Describe type I and type II errors for a hypothesis test of the indicated claim. A shoe store claims that at least \( 30 \% \) of its new customers will return to buy their next pair of shoes. Describe the type I error. Choose the correct answer below. A. A type I error will occur when the actual proportion of new customers who return to buy their next pair of shoes is no more than 0.30 , but you reject \( H_{0}: p \leq 0.30 \). A type I error will occur when the actual proportion of new customers who return to buy their next pair of shoes is at least 0.30, but you reject \( H_{0}: p \geq 0.30 \). C. A type I error will occur when the actual proportion of new customers who return to buy their next pair of shoes is no more than 0.30 , but you fail to reject \( H_{0}: p \leq 0.30 \). D. A type I error will occur when the actual proportion of new customers who return to buy their next pair of shoes is at least 0.30 , but you fail to reject \( H_{0}: p \geq 0.30 \). Describe the type II error. Choose the correct answer below.

How do you identify the relationship between two variables in a scatter plot?

A regression was run to determine if there is a relationship between hours of TV watched per day \( (\mathrm{x}) \) and number of situps a person can do \( (y) \). The results of the regression were: \( \begin{array}{l}\mathrm{y}=\mathrm{ax}+\mathrm{h} \\ \mathrm{a}=2.389 \\ \mathrm{~b}=37.535 \\ \mathrm{r}^{2}=0.412164 \\ \mathrm{r}=-0.642\end{array} \) Use this to predict the number of situps a person who watches 0.5 hours of TV can do (ta one decimal place)

Baseball regression line prediction: Suppose the regression line for the number of runs scored in a season, \( y \), is given by \( \hat{y}=-730+4300 x \), where \( x \) is the team's batting average. a. For a tearn with a batting average of 0.235 , find the expected number of runs scored in a season. Round your answer to the nearest whole number. b. If we can expect the number of runs scored in a scason is 480 , then what is the assumed team's batting average? Round your onswer to three deeima! places.

You run a regression analysis on a bivariate set of data ( \( n=88 \) ), You obtain the regression equation \[ y=0.231 x-8.928 \] with a correlation confficient of \( r=0.38 \) (which is significant at \( \alpha=0.01 \) ), You want to predict what value (on average) for the explanatory variable will give you a value of 90 on the response variable. What is the predicted explanatory value? \( x=\square \)

21. Una empresa de investigación de mercado emplea una muestra de individuos para calificar el potencial de compra de un determinado producto antes y después de que los individuos vean un comercial de televisión acerca del mismo. La calificación del potencial de compra se hace con una escala del 0 al 10 , con los valores más altos indicando un mayor potencial de compra. En la hipótesis nula se establece que la media de las calificaciones de "después" será menor o igual a la media de las calificaciones "antes". El rechazo de esta hipótesis indica que el comercial me- jöra la media de la calificación al potencial de compra. Use \( \alpha=0.05 \) y los datos de la tabla si- guiente para probar esta hipótesis y haga un comentario sobre la utilidad del comercial. Calificación al potencial de compra Individuos Después 1