Use the set of points to compute the following. The slope of the regression line for this data is computed to be b, =-0.1078494, and the standard error of b, is computed as s, = 0.2135521. 12 14 4 16 21 15 12 7 y 17 11 20 18 19 19 18 19 Send data to Excel Part: 0 /3 Part 1 of 3 Find the critical value for a 99% confidence interval for B, and round the answer to three decimal places. The critical value for a 99% confidence interval is

Calculate the co-efficient of correlation and obtain the least square regression lines for the following data : 3 x: . 1 2 4 5 6 7 8 9 у: 8 10 12 11 13 14 16 15 Also obtain an estimate of y which should correspond on the average to x = 6.2.

For the following data: a. Find the regression equation for predicting Y from X. b. Calculate the Pearson correlation for these data. Use r2 and SS_Y to compute SSresidual and the standard error of estimate for the equation. Y 3 3 4 3 7 10 5 9.

The least-square regression line for the given data is y = 0.449x - 30.27. Determine the residual of a data point for which x = 98 and y = 15, rounding to three decimal places. Temperature, x Number of absences, y OA. 28.732 B. 1.268 C. 121.535 O D. 13.732 72 3 85 7 91 10 90 10 88 98 75 100 80 8 15 4 15 5

The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, ŷ = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperature (°F) Average Temperatures and Snow Accumulations 42 31 24 45 38 18 33 21 25 9 12 27 7 15 22 30 13 20 37 Snow Accumulation (in.) 8

The value obtained for the test statistic, z, in a one-mean z-test is given. Also given is whether the test is two tailed, left tailed, or right tailed. Also is given the P-value.A left-tailed test: z = -1.17 P-value: 0.1210 Use technology to create a scatter plot of the data from the previous question. Include the regression line. (Hand drawn graphs will not be accepted.) The explanatory (input) variable and the response (output) variable must be clearly labeled, within the context of this problem.

For a given set of x and y data values, assume that the regression model assumptions are valid and that a 90% confidence interval for ₁ is given by (-2.2, -0.1). Which of the following statements are true? i) At a = 0.10, there is a significant linear relationship between x and y. ii) In the scatterplot of x and y, the values of y tend to decrease as the values of x increase. iii) Based on this confidence interval, we would reject the null hypothesis of no linear relationship at any significance level a ≤ 0.1. Select one: a. i) O b. i) and ii) O c. ii) d. i), ii), and iii)

Ten observations were provided for a dependent variable y and two independent variables  x1 and x2;  for these data, SST = 15,177.6 and SSR = 14,059.3. (a) Compute  R2.  (Round your answer to three decimal places.) R2 =  (b) Compute  Ra2.  (Round your answer to three decimal places.) Ra2 =  (c) Does the estimated regression equation explain a large amount of the variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to one decimal place.)      , after adjusting for the number of independent variables in the model, we see that  % of the variability in y has been accounted for.