If I attend 3 more tutorials, what impact do I expect this to have on my final mark? The R2 value for the model fit was 0.11. Write a sentence about what this represents with respect to the data. You have been asked to analyse a suspected relationship between tutorial attendanceand final marks for a university course. The latest set of data includes final marks Y(between 0 and 100) and tutorial attendance X (between 0 and 12) for 65 students.You have done a regression analysis on the dataset in R and the output is given below,along with a scatter plot of X and Y. Answer the subsequent questions regarding thisanalysis.80Final Mark6040TToO0 000OOo o0 0 0@0 000 00Oο απο0000 00O000O®oooo24681012Tutorials AttendedFigure 3: Q3) Scatterplot of marks against tutorial attendance.Estimate Std. Error t value Pr(> |t|)(Intercept)44.15174.62079.555 7.17e-14X2.79140.56794.915 6.65e-06Table 2: Q10) Regression summary. 21Standardized residuals-1-2000039081Normal Q-Q-2000000000 0730.2-101Theoretical Quantilesaov(ANOV_X~ group)Figure 1: Q9) QQ-plot.Residuals42T@oooo @DO O O000 @xxxxxxxxxx@0000 ORDEIDO CO-2@13-4Residuals vs Fitted10.003911.010.511.5Fitted valuesaov(ANOV_X~ group)Figure 2: Q9) Residual plot.810

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If I attend 3 more tutorials, what impact do I expect this to have on my final mark? The R2 value for the model fit was 0.11. Write a sentence about what this represents with respect to the data.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.
One set of 20 pairs of scores, X and Y values, produces a correlation of r = 0.70. If SSY = 150, calculate the standard error of the estimate for the regression line
Suppose the entering freshmen at a certain college have a mean combined SAT score of 1231 with a standard deviation of 122 In the first semester, these students attained a mean GPA of 2.64, with a standard deviation of 0.53. A scatterplot showed the association to be reasonably linear, and the correlation between SAT score and GPA was 0.47 How do i find the regression line using the equation
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x�^=�0+�1�, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 1 2 3 3.5 4 4.5 5 Midterm Grades 60 66 73 76 78 84 90 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x�^=�0+�1�, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 11 1.51.5 2.52.5 33 44 4.54.5 55 Midterm Grades 6666 6969 7575 7979 9090 9595 9898 Find the estimated slope. Round your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.…
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x�^=�0+�1�, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 1 2 3 3.5 4 4.5 5 Midterm Grades 60 66 73 76 78 84 90 Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: Find the estimated value of y when x=2�=2. Round your answer to three decimal places. Step 4 of 6: Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated…
A researcher examined the relationship between the annual tax amount paid by the construction company owners and the total expense items spent for the construction with regression analysis and calculated the regression line estimation as Y=6+2X. According to this; Write the interpretations of the cutoff and regression parameters, respectively, in your OWN SENTENCES in a statistically appropriate way over the tax amount and expenses.
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, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.050.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations Average Temperature (℉℉) 3939 2525 1515 4242 4242 2424 3232 2020 3030 3737 Snow Accumulation (in.in.) 66 1515 2929 66 1414 2626 2323 1212 1616 77 Copy Data Regression equation: yˆ=�^= Is the equation appropriate? Yes
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Find the estimated y-intercept. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Find the estimated value of y when x=3x=3. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Find the estimated slope. Round your answer to three decimal places.