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Answer:
See explaination
Step-by-step explanation:
See attachment for diagram
The r value is 0.373 (low). This implies a weak correlation between the dependent and independent variables for this sample.
The overall p- value for the regression model is 0.0017. This implies that at least one of the two independent variables (x1 or x2) in the model is significant predictor of the dependent variable y.
p- values for the both "Fact" and "Star" are < 0.05. This means both the independent variables are significant predictors of the "Rating" at 95% confidence level. The variable "Fact" is significant at 99% level of confidence also. This means the rating viewers award to a movie depends upon both the storyline (fact or Fiction) and the presence or absence of stars.
Expected rating for a fact based movie with no stars = 1.7991(1) + 1.2586(0) + 12.5685 = 14.37
Expected rating for a fiction based movie with a star = 1.7991(0) + 1.2586(1) + 12.5685 = 13.83
So, one may expect a fact based movie without any stars to get better ratings than a fiction based movie with one star.
Answer: 709
Step-by-step explanation:
The formulas we use to find the required sample size :-
1.
, where = population standard deviation,
E = Margin of error .
z* = Critical value
2. , where p= prior estimate of population proportion.
3. If prior estimate of population proportion is unavailable , then we take p= 0.5 and the formula becomes
Given : Margin of error : E= 3% =0.03
Critical value for 95% confidence interval = z*= 1.96
A study conducted several years ago revealed that the percent of junior executives leaving within three years was 21%.
i.e. p=0.21
Then by formula 2., the required sample size will be :
[Round to the next integer.]
Hence, the required sample size of junior executives should be studied = 709