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Male crickets chirp by rubbing their front wings together, and their chirping is temperature dependent. The table below shows the number of chirps per second for a cricket, recorded at 10 different temperatures. (a) Find the least-squares regression line relating the number of chirps to temperature. (Round all numerical values to four decimal places.) $y~?=$ (b) Do the data provide sufficient evidence to indicate that there is a linear relationship between number of chirps and temperature? (Test at the $?=0.05$ level of significance.) State the null and alternative hypotheses. $H_{0}:?=0versusH_{a}:?<0H_{0}:?=0versusH_{a}:??=0H_{0}:??=0versusH_{a}:?=0H_{0}:?=0versusH_{a}:?>0H_{0}:??0versusH_{a}:?>0?$ Find the test statistic. (Round your answer to three decimal places.) $t=$ Find the rejection region. (If the test is one-tailed, enter NONE for the unused region. Round your answers to three decimal places.) $t>$ $t?$ State your conclusion. $H_{0}$ is rejected. There is sufficient evidence to indicate that $y$ and $x$ are linearly related. $H_{0}$ is not rejected. There is sufficient evidence to indicate that $y$ and $x$ are linearly related. $H_{0}$ is rejected. There is insufficient evidence to indicate that $y$ and $x$ are linearly related. $H_{0}$ is not rejected. There is insufficient evidence to indicate that $y$ and $x$ are linearly related. (c) Calculate $r_{2}$. (Round your answer to three decimal places.) $r_{2}=$ What does this value tell you about the effectiveness of the linear regression analysis? $r_{2}$ is the proportion of the total variation in $y$ that is accounted for using the independent variable $x$ in a linear regression. A high value of $r_{2}$ indicates a more effective model. $r_{2}$ is the proportion of the total variation in $y$ that is unacoounted for using the independent variable $x$ in a linear regression. A high value of $r_{2}$ indicates a more effective model. $r_{2}$ is the proportion of the total variation in $x$ that is unaccounted for using the independent variable $y$ in a linear regression. A low value of $r_{2}$ indicates a more effective model. $r_{2}$ is the proportion of the total variation in $x$ that is accounted for using the independent variable $y$ in a linear regression. A low value of $r_{2}$ indicates a more effective model. You may need to use the appropriate appendix table or technology to answer this question.

R-code for regression fitting : > x<-c(30,21,31,28,28,22,19,26,19,27)> y<-c(21,15,20,19,17,15,13,18,16,17)> mod1<-lm(y~x)> summary(mod1)Call:lm(formul