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Zanzabum
3 years ago
6

The similarities and differences between correlation and regression Correlation and regression are two closely related topics in

statistics. For each of the following statements, indicate whether the statement is true of correlation, true of regression, true of both correlation and regression, or true of neither correlation nor regression. Regression Correlation Both Correlation and Regression Neither Correlation nor Regression Can tell you whether one variable (such as smoking) causes another (such as cancer) Can be influenced by extreme values on one or both variables Can tell you the degree to which a linear relationship exists between two variables?
Mathematics
1 answer:
Anna [14]3 years ago
4 0

Answer:

Both variables Can tell you the degree to which a linear relationship exists between two variables.

Step-by-step explanation:

Correlation and regression are two statistical concepts that are closely related and complement each other. They are powerful tools used in modern-day machine learning and artificial intelligence.

Correlation evaluates the linear relationship between two variables, using regression as a tool to assess the degree at which a dependant variable changes ( either increasing or decreasing ) based on the value of an independent variable.

The correlation coefficient is the degree of the linear relationship between two variables, ranging from zero ( for non-linear) to one ( for highly linear).

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4 years ago
When Ryan is serving at a restaurant, there is a 0.75 probability that each party will order drinks with their meal. During one
Triss [41]

Answer:

0.82 = 82% probability that at least one party will not order drinks

Step-by-step explanation:

For each party, there are only two possible outcomes. Either they will order drinks with their meal, or they will not. The probability of a party ordering drinks with their meal is independent of other parties. So the binomial probability distribution is used to solve this question.

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}

In which C_{n,x} is the number of different combinations of x objects from a set of n elements, given by the following formula.

C_{n,x} = \frac{n!}{x!(n-x)!}

And p is the probability of X happening.

When Ryan is serving at a restaurant, there is a 0.75 probability that each party will order drinks with their meal.

This means that p = 0.75

During one hour, Ryan served 6 parties. Assuming that each party is equally likely to order drinks, what is the probability that at least one party will not order drinks?

6 parties, so n = 6.

Either all parties will order drinks, or at least one will not. The sum of the probabilities of these events is decimal 1. So

P(X = 6) + P(X < 6) = 1

We want P(X < 6). So

P(X < 6) = 1 - P(X = 6)

In which

P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}

P(X = 6) = C_{6,6}.(0.75)^{6}.(0.25)^{0} = 0.18

P(X < 6) = 1 - P(X = 6) = 1 - 0.18 = 0.82

0.82 = 82% probability that at least one party will not order drinks

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3 years ago
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it would be y = -12/5. Hes the work. hope this helps!





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Answer:

It is the Coefficient

Step-by-step explanation:

6 0
3 years ago
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