Chapter 12: Problem 1
Give the y-intercept and slope for the line. $$y=2 x+1$$
Short Answer
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Chapter 12: Problem 1
Give the y-intercept and slope for the line. $$y=2 x+1$$
These are the key concepts you need to understand to accurately answer the question.
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Get started for freeIndependent and Dependent Variables Identify which of the two variables in Exercises \(10-14\) is the independent variable \(x\) and which is the dependent variable \(y .\) Number of hours spent studying and grade on a history test.
A Chemical Experiment A chemist measured SET the peak current generated (in microamperes) DS1205 when a solution containing a given amount of nickel (in parts per billion) is added to a buffer: $$\begin{array}{cc}\hline x=\mathrm{Ni}(\mathrm{ppb}) & y=\text { Peak } \text { Current }(\mathrm{mA}) \\\\\hline 19.1 & .095 \\\38.2 & .174 \\\57.3 & .256 \\\76.2 & .348 \\\95 & .429 \\\114 & .500 \\\131 & .580 \\\150 & .651 \\\170 & .722 \\\\\hline\end{array}$$ a. Use the data entry method for your calculator to calculate the preliminary sums of squares and crossproducts, \(S_{x x}, S_{y},\) and \(S_{x y}\) b. Calculate the least-squares regression line. c. Plot the points and the fitted line. Does the assumption of a linear relationship appear to be reasonable? d. Use the regression line to predict the peak current generated when a solution containing 100 ppb of nickel is added to the buffer. e. Construct the ANOVA table for the linear regression.
What diagnostic plot can you use to determine whether the incorrect model has been used? What should the plot look like if the correct model has been used?
Subjects in a sleep deprivation experiment were asked to solve a set of simple addition problems after having been deprived of sleep for a specified number of hours. The number of errors was recorded along with the number of hours without sleep. The results, along with the MINITAB output for a simple linear regression, are shown below. $$ \begin{aligned} &\begin{array}{l|l|l|l} \text { Number of Errors, } y & 8,6 & 6,10 & 8,14 \\ \hline \text { Number of Hours without Sleep, } x & 8 & 12 & 16 \end{array}\\\ &\begin{array}{l|l|l} \text { Number of Errors, } y & 14,12 & 16,12 \\ \hline \text { Number of Hours without Sleep, } x & 20 & 24 \end{array} \end{aligned} $$ $$ \begin{aligned} &\text { Analysis of Variance }\\\ &\begin{array}{lcrrrr} \text { Source } & \text { DF } & \text { Adj SS } & \text { Adj MS } & \text { F-Value } & \text { P-Value } \\ \hline \text { Regression } & 1 & 72.20 & 72.200 & 14.37 & 0.005 \\ \text { Error } & 8 & 40.20 & 5.025 & & \\ \text { Total } & 9 & 112.40 & & & \end{array} \end{aligned} $$ $$ \begin{aligned} &\text { Model Summary }\\\ &\begin{array}{rrr} \mathrm{S} & \text { R-sq } & \text { R-sq(adj) } \\ \hline 2.24165 & 64.23 \% & 59.76 \% \end{array} \end{aligned} $$ $$ \begin{aligned} &\text { Coefficients }\\\ &\begin{array}{lrrrr} \text { Term } & \text { Coef } & \text { SE Coef } & \text { T-Value } & \text { P-Value } \\ \hline \text { Constant } & 3.00 & 2.13 & 1.41 & 0.196 \\ \mathrm{x} & 0.475 & 0.125 & 3.79 & 0.005 \end{array} \end{aligned} $$ Regression Equation $$ y=3.00+0.475 x $$ a. Do the data present sufficient evidence to indicate that the number of errors is linearly related to the number of hours without sleep? Identify the two test statistics in the printout that can be used to answer this question. b. Would you expect the relationship between \(y\) and \(x\) to be linear if \(x\) varied over a wider range \((\) say \(, x=4\) to \(x=48\) )? c. How do you describe the strength of the relationship between \(y\) and \(x ?\) d. What is the best estimate of the common population variance \(\sigma^{2} ?\) e. Find a \(95 \%\) confidence interval for the slope of the line.
Exercises \(6-7\) were formed by reversing the slope of the lines in Exercises 4 - 5. Plot the points on graph paper and calculater and \(r^{2}\). Notice the change in the sign of \(r\) and the relationship between the values of \(r^{2}\) compared to Exercises \(4-5 .\) By what percentage was the sum of squares of deviations reduced by using the least-squares predictor \(\hat{y}=a+b x\) rather than \(\bar{y}\) as a predictor of \(y\) ? $$\begin{array}{l|rrrrr}x & -2 & -1 & 0 & 1 & 2 \\\\\hline y & 4 & 4 & 3 & 2 & 2\end{array}$$
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