Answer:
y-intercept: 5/3
Step-by-step explanation:
Our equation: y = mx + b
Given equation: y = -8/3 + 5/3
=> Now, let's compare.
=> y = mx + b = y = -8/3 + 5/3
=> we can tell that the y-intercept is b, which is 5/3.
Hoped this helped.
We get tne n-th term with: tn=1/3tn-1
We know that the first term is t1=81, so 81=1/3t*1-1
81=1/3t-1
82=1/3t
82*3t=1
3t=1/82
t=1/82*3=1/246
The second term is: 1/
2*(1/246)*2)-1
We get the third term by replacing n with 3 and so on...
Answer:
6xy^3 +12xy^2 z
Step-by-step explanation:
3xy^2 (2y+4z)
Multiply
3xy^2 * 2y + 3xy^2 *4z
common factors
3xy^3 +3xy^2 *4z
Multiply
6xy^3 +12xy^2 z
According to the line of best fit, the value of time when the temperature reach 100°c, the boiling point of water is 5.
<h3>What is linear regression?</h3>
Linear regression is a type of regression which is used to model the statement in which the growth or decay initially with constant rate, and then slow down with respect to time.
The table shows the temperature of an amount of water set on a stove to boil, recorded every half minute. a 2-row table with 10 columns.
- Time (minutes X) 0, 0.5,1.0, 1.5, 2.0,2.5,3.0, 3.5, 4, 4.5.
- Temperature (° Celsius Y) 75, 79, 83, 86, 89, 91, 93, 94, 95, 95.5.
The sum of time is 22.5 and the sum of temperature value is 880.5. In this table,
- The mean of time value, 2.25.
- The mean of temperature value 88.05
- Sum of squares 20.625
- Sum of products 93.625
The regression equation for this data can be given as,
Put the value of temperature (y) 100 in this equation.
Hence, according to the line of best fit, the value of time when the temperature reach 100°c, the boiling point of water is 5.
Learn more about the regression here;
brainly.com/question/25226042
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
