**Question 1 - True or False**

The phrase "linear regression" pertains to regression models with normal equations that can be expressed in matrix form using linear algebra to determine coefficient estimates.

**Question 2 -** Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. Let x be the independent variable and y the dependent variable. (Note that if x = 2, then y = 7 and so forth. yhat is the predicted value of the fitted equation.)

x 2 4 5 6 8

y 7 11 13 20 24

**a. yhat = 0.15 + 2.8x**

**b. yhat = 0.15x + 3.0**

**c. yhat = 0.15 + 3.0x**

**d. yhat = 2.8x**

**Question 3 - Choose the one alternative that best completes the statement or answers the question.**

Assume two independent random samples are available which provide sample proportions. For the first sample assume n1= 100 and x1= 39. For the second sample, assume n2= 100 and x2= 49. Test the null hypothesis that the population proportions are equal versus the alternative hypothesis that the proportions are not equal at the 90% confidence level. Frame the test statistic by subtracting the proportion for population 1 from that for population 2. Pick an appropriate z value, p-value and conclusion. Round your answer to the nearest thousandth.

**a. z-value = -1.425 p-value= 0.077 statistically significant**

**b. z-value = 1.425 p-value= 0.077 not statistically significant**

**c. z-value = 1.425 p-value= 0.077 statistically significant**

**d. z-value = -1.425 p-value= 0.1543 not statistically significant**

#### Top Answer

1) True 2) Option d) y ^ ... View the full answer

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(1) True, In linear regression models we often use the least square approach by expressing the regression equation in matrix... View the full answer