Question 1: Hypothesis Testing (7 points, 1 point each)
The average performance of the statistical analysis course in UD is claimed to be 70 percent. To confirm the claim, the University took a samples of 30 students who took the course in the past and their marks are reported in the tables below (for respective group analysis):
Sample For Group E:
72,73,66,72,70,74,69,67,75,78,65,73,70,67,71,74,69,72,70,76,67,71,74,69,73,72,70,75,73,71
Using the data assigned to your group undertake a hypothesis testing (at 0.05 level of significance) to determine whether the claim is true or not clearly showing all the steps below:
State the appropriate null and alternative hypotheses
Specify the desired level of significance, and state the sample size.
Determine the appropriate test statistic (Z-distribution or t-distribution) and state why.
Determine the critical values, for a = 0.05
Compute the test statistic from your sample.
Is the test statistic calculated in the acceptance or the rejection region (show it graphically)?
Make a decision and interpret the results.
Question 2: Regression Analysis (13 points: 2 + 2+ 3 + 3 + 3)
Use the house data, (available on Moodle) to
answer the following questions:
Run a linear regression where the dependent
variable is the rent value, while all the other variables are independent. Your group should work on the data sample assigned on the
excel TAB (posted in the Moodle).
a.
What is the Adjusted R Square? Interpret your result. (2 marks)
b.
Interpret the significance of each independent variable in explaining the
dependent variable. If significant, tell at what % level of significance. (4
marks)
c.
What is the estimated regression line? Interpret
the estimated coefficients for each of the independent variables. (5
marks)
d.
What will be the estimated rent-value of
a house with the following attributes:
(2
marks)
SQFT
2300
AgeOfHouse
20
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