1. B
2. B
3. C
4. C
5. D
6. A
7. B
The p-value obtained is 0.001. At p<0.05, the p-value obtained of 0.001 is statistically significant. We reject the null hypothesis. We can therefore conclude that; the hotel occupancies are not the same
When the box office gross increases by 1 million, the video unit sales is expected to increase by 4,333 units.
Sales (Units in thousands) = 76.5351 + 4.3331 (Box office gross in $ millions)
If box office gross is $30 million;
Sales (Units in thousands) = 76.5351 + 4.3331(30) = 206.5281
Therefore the video unit sales will be 206,528 units
The assumptions appear to be violated because there is no distinctive linear pattern.
H0: β1 = 0
There is a linear relationship between box office gross and home video unit sales.
The p value from the regression results is 2.13E-09. At p<0.05, the p-value obtained is statistically significant. We reject the null hypothesis. We can therefore conclude that; there is a linear relationship between box office gross and home video unit sales.
No we cannot conclude that the estimate for the true average change in video unit sales as a result of a one million dollars increase in box office is 6 thousand units. A one million dollars increase in box office will cause the video unit sales to increase by 4,333 units (4.3331*1,000)
Sales (Units in thousands) = 76.5351 + 4.3331 (Box office gross in $ millions)
15.
The estimate of the unit change in the mean GPA per unit change in number of course units, holding
SAT scores constant is 0.247270. This effect statistically significant at a 5% level. This is because the p-value from the regression results is 0.0290 which is less than 0.05.
16. The adjusted R square is 0.732. It implies that 73.2% of variations in the GPA of students can be explained by number of course units (credits) and total SAT scores of each student.
17. The head of department should conduct a t test. The value of the t statistic from the table is -3.945 for H0: β1 = 0 and 1.425 H0: β2 = 0 and the p-values are 0.0290 and 0.2494 respectively.
The p-values from the regression results are 0.0290 and 0.2494 respectively. At p<0.05, the p-values obtained are statistically significant. We reject the null hypothesis. We can therefore conclude that; there is a linear relationship between the GPA of a student and the two explanatory variables which are, the number of course units and the SAT scores. G = 4.593897 – 0.247270U + 0.001443S
Where;
G is the GPA of a student
U is the number of course units (credits)
S is the SAT scores.
The demand for building materials will increase by $ 500 per capita.
$800 per capita
Y = 10 + 5X1
For Los Angeles we include X2 which is equal to zero. Therefore; Y = 10 + 5X1 + 8*0 = Y= 10 + 5X1
Y = 10 + 5*8 = 50
The demand will be 50*100 = $ 5000 per capita
Y = 18+ 5X1
For San Francisco we include X2 which is equal to 1. Therefore; Y = 10 + 5X1 + 8*1 = Y= 10 + 5X1 + 8
Y = 18+5*10 = 68
The demand will be 68*100 = $ 6800 per capita
