1) The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant.

Price in Dollars 103 111 129 178 199

Number of Bids 10 13 14 15 16

Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.

Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.

Step 3 of 6: Determine the value of the dependent variable y at x=0.

Step 4 of 6: According to the estimated linear model, if the value of the independent variable is increased by one unit, then the change in the dependent variable y is given by?

Step 5 of 6: Determine if the statement "All points predicted by the linear model fall on the same line" is true or false.

Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places.

2) Number of Absences 1 2 3 4 12

Grade 95 73 71 59 52

Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.

Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.

Step 3 of 6: Determine the value of the dependent variable y at x=0.

Step 4 of 6: According to the estimated linear model, if the value of the independent variable is increased by one unit, then the change in the dependent variable y is given by?

Step 5 of 6: Determine if the statement "All points predicted by the linear model fall on the same line" is true or false.

Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places.

3) A study of bone density on 5 random women at a hospital produced the following results.

Age 39 45 45 49 67

Bone Density 341 333 320 318 318

Step 1 of 5: Calculate the sum of squared errors (SSE). Use the values b0=358.0215 and b1=0.6535 for the calculations. Round your answer to three decimal places.

Step 2 of 5: Calculate the estimated variance of errors, s2e. Round your answer to three decimal places.

Step 3 of 5: Calculate the estimated variance of slope, s2b1. Round your answer to three decimal places.

Step 4 of 5: Construct the 90% confidence interval for the slope. Round your answers to three decimal places.

Step 5 of 5: Construct the 80% confidence interval for the slope. Round your answers to three decimal places.

The data in the table is the number of absences for 7 students and their corresponding grade.

Number of Absences 1 2 4 4 4 6 7

Grade 3.2 2.3 2.2 2 1.7 1.7 1.6

Step 1 of 5: Calculate the sum of squared errors (SSE). Use the values b0=3.0232 and b1=0.2308 for the calculations. Round your answer to three decimal places.

Step 2 of 5: Calculate the estimated variance of errors, s2e. Round your answer to three decimal places.

Step 3 of 5: Calculate the estimated variance of slope, s2b1. Round your answer to three decimal places.

Step 4 of 5: Construct the 98% confidence interval for the slope. Round your answers to three decimal places.

Step 5 of 5: Construct the 90% confidence interval for the slope. Round your answers to three decimal places.

The following table was generated from the sample data of 10 newborn babies regarding the weight of the mother at birth, the weight of the father at birth, and the weight of the baby at birth. The dependent variable is the weight of the baby, the first independent variable (x1) is the mother's weight, and the second independent variable (x2) is the father's weight.

Coefficients Standard Error t-Stat p-value

Intercept 14.051813 1.785927 7.868078 0.000223

Mother's Weight 0.129534 0.210911 0.614164 0.561656

Father's Weight 0.891192 0.162863 5.472049 0.001555

Step 1 of 2: Write the multiple regression equation for the computer output given. Round your answers to three decimal places. Y = ( ) + ( )x1 + ( )x2

Step 2 of 2: Indicate if any of the independent variables could be eliminated at the 0.05 level of significance.(a) x1 (b) x2 (c) Keep all variables

The following table was generated from the sample data of 10 junior high students regarding the average number of hours they are unsupervised per night, the average number of hours they play video games per night, and their final grades in their math class. The dependent variable is the final grade, the first independent variable (x1) is the number of hours unsupervised each night, and the second independent variable (x2) is the number of hours of video games each night.

Coefficients Standard Error t-Stat p-value

Intercept 68.602473 5.070046 13.530938 0.000003

Hours Unsupervised 1.582977 1.479825 1.069705 0.320245

Hours Playing Video Games 4.571089 0.893680 5.114908 0.001377

Step 1 of 2: Write the multiple regression equation for the computer output given. Round your answers to three decimal places.

Step 2 of 2: Indicate if any of the independent variables could be eliminated at the 0.05 level of significance.(a) x1 (b) x2 (c) Keep all variables