Use a \( \chi^{2} \)-test to test the claim \( \sigma<44 \) at the \( \alpha=0.05 \) significance level using sample statistics \( s=40.3 \) and \( n=19 \). Assume the population is normally distributed. \( \begin{array}{l}H_{a}: \sigma>44 \\ \text { Identify the standardized test statistic. } \\ 15.10001 \text { (Round to three decimal places as needed.) } \\ \text { Identify the critical value(s). } \\ \square\end{array} \) (Round to three decimal places as needed. Use a comma to separate answers as needed.) Choose the correct conclusion below.

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.

2.50. Uz jautājumu "Cik minūtes nepieciešamas, lai skolēns no mājām atnāktu uz skolu?" 60 pirmo klašu skolēnu vecāki atbildēja šādi: \( 5 ; 4 ; 28 ; 28 ; 11 ; 17 ; 17 ; 10 ; 19 ; 27 ; 22 ; 9 ; 26 ; 5 ; 19 ; 21 ; 21 ; 21 ; 7 ; 2 ; 8 ; 14 ; 5 ; 20 ; 13 ; 24 ; 14 ; 15 \); \( 3 ; 29 ; 13 ; 13 ; 22 ; 17 ; 2 ; 26 ; 30 ; 14 ; 8 ; 1 ; 20 ; 15 ; 16 ; 21 ; 20 ; 18 ; 25 ; 8 ; 12 ; 24 ; 9 ; 27 ; 8 ; 9 ; 13 ; 2 ; \) \( 8 ; 19 ; 16 ; 23 \). a) Sakārto datus tabulā, grupējot tos no 1 līdz \( 5 ; \) no 6 līdz 10; no 11 līdz 15; no 16 līdz 20; 21 un vairāk minūtes! b) Nosaki datu modu! c) Aprēkini katras datu grupas relatīvo biežumu! d) Izmantojot tabulas datus, uzzīmē relatīvo biežumu stabinu diagrammu!

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)

Why is data collection appropriate for this two-sample proportion procedure? Because the order of applying the two treatments to each doctor was randomized in this matched pairs design. Because two separate simple random samples were taken-one from each population. Because the doctors were randomly allocated to the two treatments (aspirin and placebo).

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