1. You conducted a study that involved a task to estimate the reservation price of potential customers for a product you produce. A reserve price is a maximum price a customer is willing to pay, e.g., perhaps a customer is willing to purchase the product at $4 but is unwilling to purchase at $4.01; $4 is that customers reservation price. You also collected demographic information and are interested in relationships between reservation price and demographic variables, such as household size (total number of people living in the customers household). Below is the regression coefficient table for a model predicting reservation price by household size. Interpret both coefficients then evaluate the hypothesis tests. What is the null hypothesis and should you reject or not reject the null hypothesis.

2. Call:

   lm(formula = Reservation.Price ~ household.size, data = myData)

   Residuals:

   Min 1Q Median 3Q Max

   -210.029 -46.896 4.891 40.316 117.664

   Coefficients:

   Estimate Std. Error t value Pr(>|t|)

   (Intercept) 6.37 1.23 5.17 < .001 ***

   household.size 0.24 0.09 2.67 .03 *

   ---

   Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1

   Residual standard error: 59.88 on 124 degrees of freedom

   Multiple R-squared: 0.2651, Adjusted R-squared: 0.2592

   F-statistic: 6.74 on 1 and 124 DF, p-value: .03

3. Below is the data for one customer. Calculate the expected reservation price of the customer, according to the regression results in #2. Then calculate the residual for that customer according to the same regression results.

ID Reservation.Price Household.Size

3546 7.25 3