Answer:
The least squares method results in values of the y-intercept and the slope, that minimizes the sum of the squared deviations between the observed (actual) value and the fitted value.
Step-by-step explanation:
The method of least squares works under these assumptions
- The best fit for a data collection is a function (sometimes called curve).
- This function, is such that allows the minimal sum of difference between each observation and the expected value.
- The expected values are calculated using the fitting function.
- The difference between the observation, and the expecte value is know as least square error.
<span>m∠CED = </span><span>1/2(m∠AOB + </span><span><span>m∠COD)</span> = 1/2(90° + 16°) = 1/2(106°) = 53°</span>
Answer:
bottom right
Step-by-step explanation:
Answer:
242
Step-by-step explanation:
sorry this is like kinda late, but there ya go
To calculate expected value,
We can add up the probability of all possible events.
The 2 possible events are either winning $50 or losing the prize, which is $ - 50
Expected value =
50 x 40 % + (-50) x 60%
= $-10
So the answer is $-10
