Answer:
Step-by-step explanation:
Correlation occurs when we can observe a trend between the response/dependent variable (y) and the explanatory/independent variable (x).
When comparing two sets of data, we may observe and use correlation to determine whether or not the data presented if significant or not and whether or not it supports our hypothesis. We may want to see if this is just a fluke and whether or not the trend causes causation.
An example of this would be if we thought that the weight of female mice determines how many kids they have in a month.. Let's say that mice that weigh up to one ounce have 6 baby mice per month.
Let's say that the x variable is the weight which is between 0.25 oz and 1.25 oz and the y-variable is the amount of kids between each month. If another laboratory conducts the same study with the same type of mice with the same weights as us, we would need to determine if there is correlation and if there is causation and try to use this information to determine if both sets of data are significant to our hypothesis.
Answer:
Definitions:
The third quartile, denoted by Q3 , is the median of the upper half of the data set. This means that about 75% of the numbers in the data set lie below Q3 and about 25% lie above Q3;
The upper half of a data set is the set of all values that are to the right of the median value when the data has been put into increasing order;
First, we verify data in increasing order:
21, 24, 25, 28, 29, 35, 37, 38, 42 ( okay);
The median is 29;
Therefore, the upper half of the data is {35, 37, 39, 42};
Q3 = ( 37 + 39 ) / 2 = 76 / 2 = 38;
Step-by-step explanation:
Answer:
Kindly check explanation
Step-by-step explanation:
Given the data:
Temperature (degrees Celsius) : 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Percent heat loss from beak : 35 36 38 28 41 43 55 46 39 54 45 58 60 56 62 67
Using an online regression calculator ; the regression equation obtained is :
ŷ = 2.0927X + 0.6029
X = independent variable
Y = predicted variable
2.0927 = slope
0.6029 = intercept
B.) temperature = 25
ŷ = 2.0927(25) + 0.6029
= 52.9204
C.) the explained variance is the value of the coefficient of determination (R²) which is the square of the correlation Coefficient
0.8785² = 0.7718
D.) the correlation Coefficient r is 0.8785 using the Coefficient of regression calculator
Answer:
(-5)+(-4)= -9
(-3)+(+1)= -2
(-2)+(-7)= -9
Step-by-step explanation:
Answer:
You need to link a diagram
Step-by-step explanation:
Try taking a screenshot or picture and attach it to the question so we can actually see the diagram
