Calc Correlation Coefficient & Regression Equation for Normal Variable X

In summary, the given data provides values for two normal random variables, X and Z, with parameters \mu and \sigma^2 for X and 0 and 1 for Z. The questions ask for the correlation coefficient between the two variables, the regression equation to predict X if Z is known, and the values of \mu and \sigma^2. After using linear regression to find the correlation coefficient of 1, it is determined that the regression equation to predict X if Z is known is x = 12.5z + 59.5. It is also found that \mu = 59.5 and \sigma = 12.5.
  • #1
steven10137
118
0

Homework Statement


x represents values of a Normal random variable X, with parameters [tex]\mu[/tex] and [tex]\sigma^2[/tex]
z represents corresponding values of normal random variable Z, with parameters 0 and 1.

z x
-3 22
-2 34.5
1 72
3 97

The following questions relate to the tabled data:
a) calculate the correlation co-efficient between z and x.
b) calculate the regression equation used to predict x if z is unknown
c) determine the values of [tex]\mu[/tex] and [tex]\sigma^2[/tex]

2. The attempt at a solution
Well the random variable Z has a mean 0 and variance 1, meaning that it is a standard normal distribution.

a) can I just use a linear regression on the data? this gives a correlation co-efficient of 1
b) using the same regression, x=12.5z+59.5 ??
c) Now I am stuck.

Cheers
Steven
(sorry I have exams coming up ... hence the rush of posts)
 
Last edited:
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  • #2
"used to predict x if z is unknown" doesn't make sense. You have to know at least one.

For correlation I wouldn't estimate the regression; just the simple correlation between x and z.

Independent of 1 and 2, what do you think the answer to 3 might be?
 
  • #3
it can't be as simple as:
[tex]\mu=0[/tex]
and
[tex]\sigma^2=1[/tex]

can it?
 
  • #4
damn just realized:

part b should be:
"calculate the regression used to predict x if z is known"

sorry
 
  • #5
dont worry finally got it:
[tex]\mu=59.5[/tex]
and
[tex]\sigma=12.5[/tex]

thanks
 

Related to Calc Correlation Coefficient & Regression Equation for Normal Variable X

What is the Calc Correlation Coefficient?

The Calc Correlation Coefficient, also known as Pearson's correlation coefficient, is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables. It ranges from -1 to 1, with values closer to 1 indicating a strong positive correlation, values closer to -1 indicating a strong negative correlation, and values close to 0 indicating little to no correlation.

How is the Calc Correlation Coefficient calculated?

The Calc Correlation Coefficient is calculated by dividing the covariance of the two variables by the product of their standard deviations. The formula is: r = (Σ(x-x̄)(y-ȳ))/(√(Σ(x-x̄)^2)√(Σ(y-ȳ)^2)), where x and y are the two variables, x̄ and ȳ are the means of x and y, and Σ represents the sum of all the values. This calculation can also be done using statistical software or calculators.

What is the Regression Equation for Normal Variable X?

The Regression Equation for Normal Variable X is a mathematical formula that represents the relationship between two variables. It is used to predict the value of the dependent variable based on the value of the independent variable. In this case, the equation will be in the form of y = mx + b, where y is the predicted value, m is the slope of the regression line, x is the value of the independent variable, and b is the y-intercept.

What is the purpose of calculating the Calc Correlation Coefficient and Regression Equation?

The purpose of calculating the Calc Correlation Coefficient and Regression Equation is to determine the strength and direction of the relationship between two variables and to use that information to make predictions. This can be useful in various fields such as science, economics, and social sciences, where understanding and predicting relationships between variables is important.

What are the limitations of using the Calc Correlation Coefficient and Regression Equation?

While the Calc Correlation Coefficient and Regression Equation can provide valuable insights, they have some limitations. They only measure linear relationships and cannot capture non-linear relationships. Additionally, correlation does not imply causation, so even if a strong correlation is found, it does not necessarily mean that one variable causes the other. It is important to consider other factors and conduct further research before making any conclusions.

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