Answer:
See explanation below.
Step-by-step explanation:
When we want to fit a linear model given by:
Where y is a vector with the observations of the dependent variable, the parameters of the model and x the vector with the observations of the independent variable.
For this case this population regression function represent the conditional mean of the variable Y with values of X constant. And since is a population regression the parameters are not known, for this reason we use the sample data to obtain the sample regression in order to estimate the parameters of interest
We can use any method in order to estimate the parameters for example least squares minimizing the difference between the fitted and the real observations for the dependenet variable. After we find the estimators for the regression model then we have a model with the estimated parameters like this one:
With
And this model represent the sample regression function, and this equation shows to use the estimated relation between the dependent and the independent variable.
Answer:
≈2513.27
Step-by-step explanation:
Answer:
10.69% probability that all 12 flights were on time
Step-by-step explanation:
For each flight, there are only two possible outcomes. Either it was on time, or it was not. The probability of a flight being on time is independent of any other flight. So we use the binomial probability distribution to solve this question.
Binomial probability distribution
The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.
In which is the number of different combinations of x objects from a set of n elements, given by the following formula.
And p is the probability of X happening.
83% of recent flights have arrived on time.
This means that
A sample of 12 flights is studied.
This means that
Calculate the probability that all 12 flights were on time
This is P(X = 12).
10.69% probability that all 12 flights were on time
The answer is C I think but I am pretty sure it is right