Minimizing the sum of the squared deviations around the line is called Least square estimation.
It is given that the sum of squares is around the line.
Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.
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The answer and equation is 17 - 19 = -2
Answer:14 hrs and 25 mins
Step-by-step explanation:
The probability that a randomly selected runner has a time less than or equal to 3 hours and 20 minutes is 0.16 or 16%.
<h3>What is normally distributed data?</h3>
Normally distributed data is the distribution of probability which is symmetric about the mean.
The mean of the data is the average value of the given data.
The standard deviation of the data is the half of the difference of the highest value and mean of the data set.
The times of the runners in a marathon are normally distributed, with
- Mean of 3 hours and 50 minutes
- Standard deviation of 30 minutes.
Refere the probabiliity table attached below. The probability of Z being inside the 1 Standard daviation of mean is 0.84.
The probability of runner selected with time less than or equal to 3 hours and 20 minutes,

Thus, the probability that a randomly selected runner has a time less than or equal to 3 hours and 20 minutes is 0.16 or 16%.
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Answer: the hundreds place