Slope of -2, (1,5)
y = mx + b
slope(m) = -2
(1,5)...x = 1 and y = 5
now we sub and find b, the y int
5 = -2(1) + b
5 = -2 + b
5 + 2 = b
7 = b
so ur equation is : y = -2x + 7
The approximation method used to estimate a point between 2 given points is called linear interpolation. The approximation method used to estimate a point that does not lie between 2 given points is called linear extrapolation.A linear function has the form f(x) = mx + b. Its graph is a line that has slope m and y intercept at (0,b).
Answer:
720 liters
Step-by-step explanation:
In 2019 (a non-leap year)
January has 31 days
February has 28 days
March has 31 days
The Total number of days is 90 days
Each day shows a consumption rate of 8 liters / day
Total consumption = 8 * 90 = 720
Answer:
Step-by-step explanation:
From the picture attached,
a). Triangle in the figure is ΔBCF
b). Since, 
and 
are the parallel lines and m is a transversal line,
m∠FBC = m∠BFG [Alternate interior angles]
Since, and are the parallel lines and n is a transversal line,
m∠BCF = m∠CFE [Alternate interior angles]
By triangle sum theorem in ΔBCF
m∠FBC + m∠BCF + m∠BFC = 180°
From the properties given above,
m∠BFG + m∠CFE + m∠BFC = 180°
m∠GFE = 180°
Therefore, angle GFE is the straight angle that will be useful in proving that the sum of the measures of the interior angles of the triangle is 180°.
Answer:
The following variable is categorical.
Step-by-step explanation:
Consider the provided information.
Categorical variables: It take the values of categories or labels and position an entity in one of more classes.
Quantitative variables: It take numerical values and represent a calculation of some kind.
Now consider the given scenario.
Teenagers with a question that asks ‘‘Do you eat at least five servings a day of fruits and vegetables?"
Here we have two categories i.e fruits and vegetables.
Therefore, the following variable is categorical.
There could be a strong correlation between the proximity of the holiday season and the number of people who buy in the shopping centers.
It is known that when there are vacations people tend to frequent shopping centers more often than when they are busy with work or school.
Therefore, the proximity in the holiday season is related to the increase in the number of people who buy in the shopping centers.
This means that there is a strong correlation between both variables, since when one increases the other also does. This type of correlation is called positive. When, on the contrary, the increase of one variable causes the decrease of another variable, it is said that there is a negative correlation.
There are several coefficients that measure the degree of correlation (strong or weak), adapted to the nature of the data. The best known is the 'r' coefficient of Pearson correlation
A correlation is strong when the change in a variable x produces a significant change in a variable 'y'. In this case, the correlation coefficient r approaches | 1 |.
When the correlation between two variables is weak, the change of one causes a very slight and difficult to perceive change in the other variable. In this case, the correlation coefficient approaches zero
