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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Answer:
4x+10
Step-by-step explanation:
Answer: 2185
Step-by-step explanation:
Let p be the proportion of visitors are campers.
Given : The Tennessee Tourism Institute (TTI) plans to sample information center visitors entering the state to learn the fraction of visitors who plan to camp in the state.
The prior proportion of visitors are campers : p=0.35
Allowable error : E= 2%= 0.02
We know that the z-value for 95% confidence = 
Then by Central Limit Theorem , the required sample size would be :
Simply , we get
[Rounded to the next whole number.]
Hence, the smallest sample size to estimate the population proportion of campers =2185
Answer:
-6
Step-by-step explanation:
