Answer:
Options are missing.
The options for the above question are:
TS.1: Linear in parameters.
TS.2:No perfect collinearity
TS.3: Zero conditional mean.
TS.4: Homoskedasticity.
TS.5: No serial correlation
TS.6: Normality.
Hence the correct answer is TS1 to TS 5
Step-by-step explanation:
Assumptions TS 1 to TS 5 are the minimum set of assumptions needed to for the OLS estimates to be the best linear unbiased estimators conditional on explanatory variables for all time periods.
The assumptions of Normality is not needed for the estimators to show the BLUE property
Answer:
It will cost Mr. Smith $68 to make his suit.
Step-by-step explanation:
Since the charge $12 per hour, and it takes 4 hours to make the suit, then we need to multiply 12 by 4.
12 × 4 = 48
Now we need to add the flat rate since that is the initial charge for service.
48 + 20 = 68
This means it will cost Mr. Smith a total of $68 for his suit.
Answer:
5
Hope it helps
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Thank you
Answer:
Step-by-step explanation:
Hello!
a.
Your study variable is X: "daily income of a parking garage"
The parameter to estimate is the population mean (μ) of the daily income of the parking garage. Since the population mean is a parameter of the normal distribution you need your study variable to be normally distributed to study it. If your study variable hasn't the required distribution, since you have a big enough sample, apply the Central Limit Theorem to approximate the sample mean distribution to normal.
So the assumption you need to make is that X[bar]≈N(μ;δ²/n)
b.
The formula for the Confidence Interval is:
X[bar]±
*(S/√n)
I don't have the value of the population standard deviation, so I'll use the approximation with the sample standard deviation.
Sample
n=44
X[bar]= $126
S= $15

X[bar]±
*(S/√n)
[126±1.64*(15/√44)]
[122.29;129.71]
c.
With a confidence level of 90%, you can expect the interval [122.29;129.71] will contain the true mean of the daily income of the parking garage.
d.
90% of confidence means that if you were to take 100 samples and calculate a confidence interval for the population mean of the daily income of the parking garages, you'd expect 90 of those intervals to contain the true value of the parameter.
e.
If I were to make a hypothesis test to see if the consultant was right, using the complemental level of significance, since the interval doesn't contain the supposed value of $130, I would reject the null hypothesis and conclude that he wasn't right.
H₀: μ = 130
H₁: μ ≠ 130
α: 0.10
[122.29;129.71]
Interval doesn't include 130 ⇒ to Reject the null hypothesis.
I hope this helps!
Divide 28 by 4 which is 7 and then times that number by 9 so 63 windows im pretty sure