The coefficient of determination: a. cannot be negative b. is the square root of the coefficient of correlation c. can be negati
1 answer:
Answer:
Correct statement: (a) and (b).
Step-by-step explanation:
R-squared is a statistical quantity that measures, just how near the values are to the fitted regression line. It is also known as the coefficient of determination.
The coefficient of determination is the squared value of the correlation coefficient.
That is,
The correlation coefficient between two variables is the measure of the strength and direction of the relationship between two variables.
Its values ranges from -1.00 to +1.00.
Although <em>r</em> can be negative but R² is always positive, since square of a negative number is also positive.
So the correct statements are:
a. cannot be negative
b. is the square root of the coefficient of correlation
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Answer:
The graph that best fits this model of profit and price per unit is attached to this solution.
Step-by-step explanation:
A couple of graphs are missing from the question. According to the full question as obtained online, there are a number of graph options, and we are expected to pick the best option. I would not add all the other graphs to limit the chances of deletion due to violating community guidelines.
The function is to model the profits of a new pet-monitoring system, so, if the profits are y and x is the price per unit, y = f(x)
The profits vary as the price per unit varies, there is no profit made until the price reaches
$95 per unit, a maximum profit at a price of $140 per unit, and no profit at a price over $185 per unit.
This description of the curve shows that y is negative at values of x less than $95, then the profits increase when x varies between $95 and $140. The profits reach a maximum at $140 and then the profits start to decline as x increases again, the profits go to 0 at x = $185.
This description shows that the function is a quadratic function with a graph that is n-shaped, peaking at x=185 and the graph crosses the x-axis at x=95 and x=185.
The most fitting graph for this model is attached to this solution provided.
From the graph, all the descriptions above are evident.
Hope this Helps!!!
