is linear, and is therefore more robust to non-linear relationships. It ranges from -1 to +1, with a value of +1 indicating a perfect monotonic positive correlation, a value of -1 indicating a perfect monotonic negative correlation, and a value of 0 indicating no monotonic correlation.

correlation exists if one variable increases simultaneously with the other, i.e. the high numerical values of one variable relate to the high numerical values of the other.
Select one:
a.Regression
b.Positive
c.Analytical
d.Negative

 

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correlation exists if one variable increases simultaneously with the other, i.e. the high numerical values of one variable relate to the high numerical values of the other.
Select one:
a.Regression
b.Positive
c.Analytical
d.Negative

The correct answer is b. Positive. A positive correlation exists when two variables increase or decrease together, meaning that high numerical values of one variable are associated with high numerical values of the other variable, and low numerical values of one variable are associated with low numerical values of the other variable. The correlation coefficient for a positive correlation ranges from 0 to +1, with a value of +1 indicating a perfect positive correlation.

 

[Scarlett OHara]

The correct answer is b. Positive. A positive correlation exists when two variables increase or decrease together, meaning that high numerical values of one variable are associated with high numerical values of the other variable, and low numerical values of one variable are associated with low numerical values of the other variable. The correlation coefficient for a positive correlation ranges from 0 to +1, with a value of +1 indicating a perfect positive correlation.

A bell shaped curve to a scatter graph would suggest what?
Select one:
a.
There would be a non-linear relationship and a Pearson's product-moment should be used
b.
There would be a non-linear relationship and a Pearson's product-moment should not be used
c.
There is an outlier but a Pearson's product moment can still be used
d.
There would be a linear relationship and a Pearson's product-moment should be used

 

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A bell shaped curve to a scatter graph would suggest what?
Select one:
a.
There would be a non-linear relationship and a Pearson's product-moment should be used
b.
There would be a non-linear relationship and a Pearson's product-moment should not be used
c.
There is an outlier but a Pearson's product moment can still be used
d.
There would be a linear relationship and a Pearson's product-moment should be used

The correct answer is d. There would be a linear relationship and a Pearson's product-moment should be used. A bell-shaped curve in a scatter plot suggests a linear relationship between the two variables. This is because the bell-shaped curve indicates that the data is normally distributed, which is a characteristic of a linear relationship. In this case, Pearson's product-moment correlation coefficient can be used to determine the strength and direction of the linear relationship between the two variables.

 

[Scarlett OHara]

The correct answer is d. There would be a linear relationship and a Pearson's product-moment should be used. A bell-shaped curve in a scatter plot suggests a linear relationship between the two variables. This is because the bell-shaped curve indicates that the data is normally distributed, which is a characteristic of a linear relationship. In this case, Pearson's product-moment correlation coefficient can be used to determine the strength and direction of the linear relationship between the two variables.

It is is calculated by taking the ratio of the sample of the two variables to the product of the two standard deviations and illustrates the strength of linear relationships
Select one:
a.Autocorrelation
b.Correlation Analysis
c.Pearson's product-moment coefficient
d.Spearman's Rank

 

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It is is calculated by taking the ratio of the sample of the two variables to the product of the two standard deviations and illustrates the strength of linear relationships
Select one:
a.Autocorrelation
b.Correlation Analysis
c.Pearson's product-moment coefficient
d.Spearman's Rank

The correct answer is c. Pearson's product-moment coefficient. Pearson's product-moment correlation coefficient is calculated by taking the ratio of the sample covariance of the two variables to the product of their standard deviations. It is a measure of the strength and direction of the linear relationship between two continuous variables. The closer the correlation coefficient is to +1 or -1, the stronger the linear relationship between the variables.