Exam 12: Simple Linear Regression

Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided -Refer to Exhibit 12-8. The mean square error (MSE) is

Exhibit 12-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. -Refer to Exhibit 12-5. The least squares estimate of the Y intercept is

In a regression analysis if SSE = 200 and SSR = 300, then the coefficient of determination is

Regression analysis was applied between demand for a product (Y) and the price of the product (X), and the following estimated regression equation was obtained. = 120 - 10 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to

Exhibit 12-1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. SSE = 6 SST = 16 -Refer to Exhibit 12-1. The coefficient of determination is

Exhibit 12-6 For the following data the value of SSE = 0.4130. -Refer to Exhibit 12-6. The slope of the regression equation is

In regression analysis, if the independent variable is measured in pounds, the dependent variable

A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation = 60 - 8X The above equation implies that an

Exhibit 12-3 You are given the following information about y and x. -Refer to Exhibit 12-3. The coefficient of determination equals

Exhibit 12-3 You are given the following information about y and x. -Refer to Exhibit 12-3. The least squares estimate of b₁ equals

Exhibit 12-1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. SSE = 6 SST = 16 -Refer to Exhibit 12-1. The coefficient of correlation is

The owner of a bakery wants to analyze the relationship between the expenditure of a customer and the customer's income. A sample of 5 customers is taken and the following information was obtained. The least squares estimated line is = 4.348 + 0.0826 X. a.Obtain a measure of how well the estimated regression line fits the data. b.You want to test to see if there is a significant relationship between expenditure and income at the 5% level of significance. Be sure to state the null and alternative hypotheses. c.Construct a 95% confidence interval estimate for the average expenditure for all customers with an income of $20,000. d.Construct a 95% confidence interval estimate for the expenditure of one customer whose income is $20,000.

Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. -Refer to Exhibit 12-10. The MSE is

In regression analysis, the variable that is being predicted is the

A regression analysis between sales (Y in $1000) and advertising (X in dollars) resulted in the following equation = 30,000 + 4 X The above equation implies that an

Below you are given a partial computer output based on a sample of 14 observations, relating an independent variable (x) and a dependent variable (y). a.Develop the estimated regression line. b.At $\alpha$ = 0.05, test for the significance of the slope. c.At $\alpha$ = 0.05, perform an F test. d.Determine the coefficient of determination. e. Determine the coefficient of correlation.

Correlation analysis is used to determine

Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. -Refer to Exhibit 12-10. The coefficient of correlation is

Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with "?".

Exhibit 12-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). -Refer to Exhibit 12-9. The sample correlation coefficient equals