A quadrilateral with only 2 right angles could be a trapezoid so Balavan is wrong. Denna is right because if 3 angles are right angles the the fourth angle must also be a right angle and has to be a square or rectangle.
We will traduce the sentences into equations.
Let x be the number of hours in the first job, and y be the number of hours in the second job.
Then the equations are:

The above system has many solution, we can select for example:
x = 1, y = 16. So we can work one hour at the first job and 16 hour at the second job.
Answer:
D. The difference of the means is not significant because the re-randomizations show that it is within the range of what could happen by chance.
Step-by-step explanation:
The treatment group using System A reported a mean of 18.5 lost bags per day. The treatment group using System B reported a mean of 16.6 lost bags per day.
The best conclusion that can be made is - The difference of the means is not significant because the re-randomizations show that it is within the range of what could happen by chance.
As we know, in statistics, nothing happens by chance. So, this option is correct.
Answer:
Residual = -2
The negative residual value indicates that the data point lies below the regression line.
Step-by-step explanation:
We are given a linear regression model that relates daily high temperature, in degrees Fahrenheit and number of lemonade cups sold.

Where y is the number of cups sold and x is the daily temperature in Fahrenheit.
Residual value:
A residual value basically shows the position of a data point with respect to the regression line.
A residual value of 0 is desired which means that the regression line best fits the data.
The Residual value is calculated by
Residual = Observed value - Predicted value
The predicted value of number of lemonade cups is obtained as

So the predicted value of number of lemonade cups is 23 and the observed value is 21 so the residual value is
Residual = Observed value - Predicted value
Residual = 21 - 23
Residual = -2
The negative residual value indicates that the data point lies below the regression line.