The correlation coefficient of the health research institute data measures the relationship between the age and the years of the smokers
The correlation coefficient is 0.53
<h3>How to calculate the correlation coefficient</h3>
The correlation coefficient (r) is calculated as:
![r = \frac{n(\sum xy) - \sum x \sum y}{\sqrt{[n \sum x^2 - (\sum x)^2][n\sum y^2 - (\sum y)^2}}](https://tex.z-dn.net/?f=r%20%3D%20%5Cfrac%7Bn%28%5Csum%20xy%29%20-%20%5Csum%20x%20%5Csum%20y%7D%7B%5Csqrt%7B%5Bn%20%5Csum%20x%5E2%20-%20%28%5Csum%20x%29%5E2%5D%5Bn%5Csum%20y%5E2%20-%20%28%5Csum%20y%29%5E2%7D%7D)
Using the given parameters, we have:
![r = \frac{20 *8249 - 1257* 116}{\sqrt{[20 * 98823 - 1257^2][20 * 836 - 116^2}}](https://tex.z-dn.net/?f=r%20%3D%20%5Cfrac%7B20%20%2A8249%20-%201257%2A%20116%7D%7B%5Csqrt%7B%5B20%20%2A%2098823%20-%201257%5E2%5D%5B20%20%2A%20836%20-%20116%5E2%7D%7D)
Evaluate the exponents
![r = \frac{20 *8249 - 1257* 116}{\sqrt{[20 * 98823 - 1580049][20 * 836 - 13456}}](https://tex.z-dn.net/?f=r%20%3D%20%5Cfrac%7B20%20%2A8249%20-%201257%2A%20116%7D%7B%5Csqrt%7B%5B20%20%2A%2098823%20-%201580049%5D%5B20%20%2A%20836%20-%2013456%7D%7D)
Evaluate the products
![r = \frac{164980 - 145812}{\sqrt{[1976460 - 1580049][16720 - 13456}}](https://tex.z-dn.net/?f=r%20%3D%20%5Cfrac%7B164980%20-%20145812%7D%7B%5Csqrt%7B%5B1976460%20-%201580049%5D%5B16720%20-%2013456%7D%7D)
Evaluate the differences

Evaluate the product

Evaluate the root

Evaluate the quotient

Hence, the correlation coefficient is 0.53
Read more about correlation coefficient at:
brainly.com/question/1564293
Answer:
d) Squared differences between actual and predicted Y values.
Step-by-step explanation:
Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.
For example,
suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.
It minimize the squared difference between actual and predicted value.
Answer: 23
Step-by-step explanation:
<em>Hey</em><em>!</em><em>!</em><em>!</em>
<em>here</em><em>'s</em><em> </em><em>your</em><em> </em><em>answer</em>
<em>X+</em><em>1</em><em>2</em><em>8</em><em>=</em><em>1</em><em>8</em><em>0</em><em>(</em><em> </em><em>sum</em><em> </em><em>of</em><em> </em><em>angle</em><em> </em><em>in</em><em> </em><em>straight</em><em> </em><em>line</em><em>)</em>
<em>or</em><em>,</em><em>X=</em><em>1</em><em>8</em><em>0</em><em>-</em><em>1</em><em>2</em><em>8</em>
<em>X=</em><em>5</em><em>2</em><em> </em><em>degree</em><em>.</em>
<em>So</em><em> </em><em>the</em><em> </em><em>value</em><em> </em><em>of</em><em> </em><em>X </em><em>is</em><em> </em><em>5</em><em>2</em><em> </em><em>degree</em><em>.</em>
<em>Hope</em><em> </em><em>it</em><em> </em><em>helps</em><em>.</em><em>.</em><em>.</em>
<em>Good</em><em> </em><em>luck</em><em> </em><em>on</em><em> </em><em>your</em><em> </em><em>assignment</em>
Hello!
Your answer will be $150! Hope this helps! :D