01Suppose the regression shows that there is a negative association between two variables. If the coefficient of determination ( R2 ) is 92.3% then the correlation between them is( in two decimal places )

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02The best predictor for a response variable Y in a multiple regression model is

Select one:

a. the predictor that has highest correlation with Y

b. the predictor that has highest correlation with other predictors

c. the predictor that has lowest correlation with other predictors

d. the predictor that has lowest correlation with Y

03Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.

The regression equation is

GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV

Predictor Coef SE Coef T P

Constant 1.8015 0.1842 9.780.000

Math SAT 0.00104420.0002500 4.180.000

Verbal SAT0.00141820.0002398 5.910.000

TV -0.0147080.003269 -4.500.000

S = 0.366780R-Sq =19.5%R-Sq(adj) = 19.0%

Predict the GPA of a student at this university with a Math SAT score of 600, a Verbal SAT score of 580, and who watches 2 hours of television in a typical week. Round to three decimal places.

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04A production company manager identify three items namely Item A, Item B and Item C are major contributors to the profit of the company. He found the p-values of each of the item from the multiple regression ANOVA table as follows.

Item A0.035

Item B0.0305

Item C0.03005

The most significant contributor item of the company profit?

Select one:

a. Item C

b. All Item

c. Item A

d. Item B

e. Non of the item

05If the regression analysis was done between two variable with 44 cases, what would be the residual (error) degrees of freedom?

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06 Data were collected on the age (in years) and price (in thousands of dollars) of a random sample of 25 used Hyundai Elantras. A scatterplot of the data (with regression line) and computer output from a regression analysis are provided.

Use three decimal places when reporting the results from any calculations, unless otherwise specified.

The regression equation is Price = 15.3 - 1.71 Age

PredictorCoefSE Coef TP

Constant15.29120.5840 26.180.000

Age -1.71260.1264 -13.550.000

S = 1.37179R-Sq = 88.9%R-Sq(adj) = 88.4%

Interpretation of coefficient of determination is (a) -------------- of variation in (b) -------------- is explained by (c) -------------

select for a,b and c from (price, 88.9%, constant, age, 88.4% )

07A quantitatively savvy, young couple is interested in purchasing a home in southern Sydney. They collected data on 48 houses that had recently sold in the area. They want to predict the selling price of homes (in thousands of dollars) based on the size of the home (in square feet).

The regression equation is House Price (in thousands) = 17.1 + 0.0643 Size (sq. ft.)

PredictorCoefSE Coef TP

Constant17.0624.59 0.690.491

Size (sq. ft.)0.064270.01224 5.250.000

S = 48.5733R-Sq = 37.5%R-Sq(adj) = 36.1%

Use the computer output to test the slope, at the 5% level, to determine whether size (in square feet) is an effective predictor of the selling price of recently sold homes. Include all details of the test.

What are the null ( HO) and alternative hypotheses ( HA)?

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What is the t-test statistics?

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Degree of freedom for this t-test is

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Final conclusion of this test is

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08While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.

The regression equation is

Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)

PredictorCoefSE Coef T P

Constant6.6817.64 0.38 0.715

Fat (g)9.6091.452 6.62 0.000

Carbs (g)3.43500.3155 10.89 0.000

Protein (g)4.4182.231 1.98 0.083

S = 13.2293R-Sq = 98.8%R-Sq(adj) = 98.4%

Analysis of Variance

Source DF SS MS F P

Regression 3 116867 38956 222.58 0.000

Residual Error_ 1400 175

Total 11 118267

a)Interpret the slope of Fat in context.

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b)How many drinks were used in this sample?

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d) Which predictors are significant at the 5% level?

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c) Degrees of freedom of residual error is

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