Computer output for fitting a simple linear model is given below. State the value of the sample slope for this model and give the null and alternative hypotheses for testing if the slope in the population is different from zero. Identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. The regression equation is Y⁢=84.7-0.0115X. Predictor Coef SE Coef T P Constant 84.68 12.15 6.97 0.000 X -0.01148 0.00957 -1.20 0.245 Sample slope: Enter your answer; sample slope p-value: Enter your answer; p-value

Find the regression​ equation, letting overhead width be the predictor​ (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.5 cm. Can the prediction be​ correct? What is wrong with predicting the weight in this​ case? Use a significance level of 0.05.   Overhead Width​ (cm) 7.6 7.4 9.8 8.8 9.3 7.3   Weight​ (kg) 142 163 256 188 231 156       The regression equation is y= + x. ​(Round to one decimal place as​ needed.)   The best predicted weight for an overhead width of 2.5 cm is kg? ​(Round to one decimal place as​ needed.)   Can the prediction be​ correct? What is wrong with predicting the weight in this​ case?     A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data.   B. The prediction cannot be correct because a negative weight does not make sense and because there is…

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x Sodium, y (a) x= 150 calories (c) x= 120 calories (b) x= 90 calories (d) x=60 calories 160 180 130 130 80 190 415 465 350 380 270 540 (c) Predict the value of y for x= 120. Choose the correct answer below O A. 347 151 O B. 279.801 O C. 414.501 O D. not meaningful (d) Predict the value of y for x= 60. Choose the correct answer below. O A. 414.501 O B. 212.451 tes O C. 279.801

Which of the multivariate regression parameters listed below would be best interpreted as: the proportion of variation in the dependent variable explain by all the independent variables in the model.   a   b1   X1 R2

Computer output for fitting a simple linear model is given below. State the value of the sample slope for the given model. In testing if the slope in the population is different from zero, identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model.The regression equation is Y=86.1-8.35X. Predictor Coef SE Coef T P Constant 86.075 4.367 19.71 0.000 X -8.3536 0.9747 -8.57 0.000

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Construct and interpret a​ 95% prediction interval for the proceeds when the number of issues is 658.

Prev The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density 61 62 68 69 40 357 350 343 340 315 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Table Copy Data Next

Below table contains a data sample where X is the independent, and Y the dependent variable. Using the data, please conduct a regression analysis. Determine first the regression equation with the help of below graph and table, then answer all the questions. 1. The value of cell a is 2. The value of cell b is 3: The value of cell c is 4: The value of cell d is 5: The value of cell e is 6: The value of cell f is 7: The value of cell g is 8. The value of cell h is 9: The value of cell i is 10: The value of cell j is 11: The value of cell k is 12: The value of cell l is 13: The value of cell m is 14: The value of cell n is 15: The value of cell o is 16: The value of cell p is 17: The value of cell q is 18: The value of cell r is 19: The value of cell s is 20: The value of cell t is 21: The value of cell u is 22: The value of cell v is 23: The value of cell w is 24: The value of cell x is 25: The value of cell y is 26: The value of cell z is 27: The value of cell aa is 28: The value of cell…

randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1.5 2 3 3.5 4 4.5 5.5 Overall Grades 99 96 84 75 68 65 60 Table   Step 1 of 6 :  Find the estimated slope. Round your answer to three decimal places.